diff --git a/.github/plugins/azure-sdk-python/skills/agent-framework-azure-ai-py/SKILL.md b/.github/plugins/azure-sdk-python/skills/agent-framework-azure-ai-py/SKILL.md
index 9fceffba..5b931049 100644
--- a/.github/plugins/azure-sdk-python/skills/agent-framework-azure-ai-py/SKILL.md
+++ b/.github/plugins/azure-sdk-python/skills/agent-framework-azure-ai-py/SKILL.md
@@ -334,6 +334,11 @@ if __name__ == "__main__":
 - Use `get_new_thread()` for multi-turn conversations
 - Prefer `HostedMCPTool` for service-managed MCP, `MCPStreamableHTTPTool` for client-managed
 
+## Best Practices
+
+1. **This SDK is async-first** — use `async def` handlers and `async with` throughout.
+2. **Always use context managers for clients and async credentials.** Wrap every client in `with Client(...) as client:` (sync) or `async with Client(...) as client:` (async). For async `DefaultAzureCredential` from `azure.identity.aio`, also use `async with credential:` so tokens and transports are cleaned up.
+
 ## Reference Files
 
 - [references/tools.md](references/tools.md): Detailed hosted tool patterns
diff --git a/.github/plugins/azure-sdk-python/skills/agents-v2-py/SKILL.md b/.github/plugins/azure-sdk-python/skills/agents-v2-py/SKILL.md
index bc371241..900bb8ea 100644
--- a/.github/plugins/azure-sdk-python/skills/agents-v2-py/SKILL.md
+++ b/.github/plugins/azure-sdk-python/skills/agents-v2-py/SKILL.md
@@ -78,27 +78,26 @@ from azure.ai.projects.models import (
 ### 2. Create Hosted Agent
 
 ```python
-client = AIProjectClient(
+with AIProjectClient(
     endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
     credential=DefaultAzureCredential()
-)
-
-agent = client.agents.create_version(
-    agent_name="my-hosted-agent",
-    definition=ImageBasedHostedAgentDefinition(
-        container_protocol_versions=[
-            ProtocolVersionRecord(protocol=AgentProtocol.RESPONSES, version="v1")
-        ],
-        cpu="1",
-        memory="2Gi",
-        image="myregistry.azurecr.io/my-agent:latest",
-        tools=[{"type": "code_interpreter"}],
-        environment_variables={
-            "AZURE_AI_PROJECT_ENDPOINT": os.environ["AZURE_AI_PROJECT_ENDPOINT"],
-            "MODEL_NAME": "gpt-4o-mini"
-        }
+) as client:
+    agent = client.agents.create_version(
+        agent_name="my-hosted-agent",
+        definition=ImageBasedHostedAgentDefinition(
+            container_protocol_versions=[
+                ProtocolVersionRecord(protocol=AgentProtocol.RESPONSES, version="v1")
+            ],
+            cpu="1",
+            memory="2Gi",
+            image="myregistry.azurecr.io/my-agent:latest",
+            tools=[{"type": "code_interpreter"}],
+            environment_variables={
+                "AZURE_AI_PROJECT_ENDPOINT": os.environ["AZURE_AI_PROJECT_ENDPOINT"],
+                "MODEL_NAME": "gpt-4o-mini"
+            }
+        )
     )
-)
 
 print(f"Created agent: {agent.name} (version: {agent.version})")
 ```
@@ -235,34 +234,33 @@ from azure.ai.projects.models import (
 def create_hosted_agent():
     """Create a hosted agent with custom container image."""
     
-    client = AIProjectClient(
+    with AIProjectClient(
         endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
         credential=DefaultAzureCredential()
-    )
-    
-    agent = client.agents.create_version(
-        agent_name="data-processor-agent",
-        definition=ImageBasedHostedAgentDefinition(
-            container_protocol_versions=[
-                ProtocolVersionRecord(
-                    protocol=AgentProtocol.RESPONSES,
-                    version="v1"
-                )
-            ],
-            image="myregistry.azurecr.io/data-processor:v1.0",
-            cpu="2",
-            memory="4Gi",
-            tools=[
-                {"type": "code_interpreter"},
-                {"type": "file_search"}
-            ],
-            environment_variables={
-                "AZURE_AI_PROJECT_ENDPOINT": os.environ["AZURE_AI_PROJECT_ENDPOINT"],
-                "MODEL_NAME": "gpt-4o-mini",
-                "MAX_RETRIES": "3"
-            }
+    ) as client:
+        agent = client.agents.create_version(
+            agent_name="data-processor-agent",
+            definition=ImageBasedHostedAgentDefinition(
+                container_protocol_versions=[
+                    ProtocolVersionRecord(
+                        protocol=AgentProtocol.RESPONSES,
+                        version="v1"
+                    )
+                ],
+                image="myregistry.azurecr.io/data-processor:v1.0",
+                cpu="2",
+                memory="4Gi",
+                tools=[
+                    {"type": "code_interpreter"},
+                    {"type": "file_search"}
+                ],
+                environment_variables={
+                    "AZURE_AI_PROJECT_ENDPOINT": os.environ["AZURE_AI_PROJECT_ENDPOINT"],
+                    "MODEL_NAME": "gpt-4o-mini",
+                    "MAX_RETRIES": "3"
+                }
+            )
         )
-    )
     
     print(f"Created hosted agent: {agent.name}")
     print(f"Version: {agent.version}")
@@ -322,11 +320,13 @@ async def create_hosted_agent_async():
 
 ## Best Practices
 
-1. **Version Your Images** - Use specific tags, not `latest` in production
-2. **Minimal Resources** - Start with minimum CPU/memory, scale up as needed
-3. **Environment Variables** - Use for all configuration, never hardcode
-4. **Error Handling** - Wrap agent creation in try/except blocks
-5. **Cleanup** - Delete unused agent versions to free resources
+1. **Pick sync OR async and stay consistent.** Do not mix `azure.xxx` sync clients with `azure.xxx.aio` async clients in the same call path. Choose one mode per module.
+2. **Always use context managers for clients and async credentials.** Wrap every client in `with Client(...) as client:` (sync) or `async with Client(...) as client:` (async). For async `DefaultAzureCredential` from `azure.identity.aio`, also use `async with credential:` so tokens and transports are cleaned up.
+3. **Version Your Images** - Use specific tags, not `latest` in production
+4. **Minimal Resources** - Start with minimum CPU/memory, scale up as needed
+5. **Environment Variables** - Use for all configuration, never hardcode
+6. **Error Handling** - Wrap agent creation in try/except blocks
+7. **Cleanup** - Delete unused agent versions to free resources
 
 ## Reference Links
 
diff --git a/.github/plugins/azure-sdk-python/skills/azure-ai-contentsafety-py/SKILL.md b/.github/plugins/azure-sdk-python/skills/azure-ai-contentsafety-py/SKILL.md
index 12f5e4b9..5be93049 100644
--- a/.github/plugins/azure-sdk-python/skills/azure-ai-contentsafety-py/SKILL.md
+++ b/.github/plugins/azure-sdk-python/skills/azure-ai-contentsafety-py/SKILL.md
@@ -67,18 +67,17 @@ from azure.ai.contentsafety import ContentSafetyClient
 from azure.ai.contentsafety.models import AnalyzeTextOptions, TextCategory
 from azure.core.credentials import AzureKeyCredential
 
-client = ContentSafetyClient(endpoint, AzureKeyCredential(key))
-
-request = AnalyzeTextOptions(text="Your text content to analyze")
-response = client.analyze_text(request)
-
-# Check each category
-for category in [TextCategory.HATE, TextCategory.SELF_HARM, 
-                 TextCategory.SEXUAL, TextCategory.VIOLENCE]:
-    result = next((r for r in response.categories_analysis 
-                   if r.category == category), None)
-    if result:
-        print(f"{category}: severity {result.severity}")
+with ContentSafetyClient(endpoint, AzureKeyCredential(key)) as client:
+    request = AnalyzeTextOptions(text="Your text content to analyze")
+    response = client.analyze_text(request)
+
+    # Check each category
+    for category in [TextCategory.HATE, TextCategory.SELF_HARM, 
+                     TextCategory.SEXUAL, TextCategory.VIOLENCE]:
+        result = next((r for r in response.categories_analysis 
+                       if r.category == category), None)
+        if result:
+            print(f"{category}: severity {result.severity}")
 ```
 
 ## Analyze Image
@@ -89,20 +88,19 @@ from azure.ai.contentsafety.models import AnalyzeImageOptions, ImageData
 from azure.core.credentials import AzureKeyCredential
 import base64
 
-client = ContentSafetyClient(endpoint, AzureKeyCredential(key))
+with ContentSafetyClient(endpoint, AzureKeyCredential(key)) as client:
+    # From file
+    with open("image.jpg", "rb") as f:
+        image_data = base64.b64encode(f.read()).decode("utf-8")
 
-# From file
-with open("image.jpg", "rb") as f:
-    image_data = base64.b64encode(f.read()).decode("utf-8")
-
-request = AnalyzeImageOptions(
-    image=ImageData(content=image_data)
-)
+    request = AnalyzeImageOptions(
+        image=ImageData(content=image_data)
+    )
 
-response = client.analyze_image(request)
+    response = client.analyze_image(request)
 
-for result in response.categories_analysis:
-    print(f"{result.category}: severity {result.severity}")
+    for result in response.categories_analysis:
+        print(f"{result.category}: severity {result.severity}")
 ```
 
 ### Image from URL
@@ -126,17 +124,16 @@ from azure.ai.contentsafety import BlocklistClient
 from azure.ai.contentsafety.models import TextBlocklist
 from azure.core.credentials import AzureKeyCredential
 
-blocklist_client = BlocklistClient(endpoint, AzureKeyCredential(key))
-
-blocklist = TextBlocklist(
-    blocklist_name="my-blocklist",
-    description="Custom terms to block"
-)
+with BlocklistClient(endpoint, AzureKeyCredential(key)) as blocklist_client:
+    blocklist = TextBlocklist(
+        blocklist_name="my-blocklist",
+        description="Custom terms to block"
+    )
 
-result = blocklist_client.create_or_update_text_blocklist(
-    blocklist_name="my-blocklist",
-    options=blocklist
-)
+    result = blocklist_client.create_or_update_text_blocklist(
+        blocklist_name="my-blocklist",
+        options=blocklist
+    )
 ```
 
 ### Add Block Items
@@ -215,10 +212,12 @@ request = AnalyzeTextOptions(
 
 ## Best Practices
 
-1. **Use blocklists** for domain-specific terms
-2. **Set severity thresholds** appropriate for your use case
-3. **Handle multiple categories** — content can be harmful in multiple ways
-4. **Use halt_on_blocklist_hit** for immediate rejection
-5. **Log analysis results** for audit and improvement
-6. **Consider 8-severity mode** for finer-grained control
-7. **Pre-moderate AI outputs** before showing to users
+1. **Pick sync OR async and stay consistent.** Do not mix `azure.ai.contentsafety` sync clients with `azure.ai.contentsafety.aio` async clients in the same call path. Choose one mode per module.
+2. **Always use context managers for clients and async credentials.** Wrap every client in `with ContentSafetyClient(...) as client:` (sync) or `async with ContentSafetyClient(...) as client:` (async). For async `DefaultAzureCredential` from `azure.identity.aio`, also use `async with credential:` so tokens and transports are cleaned up.
+3. **Use blocklists** for domain-specific terms
+4. **Set severity thresholds** appropriate for your use case
+5. **Handle multiple categories** — content can be harmful in multiple ways
+6. **Use halt_on_blocklist_hit** for immediate rejection
+7. **Log analysis results** for audit and improvement
+8. **Consider 8-severity mode** for finer-grained control
+9. **Pre-moderate AI outputs** before showing to users
diff --git a/.github/plugins/azure-sdk-python/skills/azure-ai-contentunderstanding-py/SKILL.md b/.github/plugins/azure-sdk-python/skills/azure-ai-contentunderstanding-py/SKILL.md
index 3d5abfec..54c9cec5 100644
--- a/.github/plugins/azure-sdk-python/skills/azure-ai-contentunderstanding-py/SKILL.md
+++ b/.github/plugins/azure-sdk-python/skills/azure-ai-contentunderstanding-py/SKILL.md
@@ -70,22 +70,21 @@ from azure.ai.contentunderstanding.models import AnalyzeInput
 from azure.identity import DefaultAzureCredential
 
 endpoint = os.environ["CONTENTUNDERSTANDING_ENDPOINT"]
-client = ContentUnderstandingClient(
+with ContentUnderstandingClient(
     endpoint=endpoint,
     credential=DefaultAzureCredential()
-)
-
-# Analyze document from URL
-poller = client.begin_analyze(
-    analyzer_id="prebuilt-documentSearch",
-    inputs=[AnalyzeInput(url="https://example.com/document.pdf")]
-)
-
-result = poller.result()
-
-# Access markdown content (contents is a list)
-content = result.contents[0]
-print(content.markdown)
+) as client:
+    # Analyze document from URL
+    poller = client.begin_analyze(
+        analyzer_id="prebuilt-documentSearch",
+        inputs=[AnalyzeInput(url="https://example.com/document.pdf")]
+    )
+
+    result = poller.result()
+
+    # Access markdown content (contents is a list)
+    content = result.contents[0]
+    print(content.markdown)
 ```
 
 ## Access Document Content Details
@@ -226,19 +225,18 @@ from azure.identity.aio import DefaultAzureCredential
 
 async def analyze_document():
     endpoint = os.environ["CONTENTUNDERSTANDING_ENDPOINT"]
-    credential = DefaultAzureCredential()
-    
-    async with ContentUnderstandingClient(
-        endpoint=endpoint,
-        credential=credential
-    ) as client:
-        poller = await client.begin_analyze(
-            analyzer_id="prebuilt-documentSearch",
-            inputs=[AnalyzeInput(url="https://example.com/doc.pdf")]
-        )
-        result = await poller.result()
-        content = result.contents[0]
-        return content.markdown
+    async with DefaultAzureCredential() as credential:
+        async with ContentUnderstandingClient(
+            endpoint=endpoint,
+            credential=credential
+        ) as client:
+            poller = await client.begin_analyze(
+                analyzer_id="prebuilt-documentSearch",
+                inputs=[AnalyzeInput(url="https://example.com/doc.pdf")]
+            )
+            result = await poller.result()
+            content = result.contents[0]
+            return content.markdown
 
 asyncio.run(analyze_document())
 ```
@@ -273,10 +271,12 @@ from azure.ai.contentunderstanding.models import (
 
 ## Best Practices
 
-1. **Use `begin_analyze` with `AnalyzeInput`** — this is the correct method signature
-2. **Access results via `result.contents[0]`** — results are returned as a list
-3. **Use prebuilt analyzers** for common scenarios (document/image/audio/video search)
-4. **Create custom analyzers** only for domain-specific field extraction
-5. **Use async client** for high-throughput scenarios with `azure.identity.aio` credentials
-6. **Handle long-running operations** — video/audio analysis can take minutes
-7. **Use URL sources** when possible to avoid upload overhead
+1. **Pick sync OR async and stay consistent.** Do not mix `azure.ai.contentunderstanding` sync clients with `azure.ai.contentunderstanding.aio` async clients in the same call path. Choose one mode per module.
+2. **Always use context managers for clients and async credentials.** Wrap every client in `with ContentUnderstandingClient(...) as client:` (sync) or `async with ContentUnderstandingClient(...) as client:` (async). For async `DefaultAzureCredential` from `azure.identity.aio`, also use `async with credential:` so tokens and transports are cleaned up.
+3. **Use `begin_analyze` with `AnalyzeInput`** — this is the correct method signature
+4. **Access results via `result.contents[0]`** — results are returned as a list
+5. **Use prebuilt analyzers** for common scenarios (document/image/audio/video search)
+6. **Create custom analyzers** only for domain-specific field extraction
+7. **Use async client** for high-throughput scenarios with `azure.identity.aio` credentials
+8. **Handle long-running operations** — video/audio analysis can take minutes
+9. **Use URL sources** when possible to avoid upload overhead
diff --git a/.github/plugins/azure-sdk-python/skills/azure-ai-language-conversations-py/SKILL.md b/.github/plugins/azure-sdk-python/skills/azure-ai-language-conversations-py/SKILL.md
index 0edfbfb8..7a373846 100644
--- a/.github/plugins/azure-sdk-python/skills/azure-ai-language-conversations-py/SKILL.md
+++ b/.github/plugins/azure-sdk-python/skills/azure-ai-language-conversations-py/SKILL.md
@@ -20,8 +20,9 @@ When responding to requests about Azure AI Language Conversations:
 4. Handle exceptions properly.
 
 ## Best Practices
+- **Pick sync OR async and stay consistent.** Do not mix `azure.ai.language.conversations` sync clients with `azure.ai.language.conversations.aio` async clients in the same call path. Choose one mode per module.
+- **Always use context managers for clients and async credentials.** Wrap every client in `with ConversationAnalysisClient(...) as client:` (sync) or `async with ConversationAnalysisClient(...) as client:` (async). For async `DefaultAzureCredential` from `azure.identity.aio`, also use `async with credential:` so tokens and transports are cleaned up.
 - Use environment variables for the endpoint, API key, project name, and deployment name.
-- Always use context managers (`with client:`) to ensure proper resource handling.
 - Clearly map the `participantId` and `id` in the `conversationItem` payload.
 
 ## Examples
diff --git a/.github/plugins/azure-sdk-python/skills/azure-ai-ml-py/SKILL.md b/.github/plugins/azure-sdk-python/skills/azure-ai-ml-py/SKILL.md
index d1e90a58..ff3de5a9 100644
--- a/.github/plugins/azure-sdk-python/skills/azure-ai-ml-py/SKILL.md
+++ b/.github/plugins/azure-sdk-python/skills/azure-ai-ml-py/SKILL.md
@@ -273,10 +273,12 @@ print(f"Default: {default_ds.name}")
 
 ## Best Practices
 
-1. **Use versioning** for data, models, and environments
-2. **Configure idle scale-down** to reduce compute costs
-3. **Use environments** for reproducible training
-4. **Stream job logs** to monitor progress
-5. **Register models** after successful training jobs
-6. **Use pipelines** for multi-step workflows
-7. **Tag resources** for organization and cost tracking
+1. **Pick sync OR async and stay consistent.** Do not mix `azure.ai.ml` sync clients with `azure.ai.ml` async clients in the same call path. Choose one mode per module.
+2. **Always use context managers for clients and async credentials.** Wrap every client in `with MLClient(...) as client:` (sync) or `async with MLClient(...) as client:` (async). For async `DefaultAzureCredential` from `azure.identity.aio`, also use `async with credential:` so tokens and transports are cleaned up.
+3. **Use versioning** for data, models, and environments
+4. **Configure idle scale-down** to reduce compute costs
+5. **Use environments** for reproducible training
+6. **Stream job logs** to monitor progress
+7. **Register models** after successful training jobs
+8. **Use pipelines** for multi-step workflows
+9. **Tag resources** for organization and cost tracking
diff --git a/.github/plugins/azure-sdk-python/skills/azure-ai-projects-py/SKILL.md b/.github/plugins/azure-sdk-python/skills/azure-ai-projects-py/SKILL.md
index 7d4f8d35..bacf2c7d 100644
--- a/.github/plugins/azure-sdk-python/skills/azure-ai-projects-py/SKILL.md
+++ b/.github/plugins/azure-sdk-python/skills/azure-ai-projects-py/SKILL.md
@@ -63,17 +63,16 @@ client = AIProjectClient(
 ```python
 from azure.ai.projects import AIProjectClient
 
-client = AIProjectClient(
+with AIProjectClient(
     endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
     credential=DefaultAzureCredential(),
-)
-
-# Use Foundry-native operations
-agent = client.agents.create_agent(
-    model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
-    name="my-agent",
-    instructions="You are helpful.",
-)
+) as client:
+    # Use Foundry-native operations
+    agent = client.agents.create_agent(
+        model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+        name="my-agent",
+        instructions="You are helpful.",
+    )
 ```
 
 ### 2. OpenAI-Compatible Client
@@ -270,11 +269,12 @@ agent = client.agents.create_agent(
 
 ## Best Practices
 
-1. **Use context managers** for async client: `async with AIProjectClient(...) as client:`
-2. **Clean up agents** when done: `client.agents.delete_agent(agent.id)`
-3. **Use `create_and_process`** for simple runs, **streaming** for real-time UX
-4. **Use versioned agents** for production deployments
-5. **Prefer connections** for external service integration (AI Search, Bing, etc.)
+1. **Pick sync OR async and stay consistent.** Do not mix `azure.ai.projects` sync clients with `azure.ai.projects.aio` async clients in the same call path. Choose one mode per module.
+2. **Always use context managers for clients and async credentials.** Wrap every client in `with AIProjectClient(...) as client:` (sync) or `async with AIProjectClient(...) as client:` (async). For async `DefaultAzureCredential` from `azure.identity.aio`, also use `async with credential:` so tokens and transports are cleaned up.
+3. **Clean up agents** when done: `client.agents.delete_agent(agent.id)`
+4. **Use `create_and_process`** for simple runs, **streaming** for real-time UX
+5. **Use versioned agents** for production deployments
+6. **Prefer connections** for external service integration (AI Search, Bing, etc.)
 
 ## SDK Comparison
 
diff --git a/.github/plugins/azure-sdk-python/skills/azure-ai-textanalytics-py/SKILL.md b/.github/plugins/azure-sdk-python/skills/azure-ai-textanalytics-py/SKILL.md
index bbcabebb..b01f3cbd 100644
--- a/.github/plugins/azure-sdk-python/skills/azure-ai-textanalytics-py/SKILL.md
+++ b/.github/plugins/azure-sdk-python/skills/azure-ai-textanalytics-py/SKILL.md
@@ -199,12 +199,13 @@ from azure.ai.textanalytics.aio import TextAnalyticsClient
 from azure.identity.aio import DefaultAzureCredential
 
 async def analyze():
-    async with TextAnalyticsClient(
-        endpoint=endpoint,
-        credential=DefaultAzureCredential()
-    ) as client:
-        result = await client.analyze_sentiment(documents)
-        # Process results...
+    async with DefaultAzureCredential() as credential:
+        async with TextAnalyticsClient(
+            endpoint=endpoint,
+            credential=credential
+        ) as client:
+            result = await client.analyze_sentiment(documents)
+            # Process results...
 ```
 
 ## Client Types
@@ -229,9 +230,10 @@ async def analyze():
 
 ## Best Practices
 
-1. **Use batch operations** for multiple documents (up to 10 per request)
-2. **Enable opinion mining** for detailed aspect-based sentiment
-3. **Use async client** for high-throughput scenarios
-4. **Handle document errors** — results list may contain errors for some docs
-5. **Specify language** when known to improve accuracy
-6. **Use context manager** or close client explicitly
+1. **Pick sync OR async and stay consistent.** Do not mix `azure.ai.textanalytics` sync clients with `azure.ai.textanalytics.aio` async clients in the same call path. Choose one mode per module.
+2. **Always use context managers for clients and async credentials.** Wrap every client in `with TextAnalyticsClient(...) as client:` (sync) or `async with TextAnalyticsClient(...) as client:` (async). For async `DefaultAzureCredential` from `azure.identity.aio`, also use `async with credential:` so tokens and transports are cleaned up.
+3. **Use batch operations** for multiple documents (up to 10 per request)
+4. **Enable opinion mining** for detailed aspect-based sentiment
+5. **Use async client** for high-throughput scenarios
+6. **Handle document errors** — results list may contain errors for some docs
+7. **Specify language** when known to improve accuracy
diff --git a/.github/plugins/azure-sdk-python/skills/azure-ai-transcription-py/SKILL.md b/.github/plugins/azure-sdk-python/skills/azure-ai-transcription-py/SKILL.md
index 5fd05e61..a5155fe5 100644
--- a/.github/plugins/azure-sdk-python/skills/azure-ai-transcription-py/SKILL.md
+++ b/.github/plugins/azure-sdk-python/skills/azure-ai-transcription-py/SKILL.md
@@ -65,9 +65,11 @@ for event in stream:
 
 ## Best Practices
 
-1. **Enable diarization** when multiple speakers are present
-2. **Use batch transcription** for long files stored in blob storage
-3. **Capture timestamps** for subtitle generation
-4. **Specify language** to improve recognition accuracy
-5. **Handle streaming backpressure** for real-time transcription
-6. **Close transcription sessions** when complete
+1. **Pick sync OR async and stay consistent.** Do not mix `azure.xxx` sync clients with `azure.xxx.aio` async clients in the same call path. Choose one mode per module.
+2. **Always use context managers for clients and async credentials.** Wrap every client in `with Client(...) as client:` (sync) or `async with Client(...) as client:` (async). For async `DefaultAzureCredential` from `azure.identity.aio`, also use `async with credential:` so tokens and transports are cleaned up.
+3. **Enable diarization** when multiple speakers are present
+4. **Use batch transcription** for long files stored in blob storage
+5. **Capture timestamps** for subtitle generation
+6. **Specify language** to improve recognition accuracy
+7. **Handle streaming backpressure** for real-time transcription
+8. **Close transcription sessions** when complete
diff --git a/.github/plugins/azure-sdk-python/skills/azure-ai-translation-document-py/SKILL.md b/.github/plugins/azure-sdk-python/skills/azure-ai-translation-document-py/SKILL.md
index 711de49c..f3af1cca 100644
--- a/.github/plugins/azure-sdk-python/skills/azure-ai-translation-document-py/SKILL.md
+++ b/.github/plugins/azure-sdk-python/skills/azure-ai-translation-document-py/SKILL.md
@@ -116,16 +116,15 @@ poller = client.begin_translation(
 ```python
 from azure.ai.translation.document import SingleDocumentTranslationClient
 
-single_client = SingleDocumentTranslationClient(endpoint, AzureKeyCredential(key))
-
 with open("document.docx", "rb") as f:
     document_content = f.read()
 
-result = single_client.translate(
-    body=document_content,
-    target_language="es",
-    content_type="application/vnd.openxmlformats-officedocument.wordprocessingml.document"
-)
+with SingleDocumentTranslationClient(endpoint, AzureKeyCredential(key)) as single_client:
+    result = single_client.translate(
+        body=document_content,
+        target_language="es",
+        content_type="application/vnd.openxmlformats-officedocument.wordprocessingml.document"
+    )
 
 # Save translated document
 with open("document_es.docx", "wb") as f:
@@ -249,10 +248,12 @@ async def translate_documents():
 
 ## Best Practices
 
-1. **Use SAS tokens** with minimal required permissions
-2. **Monitor long-running operations** with `poller.status()`
-3. **Handle document-level errors** by iterating document statuses
-4. **Use glossaries** for domain-specific terminology
-5. **Separate target containers** for each language
-6. **Use async client** for multiple concurrent jobs
-7. **Check supported formats** before submitting documents
+1. **Pick sync OR async and stay consistent.** Do not mix `azure.xxx` sync clients with `azure.xxx.aio` async clients in the same call path. Choose one mode per module.
+2. **Always use context managers for clients and async credentials.** Wrap every client in `with Client(...) as client:` (sync) or `async with Client(...) as client:` (async). For async `DefaultAzureCredential` from `azure.identity.aio`, also use `async with credential:` so tokens and transports are cleaned up.
+3. **Use SAS tokens** with minimal required permissions
+4. **Monitor long-running operations** with `poller.status()`
+5. **Handle document-level errors** by iterating document statuses
+6. **Use glossaries** for domain-specific terminology
+7. **Separate target containers** for each language
+8. **Use async client** for multiple concurrent jobs
+9. **Check supported formats** before submitting documents
diff --git a/.github/plugins/azure-sdk-python/skills/azure-ai-translation-text-py/SKILL.md b/.github/plugins/azure-sdk-python/skills/azure-ai-translation-text-py/SKILL.md
index 509856bd..7ae1c5f5 100644
--- a/.github/plugins/azure-sdk-python/skills/azure-ai-translation-text-py/SKILL.md
+++ b/.github/plugins/azure-sdk-python/skills/azure-ai-translation-text-py/SKILL.md
@@ -277,10 +277,12 @@ async def translate_text():
 
 ## Best Practices
 
-1. **Batch translations** — Send multiple texts in one request (up to 100)
-2. **Specify source language** when known to improve accuracy
-3. **Use async client** for high-throughput scenarios
-4. **Cache language list** — Supported languages don't change frequently
-5. **Handle profanity** appropriately for your application
-6. **Use html text_type** when translating HTML content
-7. **Include alignment** for applications needing word mapping
+1. **Pick sync OR async and stay consistent.** Do not mix `azure.xxx` sync clients with `azure.xxx.aio` async clients in the same call path. Choose one mode per module.
+2. **Always use context managers for clients and async credentials.** Wrap every client in `with Client(...) as client:` (sync) or `async with Client(...) as client:` (async). For async `DefaultAzureCredential` from `azure.identity.aio`, also use `async with credential:` so tokens and transports are cleaned up.
+3. **Batch translations** — Send multiple texts in one request (up to 100)
+4. **Specify source language** when known to improve accuracy
+5. **Use async client** for high-throughput scenarios
+6. **Cache language list** — Supported languages don't change frequently
+7. **Handle profanity** appropriately for your application
+8. **Use html text_type** when translating HTML content
+9. **Include alignment** for applications needing word mapping
diff --git a/.github/plugins/azure-sdk-python/skills/azure-ai-vision-imageanalysis-py/SKILL.md b/.github/plugins/azure-sdk-python/skills/azure-ai-vision-imageanalysis-py/SKILL.md
index 49ea8065..1d6c314c 100644
--- a/.github/plugins/azure-sdk-python/skills/azure-ai-vision-imageanalysis-py/SKILL.md
+++ b/.github/plugins/azure-sdk-python/skills/azure-ai-vision-imageanalysis-py/SKILL.md
@@ -216,15 +216,16 @@ from azure.ai.vision.imageanalysis.aio import ImageAnalysisClient
 from azure.identity.aio import DefaultAzureCredential
 
 async def analyze_image():
-    async with ImageAnalysisClient(
-        endpoint=endpoint,
-        credential=DefaultAzureCredential()
-    ) as client:
-        result = await client.analyze_from_url(
-            image_url=image_url,
-            visual_features=[VisualFeatures.CAPTION]
-        )
-        print(result.caption.text)
+    async with DefaultAzureCredential() as credential:
+        async with ImageAnalysisClient(
+            endpoint=endpoint,
+            credential=credential
+        ) as client:
+            result = await client.analyze_from_url(
+                image_url=image_url,
+                visual_features=[VisualFeatures.CAPTION]
+            )
+            print(result.caption.text)
 ```
 
 ## Visual Features
@@ -263,10 +264,12 @@ except HttpResponseError as e:
 
 ## Best Practices
 
-1. **Select only needed features** to optimize latency and cost
-2. **Use async client** for high-throughput scenarios
-3. **Handle HttpResponseError** for invalid images or auth issues
-4. **Enable gender_neutral_caption** for inclusive descriptions
-5. **Specify language** for localized captions
-6. **Use smart_crops_aspect_ratios** matching your thumbnail requirements
-7. **Cache results** when analyzing the same image multiple times
+1. **Pick sync OR async and stay consistent.** Do not mix `azure.ai.vision.imageanalysis` sync clients with `azure.ai.vision.imageanalysis.aio` async clients in the same call path. Choose one mode per module.
+2. **Always use context managers for clients and async credentials.** Wrap every client in `with ImageAnalysisClient(...) as client:` (sync) or `async with ImageAnalysisClient(...) as client:` (async). For async `DefaultAzureCredential` from `azure.identity.aio`, also use `async with credential:` so tokens and transports are cleaned up.
+3. **Select only needed features** to optimize latency and cost
+4. **Use async client** for high-throughput scenarios
+5. **Handle HttpResponseError** for invalid images or auth issues
+6. **Enable gender_neutral_caption** for inclusive descriptions
+7. **Specify language** for localized captions
+8. **Use smart_crops_aspect_ratios** matching your thumbnail requirements
+9. **Cache results** when analyzing the same image multiple times
diff --git a/.github/plugins/azure-sdk-python/skills/azure-ai-voicelive-py/SKILL.md b/.github/plugins/azure-sdk-python/skills/azure-ai-voicelive-py/SKILL.md
index 1a5d337f..39a0bed4 100644
--- a/.github/plugins/azure-sdk-python/skills/azure-ai-voicelive-py/SKILL.md
+++ b/.github/plugins/azure-sdk-python/skills/azure-ai-voicelive-py/SKILL.md
@@ -314,6 +314,11 @@ except ConnectionError as e:
     print(f"Connection error: {e}")
 ```
 
+## Best Practices
+
+1. **This SDK is async-only; use `azure.ai.voicelive.aio` throughout.** Do not try to pair it with sync clients from other Azure SDKs in the same call path — keep the whole request path async.
+2. **Always use context managers for clients and async credentials.** Wrap every connection in `async with connect(...) as conn:`. For async `DefaultAzureCredential` from `azure.identity.aio`, also use `async with credential:` so tokens and transports are cleaned up.
+
 ## References
 
 - **Detailed API Reference**: See [references/api-reference.md](references/api-reference.md)
diff --git a/.github/plugins/azure-sdk-python/skills/azure-appconfiguration-py/SKILL.md b/.github/plugins/azure-sdk-python/skills/azure-appconfiguration-py/SKILL.md
index c4fe1278..b8d050cc 100644
--- a/.github/plugins/azure-sdk-python/skills/azure-appconfiguration-py/SKILL.md
+++ b/.github/plugins/azure-sdk-python/skills/azure-appconfiguration-py/SKILL.md
@@ -225,17 +225,13 @@ from azure.appconfiguration.aio import AzureAppConfigurationClient
 from azure.identity.aio import DefaultAzureCredential
 
 async def main():
-    credential = DefaultAzureCredential()
-    client = AzureAppConfigurationClient(
-        base_url=endpoint,
-        credential=credential
-    )
-    
-    setting = await client.get_configuration_setting(key="app:message")
-    print(setting.value)
-    
-    await client.close()
-    await credential.close()
+    async with DefaultAzureCredential() as credential:
+        async with AzureAppConfigurationClient(
+            base_url=endpoint,
+            credential=credential
+        ) as client:
+            setting = await client.get_configuration_setting(key="app:message")
+            print(setting.value)
 ```
 
 ## Client Operations
@@ -252,10 +248,12 @@ async def main():
 
 ## Best Practices
 
-1. **Use labels** for environment separation (dev, staging, prod)
-2. **Use key prefixes** for logical grouping (app:database:*, app:cache:*)
-3. **Make production settings read-only** to prevent accidental changes
-4. **Create snapshots** before deployments for rollback capability
-5. **Use Entra ID** instead of connection strings in production
-6. **Refresh settings periodically** in long-running applications
-7. **Use feature flags** for gradual rollouts and A/B testing
+1. **Pick sync OR async and stay consistent.** Do not mix `azure.xxx` sync clients with `azure.xxx.aio` async clients in the same call path. Choose one mode per module.
+2. **Always use context managers for clients and async credentials.** Wrap every client in `with Client(...) as client:` (sync) or `async with Client(...) as client:` (async). For async `DefaultAzureCredential` from `azure.identity.aio`, also use `async with credential:` so tokens and transports are cleaned up.
+3. **Use labels** for environment separation (dev, staging, prod)
+4. **Use key prefixes** for logical grouping (app:database:*, app:cache:*)
+5. **Make production settings read-only** to prevent accidental changes
+6. **Create snapshots** before deployments for rollback capability
+7. **Use Entra ID** instead of connection strings in production
+8. **Refresh settings periodically** in long-running applications
+9. **Use feature flags** for gradual rollouts and A/B testing
diff --git a/.github/plugins/azure-sdk-python/skills/azure-containerregistry-py/SKILL.md b/.github/plugins/azure-sdk-python/skills/azure-containerregistry-py/SKILL.md
index 94f949ef..4df98cd4 100644
--- a/.github/plugins/azure-sdk-python/skills/azure-containerregistry-py/SKILL.md
+++ b/.github/plugins/azure-sdk-python/skills/azure-containerregistry-py/SKILL.md
@@ -63,10 +63,9 @@ client = ContainerRegistryClient(
 ## List Repositories
 
 ```python
-client = ContainerRegistryClient(endpoint, DefaultAzureCredential())
-
-for repository in client.list_repository_names():
-    print(repository)
+with ContainerRegistryClient(endpoint, DefaultAzureCredential()) as client:
+    for repository in client.list_repository_names():
+        print(repository)
 ```
 
 ## Repository Operations
@@ -193,18 +192,17 @@ client.delete_tag("my-image", "old-tag")
 ```python
 from azure.containerregistry import ContainerRegistryClient
 
-client = ContainerRegistryClient(endpoint, DefaultAzureCredential())
-
-# Download manifest
-manifest = client.download_manifest("my-image", "latest")
-print(f"Media type: {manifest.media_type}")
-print(f"Digest: {manifest.digest}")
+with ContainerRegistryClient(endpoint, DefaultAzureCredential()) as client:
+    # Download manifest
+    manifest = client.download_manifest("my-image", "latest")
+    print(f"Media type: {manifest.media_type}")
+    print(f"Digest: {manifest.digest}")
 
-# Download blob
-blob = client.download_blob("my-image", "sha256:abc123...")
-with open("layer.tar.gz", "wb") as f:
-    for chunk in blob:
-        f.write(chunk)
+    # Download blob
+    blob = client.download_blob("my-image", "sha256:abc123...")
+    with open("layer.tar.gz", "wb") as f:
+        for chunk in blob:
+            f.write(chunk)
 ```
 
 ## Async Client
@@ -214,14 +212,10 @@ from azure.containerregistry.aio import ContainerRegistryClient
 from azure.identity.aio import DefaultAzureCredential
 
 async def list_repos():
-    credential = DefaultAzureCredential()
-    client = ContainerRegistryClient(endpoint, credential)
-    
-    async for repo in client.list_repository_names():
-        print(repo)
-    
-    await client.close()
-    await credential.close()
+    async with DefaultAzureCredential() as credential:
+        async with ContainerRegistryClient(endpoint, credential) as client:
+            async for repo in client.list_repository_names():
+                print(repo)
 ```
 
 ## Clean Up Old Images
@@ -255,10 +249,12 @@ for manifest in client.list_manifest_properties("my-image"):
 
 ## Best Practices
 
-1. **Use Entra ID** for authentication in production
-2. **Delete by digest** not tag to avoid orphaned images
-3. **Lock production images** with can_delete=False
-4. **Clean up untagged manifests** regularly
-5. **Use async client** for high-throughput operations
-6. **Order by last_updated** to find recent/old images
-7. **Check manifest.tags** before deleting to avoid removing tagged images
+1. **Pick sync OR async and stay consistent.** Do not mix `azure.xxx` sync clients with `azure.xxx.aio` async clients in the same call path. Choose one mode per module.
+2. **Always use context managers for clients and async credentials.** Wrap every client in `with Client(...) as client:` (sync) or `async with Client(...) as client:` (async). For async `DefaultAzureCredential` from `azure.identity.aio`, also use `async with credential:` so tokens and transports are cleaned up.
+3. **Use Microsoft Entra ID** for authentication in production
+4. **Delete by digest** not tag to avoid orphaned images
+5. **Lock production images** with can_delete=False
+6. **Clean up untagged manifests** regularly
+7. **Use async client** for high-throughput operations
+8. **Order by last_updated** to find recent/old images
+9. **Check manifest.tags** before deleting to avoid removing tagged images
diff --git a/.github/plugins/azure-sdk-python/skills/azure-cosmos-db-py/SKILL.md b/.github/plugins/azure-sdk-python/skills/azure-cosmos-db-py/SKILL.md
index d381917f..50c2823c 100644
--- a/.github/plugins/azure-sdk-python/skills/azure-cosmos-db-py/SKILL.md
+++ b/.github/plugins/azure-sdk-python/skills/azure-cosmos-db-py/SKILL.md
@@ -205,6 +205,11 @@ async def test_get_project_by_id_returns_project(mock_cosmos_container):
 
 **Full testing guide**: See [references/testing.md](references/testing.md)
 
+## Best Practices
+
+1. **This skill uses async throughout (`azure.cosmos.aio`); do not mix with the sync `azure.cosmos` client.** Keep the whole FastAPI request path async — don't pair sync Cosmos calls with async handlers.
+2. **Always use context managers for clients and async credentials.** Wrap the client in `async with CosmosClient(...) as client:` (or manage its lifetime via FastAPI lifespan and close it explicitly). For async `DefaultAzureCredential` from `azure.identity.aio`, also use `async with credential:` so tokens and transports are cleaned up.
+
 ## Reference Files
 
 | File | When to Read |
diff --git a/.github/plugins/azure-sdk-python/skills/azure-cosmos-py/SKILL.md b/.github/plugins/azure-sdk-python/skills/azure-cosmos-py/SKILL.md
index 392d0f3f..324848d0 100644
--- a/.github/plugins/azure-sdk-python/skills/azure-cosmos-py/SKILL.md
+++ b/.github/plugins/azure-sdk-python/skills/azure-cosmos-py/SKILL.md
@@ -233,24 +233,23 @@ from azure.cosmos.aio import CosmosClient
 from azure.identity.aio import DefaultAzureCredential
 
 async def cosmos_operations():
-    credential = DefaultAzureCredential()
-    
-    async with CosmosClient(endpoint, credential=credential) as client:
-        database = client.get_database_client("mydb")
-        container = database.get_container_client("mycontainer")
-        
-        # Create
-        await container.create_item(body={"id": "1", "pk": "test"})
-        
-        # Read
-        item = await container.read_item(item="1", partition_key="test")
-        
-        # Query
-        async for item in container.query_items(
-            query="SELECT * FROM c",
-            partition_key="test"
-        ):
-            print(item)
+    async with DefaultAzureCredential() as credential:
+        async with CosmosClient(endpoint, credential=credential) as client:
+            database = client.get_database_client("mydb")
+            container = database.get_container_client("mycontainer")
+            
+            # Create
+            await container.create_item(body={"id": "1", "pk": "test"})
+            
+            # Read
+            item = await container.read_item(item="1", partition_key="test")
+            
+            # Query
+            async for item in container.query_items(
+                query="SELECT * FROM c",
+                partition_key="test"
+            ):
+                print(item)
 
 import asyncio
 asyncio.run(cosmos_operations())
@@ -274,13 +273,15 @@ except CosmosHttpResponseError as e:
 
 ## Best Practices
 
-1. **Always specify partition key** for point reads and queries
-2. **Use parameterized queries** to prevent injection and improve caching
-3. **Avoid cross-partition queries** when possible
-4. **Use `upsert_item`** for idempotent writes
-5. **Use async client** for high-throughput scenarios
-6. **Design partition key** for even data distribution
-7. **Use `read_item`** instead of query for single document retrieval
+1. **Pick sync OR async and stay consistent.** Do not mix `azure.cosmos` sync clients with `azure.cosmos.aio` async clients in the same call path. Choose one mode per module.
+2. **Always use context managers for clients and async credentials.** Wrap every client in `with CosmosClient(...) as client:` (sync) or `async with CosmosClient(...) as client:` (async). For async `DefaultAzureCredential` from `azure.identity.aio`, also use `async with credential:` so tokens and transports are cleaned up.
+3. **Always specify partition key** for point reads and queries
+4. **Use parameterized queries** to prevent injection and improve caching
+5. **Avoid cross-partition queries** when possible
+6. **Use `upsert_item`** for idempotent writes
+7. **Use async client** for high-throughput scenarios
+8. **Design partition key** for even data distribution
+9. **Use `read_item`** instead of query for single document retrieval
 
 ## Reference Files
 
diff --git a/.github/plugins/azure-sdk-python/skills/azure-data-tables-py/SKILL.md b/.github/plugins/azure-sdk-python/skills/azure-data-tables-py/SKILL.md
index b35be83e..a67a0a0a 100644
--- a/.github/plugins/azure-sdk-python/skills/azure-data-tables-py/SKILL.md
+++ b/.github/plugins/azure-sdk-python/skills/azure-data-tables-py/SKILL.md
@@ -209,23 +209,22 @@ from azure.data.tables.aio import TableServiceClient, TableClient
 from azure.identity.aio import DefaultAzureCredential
 
 async def table_operations():
-    credential = DefaultAzureCredential()
-    
-    async with TableClient(
-        endpoint="https://<account>.table.core.windows.net",
-        table_name="mytable",
-        credential=credential
-    ) as client:
-        # Create
-        await client.create_entity(entity={
-            "PartitionKey": "async",
-            "RowKey": "1",
-            "data": "test"
-        })
-        
-        # Query
-        async for entity in client.query_entities("PartitionKey eq 'async'"):
-            print(entity)
+    async with DefaultAzureCredential() as credential:
+        async with TableClient(
+            endpoint="https://<account>.table.core.windows.net",
+            table_name="mytable",
+            credential=credential
+        ) as client:
+            # Create
+            await client.create_entity(entity={
+                "PartitionKey": "async",
+                "RowKey": "1",
+                "data": "test"
+            })
+            
+            # Query
+            async for entity in client.query_entities("PartitionKey eq 'async'"):
+                print(entity)
 
 import asyncio
 asyncio.run(table_operations())
@@ -245,10 +244,12 @@ asyncio.run(table_operations())
 
 ## Best Practices
 
-1. **Design partition keys** for query patterns and even distribution
-2. **Query within partitions** whenever possible (cross-partition is expensive)
-3. **Use batch operations** for multiple entities in same partition
-4. **Use `upsert_entity`** for idempotent writes
-5. **Use parameterized queries** to prevent injection
-6. **Keep entities small** — max 1MB per entity
-7. **Use async client** for high-throughput scenarios
+1. **Pick sync OR async and stay consistent.** Do not mix `azure.data.tables` sync clients with `azure.data.tables.aio` async clients in the same call path. Choose one mode per module.
+2. **Always use context managers for clients and async credentials.** Wrap every client in `with TableClient(...) as client:` (sync) or `async with TableClient(...) as client:` (async). For async `DefaultAzureCredential` from `azure.identity.aio`, also use `async with credential:` so tokens and transports are cleaned up.
+3. **Design partition keys** for query patterns and even distribution
+4. **Query within partitions** whenever possible (cross-partition is expensive)
+5. **Use batch operations** for multiple entities in same partition
+6. **Use `upsert_entity`** for idempotent writes
+7. **Use parameterized queries** to prevent injection
+8. **Keep entities small** — max 1MB per entity
+9. **Use async client** for high-throughput scenarios
diff --git a/.github/plugins/azure-sdk-python/skills/azure-eventgrid-py/SKILL.md b/.github/plugins/azure-sdk-python/skills/azure-eventgrid-py/SKILL.md
index f20ee22b..eb870904 100644
--- a/.github/plugins/azure-sdk-python/skills/azure-eventgrid-py/SKILL.md
+++ b/.github/plugins/azure-sdk-python/skills/azure-eventgrid-py/SKILL.md
@@ -59,26 +59,25 @@ client = EventGridPublisherClient(endpoint, credential)
 from azure.eventgrid import EventGridPublisherClient, CloudEvent
 from azure.identity import DefaultAzureCredential
 
-client = EventGridPublisherClient(endpoint, DefaultAzureCredential())
-
-# Single event
-event = CloudEvent(
-    type="MyApp.Events.OrderCreated",
-    source="/myapp/orders",
-    data={"order_id": "12345", "amount": 99.99}
-)
-client.send(event)
-
-# Multiple events
-events = [
-    CloudEvent(
+with EventGridPublisherClient(endpoint, DefaultAzureCredential()) as client:
+    # Single event
+    event = CloudEvent(
         type="MyApp.Events.OrderCreated",
         source="/myapp/orders",
-        data={"order_id": f"order-{i}"}
+        data={"order_id": "12345", "amount": 99.99}
     )
-    for i in range(10)
-]
-client.send(events)
+    client.send(event)
+
+    # Multiple events
+    events = [
+        CloudEvent(
+            type="MyApp.Events.OrderCreated",
+            source="/myapp/orders",
+            data={"order_id": f"order-{i}"}
+        )
+        for i in range(10)
+    ]
+    client.send(events)
 ```
 
 ## Publish EventGridEvents
@@ -153,17 +152,18 @@ asyncio.run(publish_events())
 For Event Grid Namespaces (pull delivery):
 
 ```python
-from azure.eventgrid.aio import EventGridPublisherClient
+from azure.eventgrid import EventGridPublisherClient
+from azure.identity import DefaultAzureCredential
 
 # Namespace endpoint (different from custom topic)
 namespace_endpoint = "https://<namespace>.<region>.eventgrid.azure.net"
 topic_name = "my-topic"
 
-async with EventGridPublisherClient(
+with EventGridPublisherClient(
     endpoint=namespace_endpoint,
     credential=DefaultAzureCredential()
 ) as client:
-    await client.send(
+    client.send(
         event,
         namespace_topic=topic_name
     )
@@ -171,9 +171,11 @@ async with EventGridPublisherClient(
 
 ## Best Practices
 
-1. **Use CloudEvents** for new applications (industry standard)
-2. **Batch events** when publishing multiple events
-3. **Include meaningful subjects** for filtering
-4. **Use async client** for high-throughput scenarios
-5. **Handle retries** — Event Grid has built-in retry
-6. **Set appropriate event types** for routing and filtering
+1. **Pick sync OR async and stay consistent.** Do not mix `azure.xxx` sync clients with `azure.xxx.aio` async clients in the same call path. Choose one mode per module.
+2. **Always use context managers for clients and async credentials.** Wrap every client in `with Client(...) as client:` (sync) or `async with Client(...) as client:` (async). For async `DefaultAzureCredential` from `azure.identity.aio`, also use `async with credential:` so tokens and transports are cleaned up.
+3. **Use CloudEvents** for new applications (industry standard)
+4. **Batch events** when publishing multiple events
+5. **Include meaningful subjects** for filtering
+6. **Use async client** for high-throughput scenarios
+7. **Handle retries** — Event Grid has built-in retry
+8. **Set appropriate event types** for routing and filtering
diff --git a/.github/plugins/azure-sdk-python/skills/azure-eventhub-py/SKILL.md b/.github/plugins/azure-sdk-python/skills/azure-eventhub-py/SKILL.md
index b784a8dd..6f7f5de4 100644
--- a/.github/plugins/azure-sdk-python/skills/azure-eventhub-py/SKILL.md
+++ b/.github/plugins/azure-sdk-python/skills/azure-eventhub-py/SKILL.md
@@ -232,13 +232,14 @@ with producer:
 
 ## Best Practices
 
-1. **Use batches** for sending multiple events
-2. **Use checkpoint store** in production for reliable processing
-3. **Use async client** for high-throughput scenarios
-4. **Use partition keys** for ordered delivery within a partition
-5. **Handle batch size limits** — catch ValueError when batch is full
-6. **Use context managers** (`with`/`async with`) for proper cleanup
-7. **Set appropriate consumer groups** for different applications
+1. **Pick sync OR async and stay consistent.** Do not mix `azure.xxx` sync clients with `azure.xxx.aio` async clients in the same call path. Choose one mode per module.
+2. **Always use context managers for clients and async credentials.** Wrap every client in `with Client(...) as client:` (sync) or `async with Client(...) as client:` (async) for proper cleanup. For async `DefaultAzureCredential` from `azure.identity.aio`, also use `async with credential:` so tokens and transports are cleaned up.
+3. **Use batches** for sending multiple events
+4. **Use checkpoint store** in production for reliable processing
+5. **Use async client** for high-throughput scenarios
+6. **Use partition keys** for ordered delivery within a partition
+7. **Handle batch size limits** — catch ValueError when batch is full
+8. **Set appropriate consumer groups** for different applications
 
 ## Reference Files
 
diff --git a/.github/plugins/azure-sdk-python/skills/azure-identity-py/SKILL.md b/.github/plugins/azure-sdk-python/skills/azure-identity-py/SKILL.md
index 5f0fb160..6d1328d0 100644
--- a/.github/plugins/azure-sdk-python/skills/azure-identity-py/SKILL.md
+++ b/.github/plugins/azure-sdk-python/skills/azure-identity-py/SKILL.md
@@ -362,14 +362,13 @@ credential = AzurePipelinesCredential(
 ```python
 from azure.identity import DefaultAzureCredential
 
-credential = DefaultAzureCredential()
-
-# Get token for a specific scope
-token = credential.get_token("https://management.azure.com/.default")
-print(f"Token expires: {token.expires_on}")
+with DefaultAzureCredential() as credential:
+    # Get token for a specific scope
+    token = credential.get_token("https://management.azure.com/.default")
+    print(f"Token expires: {token.expires_on}")
 
-# For Azure Database for PostgreSQL
-token = credential.get_token("https://ossrdbms-aad.database.windows.net/.default")
+    # For Azure Database for PostgreSQL
+    token = credential.get_token("https://ossrdbms-aad.database.windows.net/.default")
 ```
 
 ## Async Credentials
@@ -453,16 +452,16 @@ credential = ClientSecretCredential(
 from azure.identity import DefaultAzureCredential, CredentialUnavailableError
 from azure.core.exceptions import ClientAuthenticationError
 
-credential = DefaultAzureCredential()
-try:
-    token = credential.get_token("https://management.azure.com/.default")
-except CredentialUnavailableError:
-    # No credential in the chain could attempt authentication
-    pass
-except ClientAuthenticationError as e:
-    # Authentication was attempted but failed
-    # e.message contains details from each credential in the chain
-    pass
+with DefaultAzureCredential() as credential:
+    try:
+        token = credential.get_token("https://management.azure.com/.default")
+    except CredentialUnavailableError:
+        # No credential in the chain could attempt authentication
+        pass
+    except ClientAuthenticationError as e:
+        # Authentication was attempted but failed
+        # e.message contains details from each credential in the chain
+        pass
 ```
 
 ## Logging
@@ -499,17 +498,18 @@ AZURE_LOG_LEVEL=debug
 
 ## Best Practices
 
-1. **Use `DefaultAzureCredential`** for code that runs locally and in Azure
-2. **Never hardcode credentials** — use environment variables or managed identity
-3. **Prefer managed identity** in production Azure deployments
-4. **Use `get_bearer_token_provider`** for non-Azure-SDK clients (OpenAI, REST APIs)
-5. **Use `ChainedTokenCredential`** when you need a custom credential order
-6. **Close async credentials** — use `async with credential:` context manager
-7. **Set `AZURE_CLIENT_ID`** for user-assigned managed identities (object ID and resource ID are also valid identifiers)
-8. **Exclude unused credentials** to speed up `DefaultAzureCredential` authentication
-9. **Use `CertificateCredential`** (not `ClientCertificateCredential` — that name doesn't exist)
-10. **Enable `cache_persistence_options`** for long-running services to reduce token requests
-11. **Reuse credential instances** — same credential can be shared across multiple clients
+1. **Pick sync OR async and stay consistent.** Do not mix `azure.xxx` sync clients with `azure.xxx.aio` async clients in the same call path. Choose one mode per module.
+2. **Use credentials as context managers** (`with DefaultAzureCredential() as credential:`) when they own token caches / HTTP transports you want cleaned up; for async, use `async with` on credentials from `azure.identity.aio`.
+3. **Use `DefaultAzureCredential`** for code that runs locally. Use a specific token credential for code that runs in Azure.
+4. **Never hardcode credentials** — use environment variables or managed identity
+5. **Prefer managed identity** in production Azure deployments
+6. **Use `get_bearer_token_provider`** for non-Azure-SDK clients (OpenAI, REST APIs)
+7. **Use `ChainedTokenCredential`** when you need a custom credential order
+8. **Set `AZURE_CLIENT_ID`** for user-assigned managed identities (object ID and resource ID are also valid identifiers)
+9. **Exclude unused credentials** to speed up `DefaultAzureCredential` authentication
+10. **Use `CertificateCredential`** (not `ClientCertificateCredential` — that name doesn't exist)
+11. **Enable `cache_persistence_options`** for long-running services to reduce token requests
+12. **Reuse credential instances** — same credential can be shared across multiple clients
 
 ## Reference Links
 
diff --git a/.github/plugins/azure-sdk-python/skills/azure-keyvault-py/SKILL.md b/.github/plugins/azure-sdk-python/skills/azure-keyvault-py/SKILL.md
index 19c31e58..83840332 100644
--- a/.github/plugins/azure-sdk-python/skills/azure-keyvault-py/SKILL.md
+++ b/.github/plugins/azure-sdk-python/skills/azure-keyvault-py/SKILL.md
@@ -132,33 +132,32 @@ deleted_key = poller.result()
 from azure.keyvault.keys.crypto import CryptographyClient, EncryptionAlgorithm
 
 # Get crypto client for a specific key
-crypto_client = CryptographyClient(key, credential=credential)
+# crypto_client = CryptographyClient(key, credential=credential)
 # Or from key ID
-crypto_client = CryptographyClient(
+with CryptographyClient(
     "https://<vault>.vault.azure.net/keys/<key-name>/<version>",
     credential=credential
-)
-
-# Encrypt
-plaintext = b"Hello, Key Vault!"
-result = crypto_client.encrypt(EncryptionAlgorithm.rsa_oaep, plaintext)
-ciphertext = result.ciphertext
-
-# Decrypt
-result = crypto_client.decrypt(EncryptionAlgorithm.rsa_oaep, ciphertext)
-decrypted = result.plaintext
-
-# Sign
-from azure.keyvault.keys.crypto import SignatureAlgorithm
-import hashlib
-
-digest = hashlib.sha256(b"data to sign").digest()
-result = crypto_client.sign(SignatureAlgorithm.rs256, digest)
-signature = result.signature
-
-# Verify
-result = crypto_client.verify(SignatureAlgorithm.rs256, digest, signature)
-print(f"Valid: {result.is_valid}")
+) as crypto_client:
+    # Encrypt
+    plaintext = b"Hello, Key Vault!"
+    result = crypto_client.encrypt(EncryptionAlgorithm.rsa_oaep, plaintext)
+    ciphertext = result.ciphertext
+
+    # Decrypt
+    result = crypto_client.decrypt(EncryptionAlgorithm.rsa_oaep, ciphertext)
+    decrypted = result.plaintext
+
+    # Sign
+    from azure.keyvault.keys.crypto import SignatureAlgorithm
+    import hashlib
+
+    digest = hashlib.sha256(b"data to sign").digest()
+    result = crypto_client.sign(SignatureAlgorithm.rs256, digest)
+    signature = result.signature
+
+    # Verify
+    result = crypto_client.verify(SignatureAlgorithm.rs256, digest, signature)
+    print(f"Valid: {result.is_valid}")
 ```
 
 ## Certificates
@@ -189,9 +188,9 @@ print(f"Thumbprint: {certificate.properties.x509_thumbprint.hex()}")
 
 # Get certificate with private key (as secret)
 from azure.keyvault.secrets import SecretClient
-secret_client = SecretClient(vault_url=vault_url, credential=credential)
-cert_secret = secret_client.get_secret("my-cert")
-# cert_secret.value contains PEM or PKCS12
+with SecretClient(vault_url=vault_url, credential=credential) as secret_client:
+    cert_secret = secret_client.get_secret("my-cert")
+    # cert_secret.value contains PEM or PKCS12
 
 # List certificates
 for cert in client.list_properties_of_certificates():
@@ -218,12 +217,10 @@ from azure.identity.aio import DefaultAzureCredential
 from azure.keyvault.secrets.aio import SecretClient
 
 async def get_secret():
-    credential = DefaultAzureCredential()
-    client = SecretClient(vault_url=vault_url, credential=credential)
-    
-    async with client:
-        secret = await client.get_secret("my-secret")
-        print(secret.value)
+    async with DefaultAzureCredential() as credential:
+        async with SecretClient(vault_url=vault_url, credential=credential) as client:
+            secret = await client.get_secret("my-secret")
+            print(secret.value)
 
 import asyncio
 asyncio.run(get_secret())
@@ -246,11 +243,13 @@ except HttpResponseError as e:
 
 ## Best Practices
 
-1. **Use DefaultAzureCredential** for authentication
-2. **Use managed identity** in Azure-hosted applications
-3. **Enable soft-delete** for recovery (enabled by default)
-4. **Use RBAC** over access policies for fine-grained control
-5. **Rotate secrets** regularly using versioning
-6. **Use Key Vault references** in App Service/Functions config
-7. **Cache secrets** appropriately to reduce API calls
-8. **Use async clients** for high-throughput scenarios
+1. **Pick sync OR async and stay consistent.** Do not mix `azure.xxx` sync clients with `azure.xxx.aio` async clients in the same call path. Choose one mode per module.
+2. **Always use context managers for clients and async credentials.** Wrap every client in `with Client(...) as client:` (sync) or `async with Client(...) as client:` (async). For async `DefaultAzureCredential` from `azure.identity.aio`, also use `async with credential:` so tokens and transports are cleaned up.
+3. **Use `DefaultAzureCredential`** for code that runs locally. Use a specific token credential for code that runs in Azure.
+4. **Use managed identity** in Azure-hosted applications
+5. **Enable soft-delete** for recovery (enabled by default)
+6. **Use RBAC** over access policies for fine-grained control
+7. **Rotate secrets** regularly using versioning
+8. **Use Key Vault references** in App Service/Functions config
+9. **Cache secrets** appropriately to reduce API calls
+10. **Use async clients** for high-throughput scenarios
diff --git a/.github/plugins/azure-sdk-python/skills/azure-messaging-webpubsubservice-py/SKILL.md b/.github/plugins/azure-sdk-python/skills/azure-messaging-webpubsubservice-py/SKILL.md
index 1b90f274..834cc2da 100644
--- a/.github/plugins/azure-sdk-python/skills/azure-messaging-webpubsubservice-py/SKILL.md
+++ b/.github/plugins/azure-sdk-python/skills/azure-messaging-webpubsubservice-py/SKILL.md
@@ -218,17 +218,13 @@ from azure.messaging.webpubsubservice.aio import WebPubSubServiceClient
 from azure.identity.aio import DefaultAzureCredential
 
 async def broadcast():
-    credential = DefaultAzureCredential()
-    client = WebPubSubServiceClient(
-        endpoint="https://<name>.webpubsub.azure.com",
-        hub="my-hub",
-        credential=credential
-    )
-    
-    await client.send_to_all("Hello async!", content_type="text/plain")
-    
-    await client.close()
-    await credential.close()
+    async with DefaultAzureCredential() as credential:
+        async with WebPubSubServiceClient(
+            endpoint="https://<name>.webpubsub.azure.com",
+            hub="my-hub",
+            credential=credential
+        ) as client:
+            await client.send_to_all("Hello async!", content_type="text/plain")
 ```
 
 ## Client Operations
@@ -247,10 +243,12 @@ async def broadcast():
 
 ## Best Practices
 
-1. **Use roles** to limit client permissions
-2. **Use groups** for targeted messaging
-3. **Generate short-lived tokens** for security
-4. **Use user IDs** to send to users across connections
-5. **Handle reconnection** in client applications
-6. **Use JSON** content type for structured data
-7. **Close connections** gracefully with reasons
+1. **Pick sync OR async and stay consistent.** Do not mix `azure.xxx` sync clients with `azure.xxx.aio` async clients in the same call path. Choose one mode per module.
+2. **Always use context managers for clients and async credentials.** Wrap every client in `with Client(...) as client:` (sync) or `async with Client(...) as client:` (async). For async `DefaultAzureCredential` from `azure.identity.aio`, also use `async with credential:` so tokens and transports are cleaned up.
+3. **Use roles** to limit client permissions
+4. **Use groups** for targeted messaging
+5. **Generate short-lived tokens** for security
+6. **Use user IDs** to send to users across connections
+7. **Handle reconnection** in client applications
+8. **Use JSON** content type for structured data
+9. **Close connections** gracefully with reasons
diff --git a/.github/plugins/azure-sdk-python/skills/azure-mgmt-apicenter-py/SKILL.md b/.github/plugins/azure-sdk-python/skills/azure-mgmt-apicenter-py/SKILL.md
index ec284223..fb8237ef 100644
--- a/.github/plugins/azure-sdk-python/skills/azure-mgmt-apicenter-py/SKILL.md
+++ b/.github/plugins/azure-sdk-python/skills/azure-mgmt-apicenter-py/SKILL.md
@@ -245,9 +245,11 @@ metadata = client.metadata_schemas.create_or_update(
 
 ## Best Practices
 
-1. **Use workspaces** to organize APIs by team or domain
-2. **Define metadata schemas** for consistent governance
-3. **Track deployments** to understand where APIs are running
-4. **Import specifications** to enable API analysis and linting
-5. **Use lifecycle stages** to track API maturity
-6. **Add contacts** for API ownership and support
+1. **Pick sync OR async and stay consistent.** Do not mix `azure.xxx` sync clients with `azure.xxx.aio` async clients in the same call path. Choose one mode per module.
+2. **Always use context managers for clients and async credentials.** Wrap every client in `with Client(...) as client:` (sync) or `async with Client(...) as client:` (async). For async `DefaultAzureCredential` from `azure.identity.aio`, also use `async with credential:` so tokens and transports are cleaned up.
+3. **Use workspaces** to organize APIs by team or domain
+4. **Define metadata schemas** for consistent governance
+5. **Track deployments** to understand where APIs are running
+6. **Import specifications** to enable API analysis and linting
+7. **Use lifecycle stages** to track API maturity
+8. **Add contacts** for API ownership and support
diff --git a/.github/plugins/azure-sdk-python/skills/azure-mgmt-apimanagement-py/SKILL.md b/.github/plugins/azure-sdk-python/skills/azure-mgmt-apimanagement-py/SKILL.md
index 1b3e8177..b58134db 100644
--- a/.github/plugins/azure-sdk-python/skills/azure-mgmt-apimanagement-py/SKILL.md
+++ b/.github/plugins/azure-sdk-python/skills/azure-mgmt-apimanagement-py/SKILL.md
@@ -281,9 +281,11 @@ user = client.user.create_or_update(
 
 ## Best Practices
 
-1. **Use named values** for secrets and configuration
-2. **Apply policies** at appropriate scopes (global, product, API, operation)
-3. **Use products** to bundle APIs and manage access
-4. **Enable Application Insights** for monitoring
-5. **Use backends** to abstract backend services
-6. **Version your APIs** using APIM's versioning features
+1. **Pick sync OR async and stay consistent.** Do not mix `azure.xxx` sync clients with `azure.xxx.aio` async clients in the same call path. Choose one mode per module.
+2. **Always use context managers for clients and async credentials.** Wrap every client in `with Client(...) as client:` (sync) or `async with Client(...) as client:` (async). For async `DefaultAzureCredential` from `azure.identity.aio`, also use `async with credential:` so tokens and transports are cleaned up.
+3. **Use named values** for secrets and configuration
+4. **Apply policies** at appropriate scopes (global, product, API, operation)
+5. **Use products** to bundle APIs and manage access
+6. **Enable Application Insights** for monitoring
+7. **Use backends** to abstract backend services
+8. **Version your APIs** using APIM's versioning features
diff --git a/.github/plugins/azure-sdk-python/skills/azure-mgmt-botservice-py/SKILL.md b/.github/plugins/azure-sdk-python/skills/azure-mgmt-botservice-py/SKILL.md
index 47f2a021..99c3cb89 100644
--- a/.github/plugins/azure-sdk-python/skills/azure-mgmt-botservice-py/SKILL.md
+++ b/.github/plugins/azure-sdk-python/skills/azure-mgmt-botservice-py/SKILL.md
@@ -55,31 +55,30 @@ from azure.mgmt.botservice.models import Bot, BotProperties, Sku
 from azure.identity import DefaultAzureCredential
 import os
 
-credential = DefaultAzureCredential()
-client = AzureBotService(
-    credential=credential,
-    subscription_id=os.environ["AZURE_SUBSCRIPTION_ID"]
-)
-
 resource_group = os.environ["AZURE_RESOURCE_GROUP"]
 bot_name = "my-chat-bot"
 
-bot = client.bots.create(
-    resource_group_name=resource_group,
-    resource_name=bot_name,
-    parameters=Bot(
-        location="global",
-        sku=Sku(name="F0"),  # Free tier
-        kind="azurebot",
-        properties=BotProperties(
-            display_name="My Chat Bot",
-            description="A conversational AI bot",
-            endpoint="https://my-bot-app.azurewebsites.net/api/messages",
-            msa_app_id="<your-app-id>",
-            msa_app_type="MultiTenant"
+credential = DefaultAzureCredential()
+with AzureBotService(
+    credential=credential,
+    subscription_id=os.environ["AZURE_SUBSCRIPTION_ID"]
+) as client:
+    bot = client.bots.create(
+        resource_group_name=resource_group,
+        resource_name=bot_name,
+        parameters=Bot(
+            location="global",
+            sku=Sku(name="F0"),  # Free tier
+            kind="azurebot",
+            properties=BotProperties(
+                display_name="My Chat Bot",
+                description="A conversational AI bot",
+                endpoint="https://my-bot-app.azurewebsites.net/api/messages",
+                msa_app_id="<your-app-id>",
+                msa_app_type="MultiTenant"
+            )
         )
     )
-)
 
 print(f"Bot created: {bot.name}")
 ```
@@ -321,10 +320,12 @@ for conn in connections:
 
 ## Best Practices
 
-1. **Use DefaultAzureCredential** for authentication
-2. **Start with F0 SKU** for development, upgrade to S1 for production
-3. **Store MSA App ID/Secret securely** — use Key Vault
-4. **Enable only needed channels** — reduces attack surface
-5. **Rotate Direct Line keys** periodically
-6. **Use managed identity** when possible for bot connections
-7. **Configure proper CORS** for Web Chat channel
+1. **Pick sync OR async and stay consistent.** Do not mix `azure.xxx` sync clients with `azure.xxx.aio` async clients in the same call path. Choose one mode per module.
+2. **Always use context managers for clients and async credentials.** Wrap every client in `with Client(...) as client:` (sync) or `async with Client(...) as client:` (async). For async `DefaultAzureCredential` from `azure.identity.aio`, also use `async with credential:` so tokens and transports are cleaned up.
+3. **Use `DefaultAzureCredential`** for code that runs locally. Use a specific token credential for code that runs in Azure.
+4. **Start with F0 SKU** for development, upgrade to S1 for production
+5. **Store MSA App ID/Secret securely** — use Key Vault
+6. **Enable only needed channels** — reduces attack surface
+7. **Rotate Direct Line keys** periodically
+8. **Use managed identity** when possible for bot connections
+9. **Configure proper CORS** for Web Chat channel
diff --git a/.github/plugins/azure-sdk-python/skills/azure-mgmt-fabric-py/SKILL.md b/.github/plugins/azure-sdk-python/skills/azure-mgmt-fabric-py/SKILL.md
index 097823aa..b0e13515 100644
--- a/.github/plugins/azure-sdk-python/skills/azure-mgmt-fabric-py/SKILL.md
+++ b/.github/plugins/azure-sdk-python/skills/azure-mgmt-fabric-py/SKILL.md
@@ -55,31 +55,30 @@ from azure.mgmt.fabric.models import FabricCapacity, FabricCapacityProperties, C
 from azure.identity import DefaultAzureCredential
 import os
 
-credential = DefaultAzureCredential()
-client = FabricMgmtClient(
-    credential=credential,
-    subscription_id=os.environ["AZURE_SUBSCRIPTION_ID"]
-)
-
 resource_group = os.environ["AZURE_RESOURCE_GROUP"]
 capacity_name = "myfabriccapacity"
 
-capacity = client.fabric_capacities.begin_create_or_update(
-    resource_group_name=resource_group,
-    capacity_name=capacity_name,
-    resource=FabricCapacity(
-        location="eastus",
-        sku=CapacitySku(
-            name="F2",  # Fabric SKU
-            tier="Fabric"
-        ),
-        properties=FabricCapacityProperties(
-            administration=FabricCapacityAdministration(
-                members=["user@contoso.com"]
+credential = DefaultAzureCredential()
+with FabricMgmtClient(
+    credential=credential,
+    subscription_id=os.environ["AZURE_SUBSCRIPTION_ID"]
+) as client:
+    capacity = client.fabric_capacities.begin_create_or_update(
+        resource_group_name=resource_group,
+        capacity_name=capacity_name,
+        resource=FabricCapacity(
+            location="eastus",
+            sku=CapacitySku(
+                name="F2",  # Fabric SKU
+                tier="Fabric"
+            ),
+            properties=FabricCapacityProperties(
+                administration=FabricCapacityAdministration(
+                    members=["user@contoso.com"]
+                )
             )
         )
-    )
-).result()
+    ).result()
 
 print(f"Capacity created: {capacity.name}")
 ```
@@ -258,11 +257,13 @@ capacity = poller.result()
 
 ## Best Practices
 
-1. **Use DefaultAzureCredential** for authentication
-2. **Suspend unused capacities** to reduce costs
-3. **Start with smaller SKUs** and scale up as needed
-4. **Use tags** for cost tracking and organization
-5. **Check name availability** before creating capacities
-6. **Handle LRO properly** — don't assume immediate completion
-7. **Set up capacity admins** — specify users who can manage workspaces
-8. **Monitor capacity usage** via Azure Monitor metrics
+1. **Pick sync OR async and stay consistent.** Do not mix `azure.xxx` sync clients with `azure.xxx.aio` async clients in the same call path. Choose one mode per module.
+2. **Always use context managers for clients and async credentials.** Wrap every client in `with Client(...) as client:` (sync) or `async with Client(...) as client:` (async). For async `DefaultAzureCredential` from `azure.identity.aio`, also use `async with credential:` so tokens and transports are cleaned up.
+3. **Use `DefaultAzureCredential`** for code that runs locally. Use a specific token credential for code that runs in Azure.
+4. **Suspend unused capacities** to reduce costs
+5. **Start with smaller SKUs** and scale up as needed
+6. **Use tags** for cost tracking and organization
+7. **Check name availability** before creating capacities
+8. **Handle LRO properly** — don't assume immediate completion
+9. **Set up capacity admins** — specify users who can manage workspaces
+10. **Monitor capacity usage** via Azure Monitor metrics
diff --git a/.github/plugins/azure-sdk-python/skills/azure-monitor-ingestion-py/SKILL.md b/.github/plugins/azure-sdk-python/skills/azure-monitor-ingestion-py/SKILL.md
index 09a4c81f..513a0254 100644
--- a/.github/plugins/azure-sdk-python/skills/azure-monitor-ingestion-py/SKILL.md
+++ b/.github/plugins/azure-sdk-python/skills/azure-monitor-ingestion-py/SKILL.md
@@ -70,11 +70,6 @@ from azure.monitor.ingestion import LogsIngestionClient
 from azure.identity import DefaultAzureCredential
 import os
 
-client = LogsIngestionClient(
-    endpoint=os.environ["AZURE_DCE_ENDPOINT"],
-    credential=DefaultAzureCredential()
-)
-
 rule_id = os.environ["AZURE_DCR_RULE_ID"]
 stream_name = os.environ["AZURE_DCR_STREAM_NAME"]
 
@@ -84,7 +79,11 @@ logs = [
     {"TimeGenerated": "2024-01-15T10:02:00Z", "Computer": "server2", "Message": "Connection established"}
 ]
 
-client.upload(rule_id=rule_id, stream_name=stream_name, logs=logs)
+with LogsIngestionClient(
+    endpoint=os.environ["AZURE_DCE_ENDPOINT"],
+    credential=DefaultAzureCredential()
+) as client:
+    client.upload(rule_id=rule_id, stream_name=stream_name, logs=logs)
 ```
 
 ## Upload from JSON File
@@ -205,11 +204,12 @@ Stream names follow patterns:
 
 ## Best Practices
 
-1. **Use DefaultAzureCredential** for authentication
-2. **Handle errors gracefully** — use `on_error` callback for partial failures
-3. **Include TimeGenerated** — Required field for all logs
-4. **Match DCR schema** — Log fields must match DCR column definitions
-5. **Use async client** for high-throughput scenarios
-6. **Batch uploads** — SDK handles batching, but send reasonable chunks
-7. **Monitor ingestion** — Check Log Analytics for ingestion status
-8. **Use context manager** — Ensures proper client cleanup
+1. **Pick sync OR async and stay consistent.** Do not mix `azure.xxx` sync clients with `azure.xxx.aio` async clients in the same call path. Choose one mode per module.
+2. **Always use context managers for clients and async credentials.** Wrap every client in `with Client(...) as client:` (sync) or `async with Client(...) as client:` (async) to ensure proper cleanup. For async `DefaultAzureCredential` from `azure.identity.aio`, also use `async with credential:` so tokens and transports are cleaned up.
+3. **Use `DefaultAzureCredential`** for code that runs locally. Use a specific token credential for code that runs in Azure.
+4. **Handle errors gracefully** — use `on_error` callback for partial failures
+5. **Include TimeGenerated** — Required field for all logs
+6. **Match DCR schema** — Log fields must match DCR column definitions
+7. **Use async client** for high-throughput scenarios
+8. **Batch uploads** — SDK handles batching, but send reasonable chunks
+9. **Monitor ingestion** — Check Log Analytics for ingestion status
diff --git a/.github/plugins/azure-sdk-python/skills/azure-monitor-opentelemetry-exporter-py/SKILL.md b/.github/plugins/azure-sdk-python/skills/azure-monitor-opentelemetry-exporter-py/SKILL.md
index a46f7d2a..62a3b66b 100644
--- a/.github/plugins/azure-sdk-python/skills/azure-monitor-opentelemetry-exporter-py/SKILL.md
+++ b/.github/plugins/azure-sdk-python/skills/azure-monitor-opentelemetry-exporter-py/SKILL.md
@@ -210,9 +210,11 @@ exporter = AzureMonitorTraceExporter(
 
 ## Best Practices
 
-1. **Use BatchSpanProcessor** for production (not SimpleSpanProcessor)
-2. **Use ApplicationInsightsSampler** for consistent sampling across services
-3. **Enable offline storage** for reliability in production
-4. **Use AAD authentication** instead of instrumentation keys
-5. **Set export intervals** appropriate for your workload
-6. **Use the distro** (`azure-monitor-opentelemetry`) unless you need custom pipelines
+1. **Pick sync OR async and stay consistent.** Do not mix `azure.xxx` sync clients with `azure.xxx.aio` async clients in the same call path. Choose one mode per module.
+2. **Flush and shut down providers at process exit.** Call the shutdown/flush APIs (e.g. `tracer_provider.shutdown()`, `meter_provider.shutdown()`, `logger_provider.shutdown()`) at process exit to flush telemetry before the process terminates.
+3. **Use BatchSpanProcessor** for production (not SimpleSpanProcessor)
+4. **Use ApplicationInsightsSampler** for consistent sampling across services
+5. **Enable offline storage** for reliability in production
+6. **Use Microsoft Entra authentication** instead of instrumentation keys
+7. **Set export intervals** appropriate for your workload
+8. **Use the distro** (`azure-monitor-opentelemetry`) unless you need custom pipelines
diff --git a/.github/plugins/azure-sdk-python/skills/azure-monitor-opentelemetry-py/SKILL.md b/.github/plugins/azure-sdk-python/skills/azure-monitor-opentelemetry-py/SKILL.md
index f1822588..422d449b 100644
--- a/.github/plugins/azure-sdk-python/skills/azure-monitor-opentelemetry-py/SKILL.md
+++ b/.github/plugins/azure-sdk-python/skills/azure-monitor-opentelemetry-py/SKILL.md
@@ -226,10 +226,12 @@ configure_azure_monitor(
 
 ## Best Practices
 
-1. **Call configure_azure_monitor() early** — Before importing instrumented libraries
-2. **Use environment variables** for connection string in production
-3. **Set cloud role name** for multi-service applications
-4. **Enable sampling** in high-traffic applications
-5. **Use structured logging** for better log analytics queries
-6. **Add custom attributes** to spans for better debugging
-7. **Use AAD authentication** for production workloads
+1. **Pick sync OR async and stay consistent.** Do not mix `azure.xxx` sync clients with `azure.xxx.aio` async clients in the same call path. Choose one mode per module.
+2. **Flush and shut down providers at process exit.** Call the shutdown/flush APIs (e.g. `tracer_provider.shutdown()`, `meter_provider.shutdown()`, `logger_provider.shutdown()`) at process exit to flush telemetry before the process terminates.
+3. **Call configure_azure_monitor() early** — Before importing instrumented libraries
+4. **Use environment variables** for connection string in production
+5. **Set cloud role name** for multi-service applications
+6. **Enable sampling** in high-traffic applications
+7. **Use structured logging** for better log analytics queries
+8. **Add custom attributes** to spans for better debugging
+9. **Use Microsoft Entra authentication** for production workloads
diff --git a/.github/plugins/azure-sdk-python/skills/azure-monitor-query-py/SKILL.md b/.github/plugins/azure-sdk-python/skills/azure-monitor-query-py/SKILL.md
index 8231f1be..fcc05c0c 100644
--- a/.github/plugins/azure-sdk-python/skills/azure-monitor-query-py/SKILL.md
+++ b/.github/plugins/azure-sdk-python/skills/azure-monitor-query-py/SKILL.md
@@ -51,8 +51,6 @@ credential = DefaultAzureCredential(require_envvar=True)
 from azure.monitor.query import LogsQueryClient
 from datetime import timedelta
 
-client = LogsQueryClient(credential)
-
 query = """
 AppRequests
 | where TimeGenerated > ago(1h)
@@ -60,15 +58,16 @@ AppRequests
 | order by TimeGenerated desc
 """
 
-response = client.query_workspace(
-    workspace_id=os.environ["AZURE_LOG_ANALYTICS_WORKSPACE_ID"],
-    query=query,
-    timespan=timedelta(hours=1)
-)
+with LogsQueryClient(credential) as client:
+    response = client.query_workspace(
+        workspace_id=os.environ["AZURE_LOG_ANALYTICS_WORKSPACE_ID"],
+        query=query,
+        timespan=timedelta(hours=1)
+    )
 
-for table in response.tables:
-    for row in table.rows:
-        print(row)
+    for table in response.tables:
+        for row in table.rows:
+            print(row)
 ```
 
 ### Query with Time Range
@@ -137,20 +136,19 @@ elif response.status == LogsQueryStatus.FAILURE:
 from azure.monitor.query import MetricsQueryClient
 from datetime import timedelta
 
-metrics_client = MetricsQueryClient(credential)
-
-response = metrics_client.query_resource(
-    resource_uri=os.environ["AZURE_METRICS_RESOURCE_URI"],
-    metric_names=["Percentage CPU", "Network In Total"],
-    timespan=timedelta(hours=1),
-    granularity=timedelta(minutes=5)
-)
+with MetricsQueryClient(credential) as metrics_client:
+    response = metrics_client.query_resource(
+        resource_uri=os.environ["AZURE_METRICS_RESOURCE_URI"],
+        metric_names=["Percentage CPU", "Network In Total"],
+        timespan=timedelta(hours=1),
+        granularity=timedelta(minutes=5)
+    )
 
-for metric in response.metrics:
-    print(f"{metric.name}:")
-    for time_series in metric.timeseries:
-        for data in time_series.data:
-            print(f"  {data.timestamp}: {data.average}")
+    for metric in response.metrics:
+        print(f"{metric.name}:")
+        for time_series in metric.timeseries:
+            for data in time_series.data:
+                print(f"  {data.timestamp}: {data.average}")
 ```
 
 ### Aggregations
@@ -205,18 +203,14 @@ from azure.monitor.query.aio import LogsQueryClient, MetricsQueryClient
 from azure.identity.aio import DefaultAzureCredential
 
 async def query_logs():
-    credential = DefaultAzureCredential()
-    client = LogsQueryClient(credential)
-    
-    response = await client.query_workspace(
-        workspace_id=workspace_id,
-        query="AppRequests | take 10",
-        timespan=timedelta(hours=1)
-    )
-    
-    await client.close()
-    await credential.close()
-    return response
+    async with DefaultAzureCredential() as credential:
+        async with LogsQueryClient(credential) as client:
+            response = await client.query_workspace(
+                workspace_id=workspace_id,
+                query="AppRequests | take 10",
+                timespan=timedelta(hours=1)
+            )
+            return response
 ```
 
 ## Common Kusto Queries
@@ -252,10 +246,12 @@ AppExceptions
 
 ## Best Practices
 
-1. **Use timedelta** for relative time ranges
-2. **Handle partial results** for large queries
-3. **Use batch queries** when running multiple queries
-4. **Set appropriate granularity** for metrics to reduce data points
-5. **Convert to DataFrame** for easier data analysis
-6. **Use aggregations** to summarize metric data
-7. **Filter by dimensions** to narrow metric results
+1. **Pick sync OR async and stay consistent.** Do not mix `azure.xxx` sync clients with `azure.xxx.aio` async clients in the same call path. Choose one mode per module.
+2. **Always use context managers for clients and async credentials.** Wrap every client in `with Client(...) as client:` (sync) or `async with Client(...) as client:` (async). For async `DefaultAzureCredential` from `azure.identity.aio`, also use `async with credential:` so tokens and transports are cleaned up.
+3. **Use timedelta** for relative time ranges
+4. **Handle partial results** for large queries
+5. **Use batch queries** when running multiple queries
+6. **Set appropriate granularity** for metrics to reduce data points
+7. **Convert to DataFrame** for easier data analysis
+8. **Use aggregations** to summarize metric data
+9. **Filter by dimensions** to narrow metric results
diff --git a/.github/plugins/azure-sdk-python/skills/azure-search-documents-py/SKILL.md b/.github/plugins/azure-sdk-python/skills/azure-search-documents-py/SKILL.md
index 25148e0e..d3ecda31 100644
--- a/.github/plugins/azure-sdk-python/skills/azure-search-documents-py/SKILL.md
+++ b/.github/plugins/azure-sdk-python/skills/azure-search-documents-py/SKILL.md
@@ -112,7 +112,8 @@ index = SearchIndex(
     vector_search=vector_search
 )
 
-index_client.create_or_update_index(index)
+with index_client:
+    index_client.create_or_update_index(index)
 ```
 
 ## Upload Documents
@@ -120,8 +121,6 @@ index_client.create_or_update_index(index)
 ```python
 from azure.search.documents import SearchClient
 
-client = SearchClient(endpoint, "my-index", AzureKeyCredential(key))
-
 documents = [
     {
         "id": "1",
@@ -131,8 +130,9 @@ documents = [
     }
 ]
 
-result = client.upload_documents(documents)
-print(f"Uploaded {len(result)} documents")
+with SearchClient(endpoint, "my-index", AzureKeyCredential(key)) as client:
+    result = client.upload_documents(documents)
+    print(f"Uploaded {len(result)} documents")
 ```
 
 ## Keyword Search
@@ -268,46 +268,49 @@ from azure.search.documents.indexes.models import (
 
 indexer_client = SearchIndexerClient(endpoint, AzureKeyCredential(key))
 
-# Create data source
-data_source = SearchIndexerDataSourceConnection(
-    name="my-datasource",
-    type="azureblob",
-    connection_string=connection_string,
-    container={"name": "documents"}
-)
-indexer_client.create_or_update_data_source_connection(data_source)
-
-# Create skillset
-skillset = SearchIndexerSkillset(
-    name="my-skillset",
-    skills=[
-        EntityRecognitionSkill(
-            inputs=[InputFieldMappingEntry(name="text", source="/document/content")],
-            outputs=[OutputFieldMappingEntry(name="organizations", target_name="organizations")]
-        )
-    ]
-)
-indexer_client.create_or_update_skillset(skillset)
-
-# Create indexer
-indexer = SearchIndexer(
-    name="my-indexer",
-    data_source_name="my-datasource",
-    target_index_name="my-index",
-    skillset_name="my-skillset"
-)
-indexer_client.create_or_update_indexer(indexer)
+with indexer_client:
+    # Create data source
+    data_source = SearchIndexerDataSourceConnection(
+        name="my-datasource",
+        type="azureblob",
+        connection_string=connection_string,
+        container={"name": "documents"}
+    )
+    indexer_client.create_or_update_data_source_connection(data_source)
+
+    # Create skillset
+    skillset = SearchIndexerSkillset(
+        name="my-skillset",
+        skills=[
+            EntityRecognitionSkill(
+                inputs=[InputFieldMappingEntry(name="text", source="/document/content")],
+                outputs=[OutputFieldMappingEntry(name="organizations", target_name="organizations")]
+            )
+        ]
+    )
+    indexer_client.create_or_update_skillset(skillset)
+
+    # Create indexer
+    indexer = SearchIndexer(
+        name="my-indexer",
+        data_source_name="my-datasource",
+        target_index_name="my-index",
+        skillset_name="my-skillset"
+    )
+    indexer_client.create_or_update_indexer(indexer)
 ```
 
 ## Best Practices
 
-1. **Use hybrid search** for best relevance combining vector and keyword
-2. **Enable semantic ranking** for natural language queries
-3. **Index in batches** of 100-1000 documents for efficiency
-4. **Use filters** to narrow results before ranking
-5. **Configure vector dimensions** to match your embedding model
-6. **Use HNSW algorithm** for large-scale vector search
-7. **Create suggesters** at index creation time (cannot add later)
+1. **Pick sync OR async and stay consistent.** Do not mix `azure.xxx` sync clients with `azure.xxx.aio` async clients in the same call path. Choose one mode per module.
+2. **Always use context managers for clients and async credentials.** Wrap every client in `with Client(...) as client:` (sync) or `async with Client(...) as client:` (async). For async `DefaultAzureCredential` from `azure.identity.aio`, also use `async with credential:` so tokens and transports are cleaned up.
+3. **Use hybrid search** for best relevance combining vector and keyword
+4. **Enable semantic ranking** for natural language queries
+5. **Index in batches** of 100-1000 documents for efficiency
+6. **Use filters** to narrow results before ranking
+7. **Configure vector dimensions** to match your embedding model
+8. **Use HNSW algorithm** for large-scale vector search
+9. **Create suggesters** at index creation time (cannot add later)
 
 ## Reference Files
 
@@ -422,7 +425,8 @@ index = SearchIndex(
 )
 
 index_client = SearchIndexClient(endpoint, credential)
-index_client.create_or_update_index(index)
+with index_client:
+    index_client.create_or_update_index(index)
 ```
 
 ## Document Operations
@@ -435,11 +439,11 @@ with SearchIndexingBufferedSender(endpoint, index_name, credential) as sender:
     sender.upload_documents(documents)
 
 # Direct operations via SearchClient
-search_client = SearchClient(endpoint, index_name, credential)
-search_client.upload_documents(documents)      # Add new
-search_client.merge_documents(documents)       # Update existing
-search_client.merge_or_upload_documents(documents)  # Upsert
-search_client.delete_documents(documents)      # Remove
+with SearchClient(endpoint, index_name, credential) as search_client:
+    search_client.upload_documents(documents)      # Add new
+    search_client.merge_documents(documents)       # Update existing
+    search_client.merge_or_upload_documents(documents)  # Upsert
+    search_client.delete_documents(documents)      # Remove
 ```
 
 ## Search Patterns
@@ -500,7 +504,7 @@ async with SearchClient(endpoint, index_name, credential) as client:
 ## Best Practices
 
 1. **Use environment variables** for endpoints, keys, and deployment names
-2. **Prefer `DefaultAzureCredential`** over API keys for production
+2. **Use `DefaultAzureCredential`** for code that runs locally (instead of API keys). Use a specific token credential for code that runs in Azure.
 3. **Use `SearchIndexingBufferedSender`** for batch uploads (handles batching/retries)
 4. **Always define semantic configuration** for agentic retrieval indexes
 5. **Use `create_or_update_index`** for idempotent index creation
diff --git a/.github/plugins/azure-sdk-python/skills/azure-servicebus-py/SKILL.md b/.github/plugins/azure-sdk-python/skills/azure-servicebus-py/SKILL.md
index 194b28c9..ed373d57 100644
--- a/.github/plugins/azure-sdk-python/skills/azure-servicebus-py/SKILL.md
+++ b/.github/plugins/azure-sdk-python/skills/azure-servicebus-py/SKILL.md
@@ -259,13 +259,14 @@ with ServiceBusClient(
 
 ## Best Practices
 
-1. **Use async client** for production workloads
-2. **Use context managers** (`async with`) for proper cleanup
-3. **Complete messages** after successful processing
-4. **Use dead-letter queue** for poison messages
-5. **Use sessions** for ordered, FIFO processing
-6. **Use message batches** for high-throughput scenarios
-7. **Set `max_wait_time`** to avoid infinite blocking
+1. **Pick sync OR async and stay consistent.** Do not mix `azure.xxx` sync clients with `azure.xxx.aio` async clients in the same call path. Choose one mode per module.
+2. **Always use context managers for clients and async credentials.** Wrap every client in `with Client(...) as client:` (sync) or `async with Client(...) as client:` (async) for proper cleanup. For async `DefaultAzureCredential` from `azure.identity.aio`, also use `async with credential:` so tokens and transports are cleaned up.
+3. **Use async client** for production workloads
+4. **Complete messages** after successful processing
+5. **Use dead-letter queue** for poison messages
+6. **Use sessions** for ordered, FIFO processing
+7. **Use message batches** for high-throughput scenarios
+8. **Set `max_wait_time`** to avoid infinite blocking
 
 ## Reference Files
 
diff --git a/.github/plugins/azure-sdk-python/skills/azure-speech-to-text-rest-py/SKILL.md b/.github/plugins/azure-sdk-python/skills/azure-speech-to-text-rest-py/SKILL.md
index c6e6dd3d..b0f96a3e 100644
--- a/.github/plugins/azure-sdk-python/skills/azure-speech-to-text-rest-py/SKILL.md
+++ b/.github/plugins/azure-sdk-python/skills/azure-speech-to-text-rest-py/SKILL.md
@@ -351,12 +351,14 @@ Common language codes (see [full list](https://learn.microsoft.com/azure/ai-serv
 
 ## Best Practices
 
-1. **Use WAV PCM 16kHz mono** for best compatibility
-2. **Enable chunked transfer** for lower latency
-3. **Cache access tokens** for 9 minutes (valid for 10)
-4. **Specify the correct language** for accurate recognition
-5. **Use detailed format** when you need confidence scores
-6. **Handle all RecognitionStatus values** in production code
+1. **Pick sync OR async and stay consistent.** Do not mix `azure.xxx` sync clients with `azure.xxx.aio` async clients in the same call path. Choose one mode per module.
+2. **Always use context managers for clients.** Use `with httpx.Client(...) as client:` (sync) or `async with httpx.AsyncClient(...) as client:` (async) so connections are pooled and closed deterministically.
+3. **Use WAV PCM 16kHz mono** for best compatibility
+4. **Enable chunked transfer** for lower latency
+5. **Cache access tokens** for 9 minutes (valid for 10)
+6. **Specify the correct language** for accurate recognition
+7. **Use detailed format** when you need confidence scores
+8. **Handle all RecognitionStatus values** in production code
 
 ## When NOT to Use This API
 
diff --git a/.github/plugins/azure-sdk-python/skills/azure-storage-blob-py/SKILL.md b/.github/plugins/azure-sdk-python/skills/azure-storage-blob-py/SKILL.md
index b6dc6081..e7b8c360 100644
--- a/.github/plugins/azure-sdk-python/skills/azure-storage-blob-py/SKILL.md
+++ b/.github/plugins/azure-sdk-python/skills/azure-storage-blob-py/SKILL.md
@@ -139,20 +139,19 @@ blob_client.delete_blob(delete_snapshots="include")
 
 ```python
 # Configure chunk sizes for large uploads/downloads
-blob_client = BlobClient(
+with BlobClient(
     account_url=account_url,
     container_name="mycontainer",
     blob_name="large-file.zip",
     credential=credential,
     max_block_size=4 * 1024 * 1024,  # 4 MiB blocks
     max_single_put_size=64 * 1024 * 1024  # 64 MiB single upload limit
-)
-
-# Parallel upload
-blob_client.upload_blob(data, max_concurrency=4)
+) as blob_client:
+    # Parallel upload
+    blob_client.upload_blob(data, max_concurrency=4)
 
-# Parallel download
-download_stream = blob_client.download_blob(max_concurrency=4)
+    # Parallel download
+    download_stream = blob_client.download_blob(max_concurrency=4)
 ```
 
 ## SAS Tokens
@@ -200,13 +199,12 @@ from azure.identity.aio import DefaultAzureCredential
 from azure.storage.blob.aio import BlobServiceClient
 
 async def upload_async():
-    credential = DefaultAzureCredential()
-    
-    async with BlobServiceClient(account_url, credential=credential) as client:
-        blob_client = client.get_blob_client("mycontainer", "sample.txt")
-        
-        with open("./file.txt", "rb") as data:
-            await blob_client.upload_blob(data, overwrite=True)
+    async with DefaultAzureCredential() as credential:
+        async with BlobServiceClient(account_url, credential=credential) as client:
+            blob_client = client.get_blob_client("mycontainer", "sample.txt")
+            
+            with open("./file.txt", "rb") as data:
+                await blob_client.upload_blob(data, overwrite=True)
 
 # Download async
 async def download_async():
@@ -219,10 +217,11 @@ async def download_async():
 
 ## Best Practices
 
-1. **Use DefaultAzureCredential** instead of connection strings
-2. **Use context managers** for async clients
-3. **Set `overwrite=True`** explicitly when re-uploading
-4. **Use `max_concurrency`** for large file transfers
-5. **Prefer `readinto()`** over `readall()` for memory efficiency
-6. **Use `walk_blobs()`** for hierarchical listing
-7. **Set appropriate content types** for web-served blobs
+1. **Pick sync OR async and stay consistent.** Do not mix `azure.storage.blob` sync clients with `azure.storage.blob.aio` async clients in the same call path. Choose one mode per module.
+2. **Always use context managers for clients and async credentials.** Wrap every client in `with BlobServiceClient(...) as client:` (sync) or `async with BlobServiceClient(...) as client:` (async). For async `DefaultAzureCredential` from `azure.identity.aio`, also use `async with credential:` so tokens and transports are cleaned up.
+3. **Use `DefaultAzureCredential`** for code that runs locally (instead of connection strings). Use a specific token credential for code that runs in Azure.
+4. **Set `overwrite=True`** explicitly when re-uploading
+5. **Use `max_concurrency`** for large file transfers
+6. **Prefer `readinto()`** over `readall()` for memory efficiency
+7. **Use `walk_blobs()`** for hierarchical listing
+8. **Set appropriate content types** for web-served blobs
diff --git a/.github/plugins/azure-sdk-python/skills/azure-storage-file-datalake-py/SKILL.md b/.github/plugins/azure-sdk-python/skills/azure-storage-file-datalake-py/SKILL.md
index d8de6c90..a9274fe7 100644
--- a/.github/plugins/azure-sdk-python/skills/azure-storage-file-datalake-py/SKILL.md
+++ b/.github/plugins/azure-sdk-python/skills/azure-storage-file-datalake-py/SKILL.md
@@ -191,19 +191,18 @@ from azure.storage.filedatalake.aio import DataLakeServiceClient
 from azure.identity.aio import DefaultAzureCredential
 
 async def datalake_operations():
-    credential = DefaultAzureCredential()
-    
-    async with DataLakeServiceClient(
-        account_url="https://<account>.dfs.core.windows.net",
-        credential=credential
-    ) as service_client:
-        file_system_client = service_client.get_file_system_client("myfilesystem")
-        file_client = file_system_client.get_file_client("test.txt")
-        
-        await file_client.upload_data(b"async content", overwrite=True)
-        
-        download = await file_client.download_file()
-        content = await download.readall()
+    async with DefaultAzureCredential() as credential:
+        async with DataLakeServiceClient(
+            account_url="https://<account>.dfs.core.windows.net",
+            credential=credential
+        ) as service_client:
+            file_system_client = service_client.get_file_system_client("myfilesystem")
+            file_client = file_system_client.get_file_client("test.txt")
+            
+            await file_client.upload_data(b"async content", overwrite=True)
+            
+            download = await file_client.download_file()
+            content = await download.readall()
 
 import asyncio
 asyncio.run(datalake_operations())
@@ -211,10 +210,12 @@ asyncio.run(datalake_operations())
 
 ## Best Practices
 
-1. **Use hierarchical namespace** for file system semantics
-2. **Use `append_data` + `flush_data`** for large file uploads
-3. **Set ACLs at directory level** and inherit to children
-4. **Use async client** for high-throughput scenarios
-5. **Use `get_paths` with `recursive=True`** for full directory listing
-6. **Set metadata** for custom file attributes
-7. **Consider Blob API** for simple object storage use cases
+1. **Pick sync OR async and stay consistent.** Do not mix `azure.storage.filedatalake` sync clients with `azure.storage.filedatalake.aio` async clients in the same call path. Choose one mode per module.
+2. **Always use context managers for clients and async credentials.** Wrap every client in `with DataLakeServiceClient(...) as client:` (sync) or `async with DataLakeServiceClient(...) as client:` (async). For async `DefaultAzureCredential` from `azure.identity.aio`, also use `async with credential:` so tokens and transports are cleaned up.
+3. **Use hierarchical namespace** for file system semantics
+4. **Use `append_data` + `flush_data`** for large file uploads
+5. **Set ACLs at directory level** and inherit to children
+6. **Use async client** for high-throughput scenarios
+7. **Use `get_paths` with `recursive=True`** for full directory listing
+8. **Set metadata** for custom file attributes
+9. **Consider Blob API** for simple object storage use cases
diff --git a/.github/plugins/azure-sdk-python/skills/azure-storage-file-share-py/SKILL.md b/.github/plugins/azure-sdk-python/skills/azure-storage-file-share-py/SKILL.md
index 1a53c673..66f43e98 100644
--- a/.github/plugins/azure-sdk-python/skills/azure-storage-file-share-py/SKILL.md
+++ b/.github/plugins/azure-sdk-python/skills/azure-storage-file-share-py/SKILL.md
@@ -217,16 +217,12 @@ from azure.storage.fileshare.aio import ShareServiceClient
 from azure.identity.aio import DefaultAzureCredential
 
 async def upload_file():
-    credential = DefaultAzureCredential()
-    service = ShareServiceClient(account_url, credential=credential)
-    
-    share = service.get_share_client("my-share")
-    file_client = share.get_file_client("test.txt")
-    
-    await file_client.upload_file("Hello!")
-    
-    await service.close()
-    await credential.close()
+    async with DefaultAzureCredential() as credential:
+        async with ShareServiceClient(account_url, credential=credential) as service:
+            share = service.get_share_client("my-share")
+            file_client = share.get_file_client("test.txt")
+            
+            await file_client.upload_file("Hello!")
 ```
 
 ## Client Types
@@ -240,10 +236,11 @@ async def upload_file():
 
 ## Best Practices
 
-1. **Use connection string** for simplest setup
-2. **Use Entra ID** for production with RBAC
-3. **Stream large files** using chunks() to avoid memory issues
-4. **Create snapshots** before major changes
-5. **Set quotas** to prevent unexpected storage costs
-6. **Use ranges** for partial file updates
-7. **Close async clients** explicitly
+1. **Pick sync OR async and stay consistent.** Do not mix `azure.storage.fileshare` sync clients with `azure.storage.fileshare.aio` async clients in the same call path. Choose one mode per module.
+2. **Always use context managers for clients and async credentials.** Wrap every client in `with ShareServiceClient(...) as client:` (sync) or `async with ShareServiceClient(...) as client:` (async). For async `DefaultAzureCredential` from `azure.identity.aio`, also use `async with credential:` so tokens and transports are cleaned up.
+3. **Use connection string** for simplest setup
+4. **Use Microsoft Entra ID** for production with RBAC
+5. **Stream large files** using chunks() to avoid memory issues
+6. **Create snapshots** before major changes
+7. **Set quotas** to prevent unexpected storage costs
+8. **Use ranges** for partial file updates
diff --git a/.github/plugins/azure-sdk-python/skills/azure-storage-queue-py/SKILL.md b/.github/plugins/azure-sdk-python/skills/azure-storage-queue-py/SKILL.md
index 4bd073f0..fa26edf5 100644
--- a/.github/plugins/azure-sdk-python/skills/azure-storage-queue-py/SKILL.md
+++ b/.github/plugins/azure-sdk-python/skills/azure-storage-queue-py/SKILL.md
@@ -199,24 +199,25 @@ asyncio.run(queue_operations())
 from azure.storage.queue import QueueClient, BinaryBase64EncodePolicy, BinaryBase64DecodePolicy
 
 # For binary data
-queue_client = QueueClient(
+with QueueClient(
     account_url=account_url,
     queue_name="myqueue",
     credential=credential,
     message_encode_policy=BinaryBase64EncodePolicy(),
     message_decode_policy=BinaryBase64DecodePolicy()
-)
-
-# Send bytes
-queue_client.send_message(b"Binary content")
+) as queue_client:
+    # Send bytes
+    queue_client.send_message(b"Binary content")
 ```
 
 ## Best Practices
 
-1. **Delete messages after processing** to prevent reprocessing
-2. **Set appropriate visibility timeout** based on processing time
-3. **Handle `dequeue_count`** for poison message detection
-4. **Use async client** for high-throughput scenarios
-5. **Use `peek_messages`** for monitoring without affecting queue
-6. **Set `time_to_live`** to prevent stale messages
-7. **Consider Service Bus** for advanced features (sessions, topics)
+1. **Pick sync OR async and stay consistent.** Do not mix `azure.xxx` sync clients with `azure.xxx.aio` async clients in the same call path. Choose one mode per module.
+2. **Always use context managers for clients and async credentials.** Wrap every client in `with Client(...) as client:` (sync) or `async with Client(...) as client:` (async). For async `DefaultAzureCredential` from `azure.identity.aio`, also use `async with credential:` so tokens and transports are cleaned up.
+3. **Delete messages after processing** to prevent reprocessing
+4. **Set appropriate visibility timeout** based on processing time
+5. **Handle `dequeue_count`** for poison message detection
+6. **Use async client** for high-throughput scenarios
+7. **Use `peek_messages`** for monitoring without affecting queue
+8. **Set `time_to_live`** to prevent stale messages
+9. **Consider Service Bus** for advanced features (sessions, topics)
diff --git a/.github/plugins/azure-sdk-python/skills/fastapi-router-py/SKILL.md b/.github/plugins/azure-sdk-python/skills/fastapi-router-py/SKILL.md
index f6bff23c..0d6f32e6 100644
--- a/.github/plugins/azure-sdk-python/skills/fastapi-router-py/SKILL.md
+++ b/.github/plugins/azure-sdk-python/skills/fastapi-router-py/SKILL.md
@@ -54,3 +54,8 @@ async def list_items() -> list[Item]:
 3. Create corresponding Pydantic models
 4. Create service layer if needed
 5. Add frontend API functions
+
+## Best Practices
+
+1. **Pick `def` or `async def` per endpoint based on whether you call async I/O;** do not mix sync and async blocking calls in one handler.
+2. **Manage long-lived resources (DB pools, HTTP clients) in `lifespan` and inject via `Depends`;** use `with`/`async with` for per-request resources.
diff --git a/.github/plugins/azure-sdk-python/skills/hosted-agents-v2-py/SKILL.md b/.github/plugins/azure-sdk-python/skills/hosted-agents-v2-py/SKILL.md
index 2df659b1..4685a81b 100644
--- a/.github/plugins/azure-sdk-python/skills/hosted-agents-v2-py/SKILL.md
+++ b/.github/plugins/azure-sdk-python/skills/hosted-agents-v2-py/SKILL.md
@@ -299,50 +299,48 @@ from azure.ai.projects.models import (
 )
 from azure.identity import DefaultAzureCredential
 
-
-project = AIProjectClient(
+with AIProjectClient(
     endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
     credential=DefaultAzureCredential(),
     allow_preview=True,
-)
-
-agent = project.agents.create_version(
-    agent_name="my-hosted-agent",
-    definition=HostedAgentDefinition(
-        container_protocol_versions=[
-            ProtocolVersionRecord(
-                protocol=AgentProtocol.RESPONSES,
-                version="1.0.0",
-            ),
-            ProtocolVersionRecord(
-                protocol=AgentProtocol.INVOCATIONS,
-                version="1.0.0",
-            ),
-        ],
-        image="myregistry.azurecr.io/my-agent:v1",
-        cpu="1",
-        memory="2Gi",
-        environment_variables={
-            "AZURE_AI_MODEL_DEPLOYMENT_NAME": os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
-        },
-    ),
-)
-
-while True:
-    version = project.agents.get_version(
+) as project:
+    agent = project.agents.create_version(
         agent_name="my-hosted-agent",
-        agent_version=agent.version,
+        definition=HostedAgentDefinition(
+            container_protocol_versions=[
+                ProtocolVersionRecord(
+                    protocol=AgentProtocol.RESPONSES,
+                    version="1.0.0",
+                ),
+                ProtocolVersionRecord(
+                    protocol=AgentProtocol.INVOCATIONS,
+                    version="1.0.0",
+                ),
+            ],
+            image="myregistry.azurecr.io/my-agent:v1",
+            cpu="1",
+            memory="2Gi",
+            environment_variables={
+                "AZURE_AI_MODEL_DEPLOYMENT_NAME": os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+            },
+        ),
     )
-    status = version["status"]
-    if status == "active":
-        break
-    if status == "failed":
-        raise RuntimeError(f"Hosted agent provisioning failed: {version['error']}")
-    time.sleep(5)
-
-openai_client = project.get_openai_client(agent_name="my-hosted-agent")
-response = openai_client.responses.create(input="Hello!")
-print(response.output_text)
+
+    while True:
+        version = project.agents.get_version(
+            agent_name="my-hosted-agent",
+            agent_version=agent.version,
+        )
+        status = version["status"]
+        if status == "active":
+            break
+        if status == "failed":
+            raise RuntimeError(f"Hosted agent provisioning failed: {version['error']}")
+        time.sleep(5)
+
+    openai_client = project.get_openai_client(agent_name="my-hosted-agent")
+    response = openai_client.responses.create(input="Hello!")
+    print(response.output_text)
 ```
 
 For Invocations over REST, call the dedicated endpoint:
@@ -427,14 +425,16 @@ REST calls to hosted-agent endpoints require `Foundry-Features: HostedAgents=V1P
 
 ## Best Practices
 
-1. Start with Responses unless the caller requires arbitrary JSON, custom SSE, or non-OpenAI protocol semantics.
-2. Use Agent Framework for new Python/C# agents; use protocol libraries for existing frameworks or custom code.
-3. Test locally on `http://localhost:8088/responses` or `http://localhost:8088/invocations` before deployment.
-4. Use immutable image tags, not `latest`, for production versions.
-5. Keep secrets out of images and environment variables; prefer managed identity and managed connections.
-6. Treat each agent version as immutable. Create a new version for runtime changes.
-7. Use Application Insights and OpenTelemetry from the protocol libraries for logs, traces, and metrics.
-8. Clean up with `azd down`, `project.agents.delete_version(...)`, or REST delete calls when finished.
+1. **Pick sync OR async and stay consistent.** Do not mix `azure.xxx` sync clients with `azure.xxx.aio` async clients in the same call path. Choose one mode per module.
+2. **Always use context managers for clients and async credentials.** Wrap every client in `with Client(...) as client:` (sync) or `async with Client(...) as client:` (async). For async `DefaultAzureCredential` from `azure.identity.aio`, also use `async with credential:` so tokens and transports are cleaned up.
+3. Start with Responses unless the caller requires arbitrary JSON, custom SSE, or non-OpenAI protocol semantics.
+4. Use Agent Framework for new Python/C# agents; use protocol libraries for existing frameworks or custom code.
+5. Test locally on `http://localhost:8088/responses` or `http://localhost:8088/invocations` before deployment.
+6. Use immutable image tags, not `latest`, for production versions.
+7. Keep secrets out of images and environment variables; prefer managed identity and managed connections.
+8. Treat each agent version as immutable. Create a new version for runtime changes.
+9. Use Application Insights and OpenTelemetry from the protocol libraries for logs, traces, and metrics.
+10. Clean up with `azd down`, `project.agents.delete_version(...)`, or REST delete calls when finished.
 
 ## Reference Links
 
diff --git a/.github/plugins/azure-sdk-python/skills/m365-agents-py/SKILL.md b/.github/plugins/azure-sdk-python/skills/m365-agents-py/SKILL.md
index f6499f0e..3e9d2c7b 100644
--- a/.github/plugins/azure-sdk-python/skills/m365-agents-py/SKILL.md
+++ b/.github/plugins/azure-sdk-python/skills/m365-agents-py/SKILL.md
@@ -325,14 +325,16 @@ asyncio.run(main())
 
 ## Best Practices
 
-1. Use `microsoft_agents` import prefix (underscores, not dots).
-2. Use `MemoryStorage` only for development; use BlobStorage or CosmosDB in production.
-3. Always use `load_configuration_from_env(environ)` to load SDK configuration.
-4. Include `jwt_authorization_middleware` in aiohttp Application middlewares.
-5. Use `MsalConnectionManager` for MSAL-based authentication.
-6. Call `end_stream()` in finally blocks when using streaming responses.
-7. Use `auth_handlers` parameter on message decorators for OAuth-protected routes.
-8. Keep secrets in environment variables, not in source code.
+1. **This skill is async-first (aiohttp-based).** Use async handlers and `async with` for aiohttp sessions.
+2. **Always use context managers for clients and async credentials.** Wrap every client in `with Client(...) as client:` (sync) or `async with Client(...) as client:` (async). For async `DefaultAzureCredential` from `azure.identity.aio`, also use `async with credential:` so tokens and transports are cleaned up.
+3. Use `microsoft_agents` import prefix (underscores, not dots).
+4. Use `MemoryStorage` only for development; use BlobStorage or CosmosDB in production.
+5. Always use `load_configuration_from_env(environ)` to load SDK configuration.
+6. Include `jwt_authorization_middleware` in aiohttp Application middlewares.
+7. Use `MsalConnectionManager` for MSAL-based authentication.
+8. Call `end_stream()` in finally blocks when using streaming responses.
+9. Use `auth_handlers` parameter on message decorators for OAuth-protected routes.
+10. Keep secrets in environment variables, not in source code.
 
 ## Reference Links
 
diff --git a/.github/skills/skill-creator/SKILL.md b/.github/skills/skill-creator/SKILL.md
index a80940ac..3a5588a6 100644
--- a/.github/skills/skill-creator/SKILL.md
+++ b/.github/skills/skill-creator/SKILL.md
@@ -175,11 +175,45 @@ Azure SDKs use consistent verbs across all languages:
 
 See `references/azure-sdk-patterns.md` for detailed patterns including:
 
-- **Python**: `ItemPaged`, `LROPoller`, context managers, Sphinx docstrings
+- **Python**: `ItemPaged`, `LROPoller`, context managers, Sphinx docstrings. Pick **sync or async** for the whole skill and stick with it — never mix. Always show `with` / `async with` context managers first.
 - **.NET**: `Response<T>`, `Pageable<T>`, `Operation<T>`, mocking support
 - **Java**: Builder pattern, `PagedIterable`/`PagedFlux`, Reactor types
 - **TypeScript**: `PagedAsyncIterableIterator`, `AbortSignal`, browser considerations
 
+### Required Best Practices in Every Python Skill (User-Facing)
+
+> **Scope:** This section applies **only to Python skills** (`-py` suffix). The sync/async and context-manager rules below are Python-specific idioms. Skills for .NET, Java, and TypeScript should follow their own language idioms and are not required to include these items.
+
+**These rules are not just authoring conventions for the skill itself — they MUST be explicitly written into every generated Python skill's `## Best Practices` section so end users who follow the skill apply them in their own code.**
+
+Add both items verbatim (adapted only for SDK specifics) as the **first two items** of the Best Practices list. Do not assume users will infer them from examples.
+
+**Standard wording (Python):**
+
+```markdown
+1. **Pick sync OR async and stay consistent.** Do not mix `azure.xxx` sync clients with `azure.xxx.aio` async clients in the same call path. Choose one mode per module.
+2. **Always use context managers for clients and async credentials.** Wrap every client in `with Client(...) as client:` (sync) or `async with Client(...) as client:` (async). For async `DefaultAzureCredential` from `azure.identity.aio`, also use `async with credential:` so tokens and transports are cleaned up.
+3. **Use `DefaultAzureCredential`** for code that runs locally. Use a specific token credential (e.g. `ManagedIdentityCredential`, `WorkloadIdentityCredential`) for code that runs in Azure.
+```
+
+**Variants to apply when the SDK shape differs:**
+
+| Skill type | Adjust item #1 to | Adjust item #2 to |
+|---|---|---|
+| Async-only SDK (e.g. voicelive) | "This SDK is async-only; use the `.aio` namespace throughout." | keep standard |
+| Async-first framework (agent framework, m365-agents) | "This SDK is async-first — use `async def` handlers and `async with` throughout." | keep standard |
+| Provider-pattern (OpenTelemetry exporters/distro) | keep standard | "Call `provider.shutdown()` / `flush()` at process exit to flush telemetry — providers are not context managers." |
+| REST-over-httpx skills | keep standard | "Use `with httpx.Client(...) as client:` (sync) or `async with httpx.AsyncClient(...) as client:` (async) so connections pool and close deterministically." |
+| Identity skill | keep standard | "Use credentials as context managers (`with DefaultAzureCredential() as credential:`) when they own token caches / HTTP transports you want cleaned up; for async, use `async with` on credentials from `azure.identity.aio`." |
+| FastAPI (non-Azure) | "Pick `def` or `async def` per endpoint based on whether you call async I/O; do not mix sync and blocking calls in one handler." | "Manage long-lived resources (DB pools, HTTP clients) in `lifespan` and inject via `Depends`; use `with`/`async with` for per-request resources." |
+| Pure model/schema skill (no I/O, e.g. pydantic) | **skip both** — not applicable | **skip** |
+
+**Enforcement in code examples.** Every code example inside the skill must itself obey both rules, so the skill demonstrates what it prescribes:
+
+- Do not show sync and async calls interleaved in the same example. If you must show both modes, keep the primary example in one mode and isolate the alternative into a single `### Async variant` (or `### Sync variant`) subsection with its own complete example.
+- Every client instantiation in every example must be wrapped in `with` / `async with`. The only permitted exception is the mandatory Authentication snippet (which illustrates the credential + client construction pattern) and framework lifespan patterns where a client is owned by the app (e.g. FastAPI `lifespan`).
+- When async credentials from `azure.identity.aio` appear in an example, wrap them in `async with credential:` alongside the client.
+
 ### Handling Deprecated or Rebranded SDKs
 
 When an Azure SDK has been deprecated or rebranded, update skills to guide users toward the current package while maintaining backward compatibility:
@@ -678,6 +712,8 @@ azure-ai-agents/
 | Skip acceptance criteria         | Skills without tests can't be validated    |
 | Skip symlink categorization      | Skills won't be discoverable by category   |
 | Use wrong import paths           | Azure SDKs have specific module structures |
+| Omit sync/async + context-manager bullets from Best Practices in Python skills | End users won't follow rules that aren't written down; examples alone aren't enough |
+| Mix sync and async in the same Python example | Demonstrates the anti-pattern the skill is supposed to prevent |
 
 ---
 
@@ -697,6 +733,8 @@ Before completing a skill:
 - [ ] Authentication uses `DefaultAzureCredential`
 - [ ] Includes cleanup/delete in examples
 - [ ] References organized by feature
+- [ ] **(Python skills only) Best Practices section contains the two user-facing rules** (sync-or-async consistency + context managers for clients and async credentials), using the variant matched to the skill type
+- [ ] **(Python skills only)** Every code example obeys both rules (no mixed sync/async; every client wrapped in `with` / `async with`)
 
 **Categorization:**
 
diff --git a/.github/skills/skill-creator/references/azure-sdk-patterns.md b/.github/skills/skill-creator/references/azure-sdk-patterns.md
index 057543dd..6c78b596 100644
--- a/.github/skills/skill-creator/references/azure-sdk-patterns.md
+++ b/.github/skills/skill-creator/references/azure-sdk-patterns.md
@@ -80,6 +80,44 @@ class AsyncConfigurationClient:
     pass
 ```
 
+### Sync vs Async: Pick One, Don't Mix
+
+**Rule:** Within a single module, script, or code path, use **either** the sync client **or** the async client — never both.
+
+- Sync clients live in `azure.<service>` (e.g., `azure.ai.projects.AIProjectClient`).
+- Async clients live in `azure.<service>.aio` (e.g., `azure.ai.projects.aio.AIProjectClient`).
+- Mixing sync calls inside an `async def` (or awaiting inside a sync function) blocks the event loop, breaks context managers, and produces subtle concurrency bugs.
+
+```python
+# ✅ Good — all sync
+from azure.ai.projects import AIProjectClient
+from azure.identity import DefaultAzureCredential
+
+with AIProjectClient(endpoint=endpoint, credential=DefaultAzureCredential()) as client:
+    agent = client.agents.get_agent("agent-id")
+
+# ✅ Good — all async
+from azure.ai.projects.aio import AIProjectClient
+from azure.identity.aio import DefaultAzureCredential
+
+async with DefaultAzureCredential() as credential, \
+           AIProjectClient(endpoint=endpoint, credential=credential) as client:
+    agent = await client.agents.get_agent("agent-id")
+
+# ❌ Bad — sync client inside async function
+async def run():
+    from azure.ai.projects import AIProjectClient  # sync!
+    client = AIProjectClient(endpoint=endpoint, credential=cred)
+    agent = client.agents.get_agent("agent-id")  # blocks the event loop
+
+# ❌ Bad — mixing sync and async credentials
+from azure.identity import DefaultAzureCredential          # sync
+from azure.ai.projects.aio import AIProjectClient          # async
+# Async client must be paired with azure.identity.aio.DefaultAzureCredential
+```
+
+When writing a skill, pick one model based on the target runtime (FastAPI/async framework → async; scripts/CLIs → sync) and make every example in the skill consistent with that choice.
+
 ### Pagination: ItemPaged / AsyncItemPaged
 
 ```python
@@ -123,18 +161,33 @@ async_poller = await async_client.begin_create_resource(config)
 result = await async_poller.result()
 ```
 
-### Context Managers
+### Context Managers (Strongly Preferred)
+
+**Always prefer context managers (`with` / `async with`) over manually constructing and closing clients.** They guarantee the underlying HTTP transport and credential sessions are closed, even on exceptions, and make the sync/async choice explicit at the call site.
 
 ```python
-# Recommended pattern
+# ✅ Preferred — sync
 with ConfigurationClient(endpoint, credential) as client:
     setting = client.get_setting("key")
 
-# Async
-async with AsyncConfigurationClient(endpoint, credential) as client:
+# ✅ Preferred — async (also wrap the async credential)
+from azure.identity.aio import DefaultAzureCredential
+
+async with DefaultAzureCredential() as credential, \
+           AsyncConfigurationClient(endpoint, credential) as client:
     setting = await client.get_setting("key")
+
+# ⚠️ Only acceptable when the client lifetime spans the whole app
+# (e.g., FastAPI lifespan, long-running service). Close it explicitly.
+client = ConfigurationClient(endpoint, credential)
+try:
+    setting = client.get_setting("key")
+finally:
+    client.close()  # or `await client.close()` for async clients
 ```
 
+Skills should show the context-manager form first. Only introduce the explicit `close()` pattern when the scenario genuinely requires a long-lived client (e.g., dependency-injected singletons), and always pair it with `try/finally` or a framework lifecycle hook.
+
 ### Error Handling
 
 ```python
diff --git a/docs/index.html b/docs/index.html
index 015701d5..7a8c819e 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -20,7 +20,7 @@
 Docs
 </a> <a href="https://github.com/microsoft/skills" target="_blank" rel="noopener noreferrer" class="mobile-nav-link github-mobile-link" data-astro-cid-sckkx6r4> <svg viewBox="0 0 24 24" width="20" height="20" fill="currentColor" data-astro-cid-sckkx6r4> <path d="M12 0c-6.626 0-12 5.373-12 12 0 5.302 3.438 9.8 8.207 11.387.599.111.793-.261.793-.577v-2.234c-3.338.726-4.033-1.416-4.033-1.416-.546-1.387-1.333-1.756-1.333-1.756-1.089-.745.083-.729.083-.729 1.205.084 1.839 1.237 1.839 1.237 1.07 1.834 2.807 1.304 3.492.997.107-.775.418-1.305.762-1.604-2.665-.305-5.467-1.334-5.467-5.931 0-1.311.469-2.381 1.236-3.221-.124-.303-.535-1.524.117-3.176 0 0 1.008-.322 3.301 1.23.957-.266 1.983-.399 3.003-.404 1.02.005 2.047.138 3.006.404 2.291-1.552 3.297-1.23 3.297-1.23.653 1.653.242 2.874.118 3.176.77.84 1.235 1.911 1.235 3.221 0 4.609-2.807 5.624-5.479 5.921.43.372.823 1.102.823 2.222v3.293c0 .319.192.694.801.576 4.765-1.589 8.199-6.086 8.199-11.386 0-6.627-5.373-12-12-12z" data-astro-cid-sckkx6r4></path> </svg>
 GitHub
-</a> </div> </nav> <div class="mobile-nav-overlay" id="mobile-nav-overlay" data-astro-cid-sckkx6r4></div> <main class="main" id="main-content" data-astro-cid-sckkx6r4>  <div class="container" data-astro-cid-j7pv25f6> <!-- Skills Section with Filters --> <style>astro-island,astro-slot,astro-static-slot{display:contents}</style><script>(()=>{var e=async t=>{await(await t())()};(self.Astro||(self.Astro={})).load=e;window.dispatchEvent(new Event("astro:load"));})();</script><script>(()=>{var A=Object.defineProperty;var g=(i,o,a)=>o in i?A(i,o,{enumerable:!0,configurable:!0,writable:!0,value:a}):i[o]=a;var d=(i,o,a)=>g(i,typeof o!="symbol"?o+"":o,a);{let i={0:t=>m(t),1:t=>a(t),2:t=>new RegExp(t),3:t=>new Date(t),4:t=>new Map(a(t)),5:t=>new Set(a(t)),6:t=>BigInt(t),7:t=>new URL(t),8:t=>new Uint8Array(t),9:t=>new Uint16Array(t),10:t=>new Uint32Array(t),11:t=>1/0*t},o=t=>{let[l,e]=t;return l in i?i[l](e):void 0},a=t=>t.map(o),m=t=>typeof t!="object"||t===null?t:Object.fromEntries(Object.entries(t).map(([l,e])=>[l,o(e)]));class y extends HTMLElement{constructor(){super(...arguments);d(this,"Component");d(this,"hydrator");d(this,"hydrate",async()=>{var b;if(!this.hydrator||!this.isConnected)return;let e=(b=this.parentElement)==null?void 0:b.closest("astro-island[ssr]");if(e){e.addEventListener("astro:hydrate",this.hydrate,{once:!0});return}let c=this.querySelectorAll("astro-slot"),n={},h=this.querySelectorAll("template[data-astro-template]");for(let r of h){let s=r.closest(this.tagName);s!=null&&s.isSameNode(this)&&(n[r.getAttribute("data-astro-template")||"default"]=r.innerHTML,r.remove())}for(let r of c){let s=r.closest(this.tagName);s!=null&&s.isSameNode(this)&&(n[r.getAttribute("name")||"default"]=r.innerHTML)}let p;try{p=this.hasAttribute("props")?m(JSON.parse(this.getAttribute("props"))):{}}catch(r){let s=this.getAttribute("component-url")||"<unknown>",v=this.getAttribute("component-export");throw v&&(s+=` (export ${v})`),console.error(`[hydrate] Error parsing props for component ${s}`,this.getAttribute("props"),r),r}let u;await this.hydrator(this)(this.Component,p,n,{client:this.getAttribute("client")}),this.removeAttribute("ssr"),this.dispatchEvent(new CustomEvent("astro:hydrate"))});d(this,"unmount",()=>{this.isConnected||this.dispatchEvent(new CustomEvent("astro:unmount"))})}disconnectedCallback(){document.removeEventListener("astro:after-swap",this.unmount),document.addEventListener("astro:after-swap",this.unmount,{once:!0})}connectedCallback(){if(!this.hasAttribute("await-children")||document.readyState==="interactive"||document.readyState==="complete")this.childrenConnectedCallback();else{let e=()=>{document.removeEventListener("DOMContentLoaded",e),c.disconnect(),this.childrenConnectedCallback()},c=new MutationObserver(()=>{var n;((n=this.lastChild)==null?void 0:n.nodeType)===Node.COMMENT_NODE&&this.lastChild.nodeValue==="astro:end"&&(this.lastChild.remove(),e())});c.observe(this,{childList:!0}),document.addEventListener("DOMContentLoaded",e)}}async childrenConnectedCallback(){let e=this.getAttribute("before-hydration-url");e&&await import(e),this.start()}async start(){let e=JSON.parse(this.getAttribute("opts")),c=this.getAttribute("client");if(Astro[c]===void 0){window.addEventListener(`astro:${c}`,()=>this.start(),{once:!0});return}try{await Astro[c](async()=>{let n=this.getAttribute("renderer-url"),[h,{default:p}]=await Promise.all([import(this.getAttribute("component-url")),n?import(n):()=>()=>{}]),u=this.getAttribute("component-export")||"default";if(!u.includes("."))this.Component=h[u];else{this.Component=h;for(let f of u.split("."))this.Component=this.Component[f]}return this.hydrator=p,this.hydrate},e,this)}catch(n){console.error(`[astro-island] Error hydrating ${this.getAttribute("component-url")}`,n)}}attributeChangedCallback(){this.hydrate()}}d(y,"observedAttributes",["props"]),customElements.get("astro-island")||customElements.define("astro-island",y)}})();</script><astro-island uid="Z1qSkz9" prefix="r1" component-url="/skills/_astro/SkillsSection.JkW9vxqE.js" component-export="SkillsSection" renderer-url="/skills/_astro/client.Dc9Vh3na.js" props="{&quot;skills&quot;:[1,[[0,{&quot;name&quot;:[0,&quot;agent-framework-azure-ai-py&quot;],&quot;description&quot;:[0,&quot;Build Azure AI Foundry agents using the Microsoft Agent Framework Python SDK (agent-framework-azure-ai). Use when creating persistent agents with AzureAIAgentsProvider, using hosted tools (code interpreter, file search, web search), integrating MCP servers, managing conversation threads, or implementing streaming responses. Covers function tools, structured outputs, and multi-tool agents.&quot;],&quot;lang&quot;:[0,&quot;py&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-python/skills/agent-framework-azure-ai-py&quot;]}],[0,{&quot;name&quot;:[0,&quot;agents-v2-py&quot;],&quot;description&quot;:[0,&quot;Maintain legacy initial-preview Foundry container agents built with Azure AI Projects.\nUse only for pre-refreshed hosted-agent implementations. For current hosted agents with\nResponses or Invocations protocols, use hosted-agents-v2-py.\nTriggers: \&quot;legacy container agent\&quot;, \&quot;initial preview hosted agent\&quot;,\n\&quot;pre-refreshed Foundry Agent\&quot;, \&quot;legacy agent create_version\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;py&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-python/skills/agents-v2-py&quot;]}],[0,{&quot;name&quot;:[0,&quot;applicationinsights-web-ts&quot;],&quot;description&quot;:[0,&quot;Instrument browser/web apps with the Application Insights JavaScript SDK (@microsoft/applicationinsights-web). Use for Real User Monitoring (RUM) — page views, clicks, AJAX/fetch dependencies, exceptions, custom events, and browser-side GenAI agent traces correlated to backend OpenTelemetry traces. Covers SDK Loader Script and npm setup, framework extensions (React, React Native, Angular), Click Analytics, telemetry initializers, and OTel GenAI semantic conventions for agent/tool/model spans emitted from the browser.&quot;],&quot;lang&quot;:[0,&quot;ts&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-typescript/skills/applicationinsights-web-ts&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-ai-agents-persistent-dotnet&quot;],&quot;description&quot;:[0,&quot;Azure AI Agents Persistent SDK for .NET. Low-level SDK for creating and managing AI agents with threads, messages, runs, and tools. Use for agent CRUD, conversation threads, streaming responses, function calling, file search, and code interpreter. Triggers: \&quot;PersistentAgentsClient\&quot;, \&quot;persistent agents\&quot;, \&quot;agent threads\&quot;, \&quot;agent runs\&quot;, \&quot;streaming agents\&quot;, \&quot;function calling agents .NET\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;dotnet&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-dotnet/skills/azure-ai-agents-persistent-dotnet&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-ai-agents-persistent-java&quot;],&quot;description&quot;:[0,&quot;Azure AI Agents Persistent SDK for Java. Low-level SDK for creating and managing AI agents with threads, messages, runs, and tools.\nTriggers: \&quot;PersistentAgentsClient\&quot;, \&quot;persistent agents java\&quot;, \&quot;agent threads java\&quot;, \&quot;agent runs java\&quot;, \&quot;streaming agents java\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;java&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-java/skills/azure-ai-agents-persistent-java&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-ai-anomalydetector-java&quot;],&quot;description&quot;:[0,&quot;Build anomaly detection applications with Azure AI Anomaly Detector SDK for Java. Use when implementing univariate/multivariate anomaly detection, time-series analysis, or AI-powered monitoring.&quot;],&quot;lang&quot;:[0,&quot;java&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-java/skills/azure-ai-anomalydetector-java&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-ai-contentsafety-java&quot;],&quot;description&quot;:[0,&quot;Build content moderation applications with Azure AI Content Safety SDK for Java. Use when implementing text/image analysis, blocklist management, or harm detection for hate, violence, sexual content, and self-harm.&quot;],&quot;lang&quot;:[0,&quot;java&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-java/skills/azure-ai-contentsafety-java&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-ai-contentsafety-py&quot;],&quot;description&quot;:[0,&quot;Azure AI Content Safety SDK for Python. Use for detecting harmful content in text and images with multi-severity classification.\nTriggers: \&quot;azure-ai-contentsafety\&quot;, \&quot;ContentSafetyClient\&quot;, \&quot;content moderation\&quot;, \&quot;harmful content\&quot;, \&quot;text analysis\&quot;, \&quot;image analysis\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;py&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-python/skills/azure-ai-contentsafety-py&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-ai-contentsafety-ts&quot;],&quot;description&quot;:[0,&quot;Analyze text and images for harmful content using Azure AI Content Safety (@azure-rest/ai-content-safety). Use when moderating user-generated content, detecting hate speech, violence, sexual content, or self-harm, or managing custom blocklists.&quot;],&quot;lang&quot;:[0,&quot;ts&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-typescript/skills/azure-ai-contentsafety-ts&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-ai-contentunderstanding-py&quot;],&quot;description&quot;:[0,&quot;Azure AI Content Understanding SDK for Python. Use for multimodal content extraction from documents, images, audio, and video.\nTriggers: \&quot;azure-ai-contentunderstanding\&quot;, \&quot;ContentUnderstandingClient\&quot;, \&quot;multimodal analysis\&quot;, \&quot;document extraction\&quot;, \&quot;video analysis\&quot;, \&quot;audio transcription\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;py&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-python/skills/azure-ai-contentunderstanding-py&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-ai-document-intelligence-dotnet&quot;],&quot;description&quot;:[0,&quot;Azure AI Document Intelligence SDK for .NET. Extract text, tables, and structured data from documents using prebuilt and custom models. Use for invoice processing, receipt extraction, ID document analysis, and custom document models. Triggers: \&quot;Document Intelligence\&quot;, \&quot;DocumentIntelligenceClient\&quot;, \&quot;form recognizer\&quot;, \&quot;invoice extraction\&quot;, \&quot;receipt OCR\&quot;, \&quot;document analysis .NET\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;dotnet&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-dotnet/skills/azure-ai-document-intelligence-dotnet&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-ai-document-intelligence-ts&quot;],&quot;description&quot;:[0,&quot;Extract text, tables, and structured data from documents using Azure Document Intelligence (@azure-rest/ai-document-intelligence). Use when processing invoices, receipts, IDs, forms, or building custom document models.&quot;],&quot;lang&quot;:[0,&quot;ts&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-typescript/skills/azure-ai-document-intelligence-ts&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-ai-formrecognizer-java&quot;],&quot;description&quot;:[0,&quot;Azure AI Document Intelligence SDK for Java (com.azure:azure-ai-documentintelligence).\nUse for extracting text, tables, key-value pairs from documents, receipts, invoices, IDs, or building custom document models.\nTriggers: \&quot;document intelligence java\&quot;, \&quot;form recognizer java\&quot;, \&quot;extract text from PDF java\&quot;, \&quot;OCR document java\&quot;,\n\&quot;analyze invoice receipt java\&quot;, \&quot;custom document model java\&quot;, \&quot;document classification java\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;java&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-java/skills/azure-ai-formrecognizer-java&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-ai-language-conversations-py&quot;],&quot;description&quot;:[0,&quot;Implement Conversational Language Understanding (CLU) using the azure-ai-language-conversations Python SDK. Use when working with ConversationAnalysisClient to analyze conversation intent and entities, building NLP features, or integrating language understanding into applications.&quot;],&quot;lang&quot;:[0,&quot;py&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-python/skills/azure-ai-language-conversations-py&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-ai-ml-py&quot;],&quot;description&quot;:[0,&quot;Azure Machine Learning SDK v2 for Python. Use for ML workspaces, jobs, models, datasets, compute, and pipelines.\nTriggers: \&quot;azure-ai-ml\&quot;, \&quot;MLClient\&quot;, \&quot;workspace\&quot;, \&quot;model registry\&quot;, \&quot;training jobs\&quot;, \&quot;datasets\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;py&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-python/skills/azure-ai-ml-py&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-ai-openai-dotnet&quot;],&quot;description&quot;:[0,&quot;Azure OpenAI SDK for .NET. Client library for Azure OpenAI and OpenAI services. Use for chat completions, embeddings, image generation, audio transcription, and assistants. Triggers: \&quot;Azure OpenAI\&quot;, \&quot;AzureOpenAIClient\&quot;, \&quot;ChatClient\&quot;, \&quot;chat completions .NET\&quot;, \&quot;GPT-4\&quot;, \&quot;embeddings\&quot;, \&quot;DALL-E\&quot;, \&quot;Whisper\&quot;, \&quot;OpenAI .NET\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;dotnet&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-dotnet/skills/azure-ai-openai-dotnet&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-ai-projects-dotnet&quot;],&quot;description&quot;:[0,&quot;Azure AI Projects SDK for .NET. High-level client for Azure AI Foundry projects including agents, connections, datasets, deployments, evaluations, and indexes. Use for AI Foundry project management, versioned agents, and orchestration. Triggers: \&quot;AI Projects\&quot;, \&quot;AIProjectClient\&quot;, \&quot;Foundry project\&quot;, \&quot;versioned agents\&quot;, \&quot;evaluations\&quot;, \&quot;datasets\&quot;, \&quot;connections\&quot;, \&quot;deployments .NET\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;dotnet&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-dotnet/skills/azure-ai-projects-dotnet&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-ai-projects-java&quot;],&quot;description&quot;:[0,&quot;Azure AI Projects SDK for Java. High-level SDK for Azure AI Foundry project management including connections, datasets, indexes, and evaluations.\nTriggers: \&quot;AIProjectClient java\&quot;, \&quot;azure ai projects java\&quot;, \&quot;Foundry project java\&quot;, \&quot;ConnectionsClient\&quot;, \&quot;DatasetsClient\&quot;, \&quot;IndexesClient\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;java&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-java/skills/azure-ai-projects-java&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-ai-projects-py&quot;],&quot;description&quot;:[0,&quot;Build AI applications using the Azure AI Projects Python SDK (azure-ai-projects). Use when working with Foundry project clients, creating versioned agents with PromptAgentDefinition, running evaluations, managing connections/deployments/datasets/indexes, or using OpenAI-compatible clients. This is the high-level Foundry SDK - for low-level agent operations, use azure-ai-agents-python skill.&quot;],&quot;lang&quot;:[0,&quot;py&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-python/skills/azure-ai-projects-py&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-ai-projects-ts&quot;],&quot;description&quot;:[0,&quot;Build AI applications using Azure AI Projects SDK for JavaScript (@azure/ai-projects). Use when working with Foundry project clients, agents, connections, deployments, datasets, indexes, evaluations, or getting OpenAI clients.&quot;],&quot;lang&quot;:[0,&quot;ts&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-typescript/skills/azure-ai-projects-ts&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-ai-textanalytics-py&quot;],&quot;description&quot;:[0,&quot;Azure AI Text Analytics SDK for sentiment analysis, entity recognition, key phrases, language detection, PII, and healthcare NLP. Use for natural language processing on text.\nTriggers: \&quot;text analytics\&quot;, \&quot;sentiment analysis\&quot;, \&quot;entity recognition\&quot;, \&quot;key phrase\&quot;, \&quot;PII detection\&quot;, \&quot;TextAnalyticsClient\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;py&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-python/skills/azure-ai-textanalytics-py&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-ai-transcription-py&quot;],&quot;description&quot;:[0,&quot;Azure AI Transcription SDK for Python. Use for real-time and batch speech-to-text transcription with timestamps and diarization.\nTriggers: \&quot;transcription\&quot;, \&quot;speech to text\&quot;, \&quot;Azure AI Transcription\&quot;, \&quot;TranscriptionClient\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;py&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-python/skills/azure-ai-transcription-py&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-ai-translation-document-py&quot;],&quot;description&quot;:[0,&quot;Azure AI Document Translation SDK for batch translation of documents with format preservation. Use for translating Word, PDF, Excel, PowerPoint, and other document formats at scale.\nTriggers: \&quot;document translation\&quot;, \&quot;batch translation\&quot;, \&quot;translate documents\&quot;, \&quot;DocumentTranslationClient\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;py&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-python/skills/azure-ai-translation-document-py&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-ai-translation-text-py&quot;],&quot;description&quot;:[0,&quot;Azure AI Text Translation SDK for real-time text translation, transliteration, language detection, and dictionary lookup. Use for translating text content in applications.\nTriggers: \&quot;text translation\&quot;, \&quot;translator\&quot;, \&quot;translate text\&quot;, \&quot;transliterate\&quot;, \&quot;TextTranslationClient\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;py&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-python/skills/azure-ai-translation-text-py&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-ai-translation-ts&quot;],&quot;description&quot;:[0,&quot;Build translation applications using Azure Translation SDKs for JavaScript (@azure-rest/ai-translation-text, @azure-rest/ai-translation-document). Use when implementing text translation, transliteration, language detection, or batch document translation.&quot;],&quot;lang&quot;:[0,&quot;ts&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-typescript/skills/azure-ai-translation-ts&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-ai-vision-imageanalysis-java&quot;],&quot;description&quot;:[0,&quot;Build image analysis applications with Azure AI Vision SDK for Java. Use when implementing image captioning, OCR text extraction, object detection, tagging, or smart cropping.&quot;],&quot;lang&quot;:[0,&quot;java&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-java/skills/azure-ai-vision-imageanalysis-java&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-ai-vision-imageanalysis-py&quot;],&quot;description&quot;:[0,&quot;Azure AI Vision Image Analysis SDK for captions, tags, objects, OCR, people detection, and smart cropping. Use for computer vision and image understanding tasks.\nTriggers: \&quot;image analysis\&quot;, \&quot;computer vision\&quot;, \&quot;OCR\&quot;, \&quot;object detection\&quot;, \&quot;ImageAnalysisClient\&quot;, \&quot;image caption\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;py&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-python/skills/azure-ai-vision-imageanalysis-py&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-ai-voicelive-dotnet&quot;],&quot;description&quot;:[0,&quot;Azure AI Voice Live SDK for .NET. Build real-time voice AI applications with bidirectional WebSocket communication. Use for voice assistants, conversational AI, real-time speech-to-speech, and voice-enabled chatbots. Triggers: \&quot;voice live\&quot;, \&quot;real-time voice\&quot;, \&quot;VoiceLiveClient\&quot;, \&quot;VoiceLiveSession\&quot;, \&quot;voice assistant .NET\&quot;, \&quot;bidirectional audio\&quot;, \&quot;speech-to-speech\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;dotnet&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-dotnet/skills/azure-ai-voicelive-dotnet&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-ai-voicelive-java&quot;],&quot;description&quot;:[0,&quot;Azure AI VoiceLive SDK for Java. Real-time bidirectional voice conversations with AI assistants using WebSocket.\nTriggers: \&quot;VoiceLiveClient java\&quot;, \&quot;voice assistant java\&quot;, \&quot;real-time voice java\&quot;, \&quot;audio streaming java\&quot;, \&quot;voice activity detection java\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;java&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-java/skills/azure-ai-voicelive-java&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-ai-voicelive-py&quot;],&quot;description&quot;:[0,&quot;Build real-time voice AI applications using Azure AI Voice Live SDK (azure-ai-voicelive). Use this skill when creating Python applications that need real-time bidirectional audio communication with Azure AI, including voice assistants, voice-enabled chatbots, real-time speech-to-speech translation, voice-driven avatars, or any WebSocket-based audio streaming with AI models. Supports Server VAD (Voice Activity Detection), turn-based conversation, function calling, MCP tools, avatar integration, and transcription.&quot;],&quot;lang&quot;:[0,&quot;py&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-python/skills/azure-ai-voicelive-py&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-ai-voicelive-ts&quot;],&quot;description&quot;:[0,&quot;Azure AI Voice Live SDK for JavaScript/TypeScript. Build real-time voice AI applications with bidirectional WebSocket communication. Use for voice assistants, conversational AI, real-time speech-to-speech, and voice-enabled chatbots in Node.js or browser environments. Triggers: \&quot;voice live\&quot;, \&quot;real-time voice\&quot;, \&quot;VoiceLiveClient\&quot;, \&quot;VoiceLiveSession\&quot;, \&quot;voice assistant TypeScript\&quot;, \&quot;bidirectional audio\&quot;, \&quot;speech-to-speech JavaScript\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;ts&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-typescript/skills/azure-ai-voicelive-ts&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-appconfiguration-java&quot;],&quot;description&quot;:[0,&quot;Azure App Configuration SDK for Java. Centralized application configuration management with key-value settings, feature flags, and snapshots.\nTriggers: \&quot;ConfigurationClient java\&quot;, \&quot;app configuration java\&quot;, \&quot;feature flag java\&quot;, \&quot;configuration setting java\&quot;, \&quot;azure config java\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;java&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-java/skills/azure-appconfiguration-java&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-appconfiguration-py&quot;],&quot;description&quot;:[0,&quot;Azure App Configuration SDK for Python. Use for centralized configuration management, feature flags, and dynamic settings.\nTriggers: \&quot;azure-appconfiguration\&quot;, \&quot;AzureAppConfigurationClient\&quot;, \&quot;feature flags\&quot;, \&quot;configuration\&quot;, \&quot;key-value settings\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;py&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-python/skills/azure-appconfiguration-py&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-appconfiguration-ts&quot;],&quot;description&quot;:[0,&quot;Build applications using Azure App Configuration SDK for JavaScript (@azure/app-configuration). Use when working with configuration settings, feature flags, Key Vault references, dynamic refresh, or centralized configuration management.&quot;],&quot;lang&quot;:[0,&quot;ts&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-typescript/skills/azure-appconfiguration-ts&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-communication-callautomation-java&quot;],&quot;description&quot;:[0,&quot;Build call automation workflows with Azure Communication Services Call Automation Java SDK. Use when implementing IVR systems, call routing, call recording, DTMF recognition, text-to-speech, or AI-powered call flows.&quot;],&quot;lang&quot;:[0,&quot;java&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-java/skills/azure-communication-callautomation-java&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-communication-callingserver-java&quot;],&quot;description&quot;:[0,&quot;Azure Communication Services CallingServer (legacy) Java SDK. Note - This SDK is deprecated. Use azure-communication-callautomation instead for new projects. Only use this skill when maintaining legacy code.&quot;],&quot;lang&quot;:[0,&quot;java&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-java/skills/azure-communication-callingserver-java&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-communication-chat-java&quot;],&quot;description&quot;:[0,&quot;Build real-time chat applications with Azure Communication Services Chat Java SDK. Use when implementing chat threads, messaging, participants, read receipts, typing notifications, or real-time chat features.&quot;],&quot;lang&quot;:[0,&quot;java&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-java/skills/azure-communication-chat-java&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-communication-common-java&quot;],&quot;description&quot;:[0,&quot;Azure Communication Services common utilities for Java. Use when working with CommunicationTokenCredential, user identifiers, token refresh, or shared authentication across ACS services.&quot;],&quot;lang&quot;:[0,&quot;java&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-java/skills/azure-communication-common-java&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-communication-sms-java&quot;],&quot;description&quot;:[0,&quot;Send SMS messages with Azure Communication Services SMS Java SDK. Use when implementing SMS notifications, alerts, OTP delivery, bulk messaging, or delivery reports.&quot;],&quot;lang&quot;:[0,&quot;java&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-java/skills/azure-communication-sms-java&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-compute-batch-java&quot;],&quot;description&quot;:[0,&quot;Azure Batch SDK for Java. Run large-scale parallel and HPC batch jobs with pools, jobs, tasks, and compute nodes.\nTriggers: \&quot;BatchClient java\&quot;, \&quot;azure batch java\&quot;, \&quot;batch pool java\&quot;, \&quot;batch job java\&quot;, \&quot;HPC java\&quot;, \&quot;parallel computing java\&quot;.&quot;],&quot;lang&quot;:[0,&quot;java&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-java/skills/azure-compute-batch-java&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-containerregistry-py&quot;],&quot;description&quot;:[0,&quot;Azure Container Registry SDK for Python. Use for managing container images, artifacts, and repositories.\nTriggers: \&quot;azure-containerregistry\&quot;, \&quot;ContainerRegistryClient\&quot;, \&quot;container images\&quot;, \&quot;docker registry\&quot;, \&quot;ACR\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;py&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-python/skills/azure-containerregistry-py&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-cosmos-db-py&quot;],&quot;description&quot;:[0,&quot;Build Azure Cosmos DB NoSQL services with Python/FastAPI following production-grade patterns. Use when implementing database client setup with dual auth (DefaultAzureCredential + emulator), service layer classes with CRUD operations, partition key strategies, parameterized queries, or TDD patterns for Cosmos. Triggers on phrases like \&quot;Cosmos DB\&quot;, \&quot;NoSQL database\&quot;, \&quot;document store\&quot;, \&quot;add persistence\&quot;, \&quot;database service layer\&quot;, or \&quot;Python Cosmos SDK\&quot;.&quot;],&quot;lang&quot;:[0,&quot;py&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-python/skills/azure-cosmos-db-py&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-cosmos-java&quot;],&quot;description&quot;:[0,&quot;Azure Cosmos DB SDK for Java. NoSQL database operations with global distribution, multi-model support, and reactive patterns.\nTriggers: \&quot;CosmosClient java\&quot;, \&quot;CosmosAsyncClient\&quot;, \&quot;cosmos database java\&quot;, \&quot;cosmosdb java\&quot;, \&quot;document database java\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;java&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-java/skills/azure-cosmos-java&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-cosmos-py&quot;],&quot;description&quot;:[0,&quot;Azure Cosmos DB SDK for Python (NoSQL API). Use for document CRUD, queries, containers, and globally distributed data.\nTriggers: \&quot;cosmos db\&quot;, \&quot;CosmosClient\&quot;, \&quot;container\&quot;, \&quot;document\&quot;, \&quot;NoSQL\&quot;, \&quot;partition key\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;py&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-python/skills/azure-cosmos-py&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-cosmos-rust&quot;],&quot;description&quot;:[0,&quot;Azure Cosmos DB SDK for Rust (NoSQL API). Use for document CRUD, queries, containers, and globally distributed data.\nTriggers: \&quot;cosmos db rust\&quot;, \&quot;CosmosClient rust\&quot;, \&quot;container\&quot;, \&quot;document rust\&quot;, \&quot;NoSQL rust\&quot;, \&quot;partition key\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;rust&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-rust/skills/azure-cosmos-rust&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-cosmos-ts&quot;],&quot;description&quot;:[0,&quot;Azure Cosmos DB JavaScript/TypeScript SDK (@azure/cosmos) for data plane operations. Use for CRUD operations on documents, queries, bulk operations, and container management. Triggers: \&quot;Cosmos DB\&quot;, \&quot;@azure/cosmos\&quot;, \&quot;CosmosClient\&quot;, \&quot;document CRUD\&quot;, \&quot;NoSQL queries\&quot;, \&quot;bulk operations\&quot;, \&quot;partition key\&quot;, \&quot;container.items\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;ts&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-typescript/skills/azure-cosmos-ts&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-data-tables-java&quot;],&quot;description&quot;:[0,&quot;Build table storage applications with Azure Tables SDK for Java. Use when working with Azure Table Storage or Cosmos DB Table API for NoSQL key-value data, schemaless storage, or structured data at scale.&quot;],&quot;lang&quot;:[0,&quot;java&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-java/skills/azure-data-tables-java&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-data-tables-py&quot;],&quot;description&quot;:[0,&quot;Azure Tables SDK for Python (Storage and Cosmos DB). Use for NoSQL key-value storage, entity CRUD, and batch operations.\nTriggers: \&quot;table storage\&quot;, \&quot;TableServiceClient\&quot;, \&quot;TableClient\&quot;, \&quot;entities\&quot;, \&quot;PartitionKey\&quot;, \&quot;RowKey\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;py&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-python/skills/azure-data-tables-py&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-eventgrid-dotnet&quot;],&quot;description&quot;:[0,&quot;Azure Event Grid SDK for .NET. Client library for publishing and consuming events with Azure Event Grid. Use for event-driven architectures, pub/sub messaging, CloudEvents, and EventGridEvents. Triggers: \&quot;Event Grid\&quot;, \&quot;EventGridPublisherClient\&quot;, \&quot;CloudEvent\&quot;, \&quot;EventGridEvent\&quot;, \&quot;publish events .NET\&quot;, \&quot;event-driven\&quot;, \&quot;pub/sub\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;dotnet&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-dotnet/skills/azure-eventgrid-dotnet&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-eventgrid-java&quot;],&quot;description&quot;:[0,&quot;Build event-driven applications with Azure Event Grid SDK for Java. Use when publishing events, implementing pub/sub patterns, or integrating with Azure services via events.&quot;],&quot;lang&quot;:[0,&quot;java&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-java/skills/azure-eventgrid-java&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-eventgrid-py&quot;],&quot;description&quot;:[0,&quot;Azure Event Grid SDK for Python. Use for publishing events, handling CloudEvents, and event-driven architectures.\nTriggers: \&quot;event grid\&quot;, \&quot;EventGridPublisherClient\&quot;, \&quot;CloudEvent\&quot;, \&quot;EventGridEvent\&quot;, \&quot;publish events\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;py&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-python/skills/azure-eventgrid-py&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-eventhub-dotnet&quot;],&quot;description&quot;:[0,&quot;Azure Event Hubs SDK for .NET. Use for high-throughput event streaming: sending events (EventHubProducerClient, EventHubBufferedProducerClient), receiving events (EventProcessorClient with checkpointing), partition management, and real-time data ingestion. Triggers: \&quot;Event Hubs\&quot;, \&quot;event streaming\&quot;, \&quot;EventHubProducerClient\&quot;, \&quot;EventProcessorClient\&quot;, \&quot;send events\&quot;, \&quot;receive events\&quot;, \&quot;checkpointing\&quot;, \&quot;partition\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;dotnet&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-dotnet/skills/azure-eventhub-dotnet&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-eventhub-java&quot;],&quot;description&quot;:[0,&quot;Build real-time streaming applications with Azure Event Hubs SDK for Java. Use when implementing event streaming, high-throughput data ingestion, or building event-driven architectures.&quot;],&quot;lang&quot;:[0,&quot;java&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-java/skills/azure-eventhub-java&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-eventhub-py&quot;],&quot;description&quot;:[0,&quot;Azure Event Hubs SDK for Python streaming. Use for high-throughput event ingestion, producers, consumers, and checkpointing.\nTriggers: \&quot;event hubs\&quot;, \&quot;EventHubProducerClient\&quot;, \&quot;EventHubConsumerClient\&quot;, \&quot;streaming\&quot;, \&quot;partitions\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;py&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-python/skills/azure-eventhub-py&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-eventhub-rust&quot;],&quot;description&quot;:[0,&quot;Azure Event Hubs SDK for Rust. Use for sending and receiving events, streaming data ingestion.\nTriggers: \&quot;event hubs rust\&quot;, \&quot;ProducerClient rust\&quot;, \&quot;ConsumerClient rust\&quot;, \&quot;send event rust\&quot;, \&quot;streaming rust\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;rust&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-rust/skills/azure-eventhub-rust&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-eventhub-ts&quot;],&quot;description&quot;:[0,&quot;Build event streaming applications using Azure Event Hubs SDK for JavaScript (@azure/event-hubs). Use when implementing high-throughput event ingestion, real-time analytics, IoT telemetry, or event-driven architectures with partitioned consumers.&quot;],&quot;lang&quot;:[0,&quot;ts&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-typescript/skills/azure-eventhub-ts&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-identity-dotnet&quot;],&quot;description&quot;:[0,&quot;Azure Identity library for .NET. Authentication library for Azure SDK clients using Microsoft Entra ID. Use for DefaultAzureCredential, managed identity, service principals, and developer credentials. Triggers: \&quot;Azure Identity\&quot;, \&quot;DefaultAzureCredential\&quot;, \&quot;ManagedIdentityCredential\&quot;, \&quot;ClientSecretCredential\&quot;, \&quot;authentication .NET\&quot;, \&quot;Azure auth\&quot;, \&quot;credential chain\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;dotnet&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-dotnet/skills/azure-identity-dotnet&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-identity-java&quot;],&quot;description&quot;:[0,&quot;Azure Identity library for Java authentication with Azure services. Use when implementing DefaultAzureCredential, managed identity, service principal, or any Azure authentication pattern in Java applications.&quot;],&quot;lang&quot;:[0,&quot;java&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-java/skills/azure-identity-java&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-identity-py&quot;],&quot;description&quot;:[0,&quot;Azure Identity SDK for Python authentication with Microsoft Entra ID. Use for DefaultAzureCredential, managed identity, service principals, and token caching.\nTriggers: \&quot;azure-identity\&quot;, \&quot;DefaultAzureCredential\&quot;, \&quot;authentication\&quot;, \&quot;managed identity\&quot;, \&quot;service principal\&quot;, \&quot;credential\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;py&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-python/skills/azure-identity-py&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-identity-rust&quot;],&quot;description&quot;:[0,&quot;Azure Identity SDK for Rust authentication. Use for DeveloperToolsCredential, ManagedIdentityCredential, ClientSecretCredential, and token-based authentication.\nTriggers: \&quot;azure-identity\&quot;, \&quot;DeveloperToolsCredential\&quot;, \&quot;authentication rust\&quot;, \&quot;managed identity rust\&quot;, \&quot;credential rust\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;rust&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-rust/skills/azure-identity-rust&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-identity-ts&quot;],&quot;description&quot;:[0,&quot;Authenticate to Azure services using Azure Identity library for JavaScript (@azure/identity). Use when configuring authentication with DefaultAzureCredential, managed identity, service principals, or interactive browser login.&quot;],&quot;lang&quot;:[0,&quot;ts&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-typescript/skills/azure-identity-ts&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-keyvault-certificates-rust&quot;],&quot;description&quot;:[0,&quot;Azure Key Vault Certificates SDK for Rust. Use for creating, importing, and managing certificates.\nTriggers: \&quot;keyvault certificates rust\&quot;, \&quot;CertificateClient rust\&quot;, \&quot;create certificate rust\&quot;, \&quot;import certificate rust\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;rust&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-rust/skills/azure-keyvault-certificates-rust&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-keyvault-keys-rust&quot;],&quot;description&quot;:[0,&quot;Azure Key Vault Keys SDK for Rust. Use for creating, managing, and using cryptographic keys.\nTriggers: \&quot;keyvault keys rust\&quot;, \&quot;KeyClient rust\&quot;, \&quot;create key rust\&quot;, \&quot;encrypt rust\&quot;, \&quot;sign rust\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;rust&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-rust/skills/azure-keyvault-keys-rust&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-keyvault-keys-ts&quot;],&quot;description&quot;:[0,&quot;Manage cryptographic keys using Azure Key Vault Keys SDK for JavaScript (@azure/keyvault-keys). Use when creating, encrypting/decrypting, signing, or rotating keys.&quot;],&quot;lang&quot;:[0,&quot;ts&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-typescript/skills/azure-keyvault-keys-ts&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-keyvault-py&quot;],&quot;description&quot;:[0,&quot;Azure Key Vault SDK for Python. Use for secrets, keys, and certificates management with secure storage.\nTriggers: \&quot;key vault\&quot;, \&quot;SecretClient\&quot;, \&quot;KeyClient\&quot;, \&quot;CertificateClient\&quot;, \&quot;secrets\&quot;, \&quot;encryption keys\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;py&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-python/skills/azure-keyvault-py&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-keyvault-secrets-rust&quot;],&quot;description&quot;:[0,&quot;Azure Key Vault Secrets SDK for Rust. Use for storing and retrieving secrets, passwords, and API keys.\nTriggers: \&quot;keyvault secrets rust\&quot;, \&quot;SecretClient rust\&quot;, \&quot;get secret rust\&quot;, \&quot;set secret rust\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;rust&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-rust/skills/azure-keyvault-secrets-rust&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-keyvault-secrets-ts&quot;],&quot;description&quot;:[0,&quot;Manage secrets using Azure Key Vault Secrets SDK for JavaScript (@azure/keyvault-secrets). Use when storing and retrieving application secrets or configuration values.&quot;],&quot;lang&quot;:[0,&quot;ts&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-typescript/skills/azure-keyvault-secrets-ts&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-maps-search-dotnet&quot;],&quot;description&quot;:[0,&quot;Azure Maps SDK for .NET. Location-based services including geocoding, routing, rendering, geolocation, and weather. Use for address search, directions, map tiles, IP geolocation, and weather data. Triggers: \&quot;Azure Maps\&quot;, \&quot;MapsSearchClient\&quot;, \&quot;MapsRoutingClient\&quot;, \&quot;MapsRenderingClient\&quot;, \&quot;geocoding .NET\&quot;, \&quot;route directions\&quot;, \&quot;map tiles\&quot;, \&quot;geolocation\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;dotnet&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-dotnet/skills/azure-maps-search-dotnet&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-messaging-webpubsub-java&quot;],&quot;description&quot;:[0,&quot;Build real-time web applications with Azure Web PubSub SDK for Java. Use when implementing WebSocket-based messaging, live updates, chat applications, or server-to-client push notifications.&quot;],&quot;lang&quot;:[0,&quot;java&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-java/skills/azure-messaging-webpubsub-java&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-messaging-webpubsubservice-py&quot;],&quot;description&quot;:[0,&quot;Azure Web PubSub Service SDK for Python. Use for real-time messaging, WebSocket connections, and pub/sub patterns.\nTriggers: \&quot;azure-messaging-webpubsubservice\&quot;, \&quot;WebPubSubServiceClient\&quot;, \&quot;real-time\&quot;, \&quot;WebSocket\&quot;, \&quot;pub/sub\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;py&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-python/skills/azure-messaging-webpubsubservice-py&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-mgmt-apicenter-dotnet&quot;],&quot;description&quot;:[0,&quot;Azure API Center SDK for .NET. Centralized API inventory management with governance, versioning, and discovery. Use for creating API services, workspaces, APIs, versions, definitions, environments, deployments, and metadata schemas. Triggers: \&quot;API Center\&quot;, \&quot;ApiCenterService\&quot;, \&quot;ApiCenterWorkspace\&quot;, \&quot;ApiCenterApi\&quot;, \&quot;API inventory\&quot;, \&quot;API governance\&quot;, \&quot;API versioning\&quot;, \&quot;API catalog\&quot;, \&quot;API discovery\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;dotnet&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-dotnet/skills/azure-mgmt-apicenter-dotnet&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-mgmt-apicenter-py&quot;],&quot;description&quot;:[0,&quot;Azure API Center Management SDK for Python. Use for managing API inventory, metadata, and governance across your organization.\nTriggers: \&quot;azure-mgmt-apicenter\&quot;, \&quot;ApiCenterMgmtClient\&quot;, \&quot;API Center\&quot;, \&quot;API inventory\&quot;, \&quot;API governance\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;py&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-python/skills/azure-mgmt-apicenter-py&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-mgmt-apimanagement-dotnet&quot;],&quot;description&quot;:[0,&quot;Azure Resource Manager SDK for API Management in .NET. Use for MANAGEMENT PLANE operations: creating/managing APIM services, APIs, products, subscriptions, policies, users, groups, gateways, and backends via Azure Resource Manager. Triggers: \&quot;API Management\&quot;, \&quot;APIM service\&quot;, \&quot;create APIM\&quot;, \&quot;manage APIs\&quot;, \&quot;ApiManagementServiceResource\&quot;, \&quot;API policies\&quot;, \&quot;APIM products\&quot;, \&quot;APIM subscriptions\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;dotnet&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-dotnet/skills/azure-mgmt-apimanagement-dotnet&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-mgmt-apimanagement-py&quot;],&quot;description&quot;:[0,&quot;Azure API Management SDK for Python. Use for managing APIM services, APIs, products, subscriptions, and policies.\nTriggers: \&quot;azure-mgmt-apimanagement\&quot;, \&quot;ApiManagementClient\&quot;, \&quot;APIM\&quot;, \&quot;API gateway\&quot;, \&quot;API Management\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;py&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-python/skills/azure-mgmt-apimanagement-py&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-mgmt-applicationinsights-dotnet&quot;],&quot;description&quot;:[0,&quot;Azure Application Insights SDK for .NET. Application performance monitoring and observability resource management. Use for creating Application Insights components, web tests, workbooks, analytics items, and API keys. Triggers: \&quot;Application Insights\&quot;, \&quot;ApplicationInsights\&quot;, \&quot;App Insights\&quot;, \&quot;APM\&quot;, \&quot;application monitoring\&quot;, \&quot;web tests\&quot;, \&quot;availability tests\&quot;, \&quot;workbooks\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;dotnet&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-dotnet/skills/azure-mgmt-applicationinsights-dotnet&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-mgmt-arizeaiobservabilityeval-dotnet&quot;],&quot;description&quot;:[0,&quot;Azure Resource Manager SDK for Arize AI Observability and Evaluation (.NET). Use when managing Arize AI organizations \non Azure via Azure Marketplace, creating/updating/deleting Arize resources, or integrating Arize ML observability \ninto .NET applications. Triggers: \&quot;Arize AI\&quot;, \&quot;ML observability\&quot;, \&quot;ArizeAIObservabilityEval\&quot;, \&quot;Arize organization\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;dotnet&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-dotnet/skills/azure-mgmt-arizeaiobservabilityeval-dotnet&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-mgmt-botservice-dotnet&quot;],&quot;description&quot;:[0,&quot;Azure Resource Manager SDK for Bot Service in .NET. Management plane operations for creating and managing Azure Bot resources, channels (Teams, DirectLine, Slack), and connection settings. Triggers: \&quot;Bot Service\&quot;, \&quot;BotResource\&quot;, \&quot;Azure Bot\&quot;, \&quot;DirectLine channel\&quot;, \&quot;Teams channel\&quot;, \&quot;bot management .NET\&quot;, \&quot;create bot\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;dotnet&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-dotnet/skills/azure-mgmt-botservice-dotnet&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-mgmt-botservice-py&quot;],&quot;description&quot;:[0,&quot;Azure Bot Service Management SDK for Python. Use for creating, managing, and configuring Azure Bot Service resources.\nTriggers: \&quot;azure-mgmt-botservice\&quot;, \&quot;AzureBotService\&quot;, \&quot;bot management\&quot;, \&quot;conversational AI\&quot;, \&quot;bot channels\&quot;.&quot;],&quot;lang&quot;:[0,&quot;py&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-python/skills/azure-mgmt-botservice-py&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-mgmt-fabric-dotnet&quot;],&quot;description&quot;:[0,&quot;Azure Resource Manager SDK for Fabric in .NET. Use for MANAGEMENT PLANE operations: provisioning, scaling, suspending/resuming Microsoft Fabric capacities, checking name availability, and listing SKUs via Azure Resource Manager. Triggers: \&quot;Fabric capacity\&quot;, \&quot;create capacity\&quot;, \&quot;suspend capacity\&quot;, \&quot;resume capacity\&quot;, \&quot;Fabric SKU\&quot;, \&quot;provision Fabric\&quot;, \&quot;ARM Fabric\&quot;, \&quot;FabricCapacityResource\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;dotnet&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-dotnet/skills/azure-mgmt-fabric-dotnet&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-mgmt-fabric-py&quot;],&quot;description&quot;:[0,&quot;Azure Fabric Management SDK for Python. Use for managing Microsoft Fabric capacities and resources.\nTriggers: \&quot;azure-mgmt-fabric\&quot;, \&quot;FabricMgmtClient\&quot;, \&quot;Fabric capacity\&quot;, \&quot;Microsoft Fabric\&quot;, \&quot;Power BI capacity\&quot;.&quot;],&quot;lang&quot;:[0,&quot;py&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-python/skills/azure-mgmt-fabric-py&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-mgmt-mongodbatlas-dotnet&quot;],&quot;description&quot;:[0,&quot;Manage MongoDB Atlas Organizations as Azure ARM resources using Azure.ResourceManager.MongoDBAtlas SDK. Use when creating, updating, listing, or deleting MongoDB Atlas organizations through Azure Marketplace integration. This SDK manages the Azure-side organization resource, not Atlas clusters/databases directly.&quot;],&quot;lang&quot;:[0,&quot;dotnet&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-dotnet/skills/azure-mgmt-mongodbatlas-dotnet&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-mgmt-weightsandbiases-dotnet&quot;],&quot;description&quot;:[0,&quot;Azure Weights &amp; Biases SDK for .NET. ML experiment tracking and model management via Azure Marketplace. Use for creating W&amp;B instances, managing SSO, marketplace integration, and ML observability. Triggers: \&quot;Weights and Biases\&quot;, \&quot;W&amp;B\&quot;, \&quot;WeightsAndBiases\&quot;, \&quot;ML experiment tracking\&quot;, \&quot;model registry\&quot;, \&quot;experiment management\&quot;, \&quot;wandb\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;dotnet&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-dotnet/skills/azure-mgmt-weightsandbiases-dotnet&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-microsoft-playwright-testing-ts&quot;],&quot;description&quot;:[0,&quot;Run Playwright tests at scale using Azure Playwright Workspaces (formerly Microsoft Playwright Testing). Use when scaling browser tests across cloud-hosted browsers, integrating with CI/CD pipelines, or publishing test results to the Azure portal.&quot;],&quot;lang&quot;:[0,&quot;ts&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-typescript/skills/azure-microsoft-playwright-testing-ts&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-monitor-ingestion-java&quot;],&quot;description&quot;:[0,&quot;Azure Monitor Ingestion SDK for Java. Send custom logs to Azure Monitor via Data Collection Rules (DCR) and Data Collection Endpoints (DCE).\nTriggers: \&quot;LogsIngestionClient java\&quot;, \&quot;azure monitor ingestion java\&quot;, \&quot;custom logs java\&quot;, \&quot;DCR java\&quot;, \&quot;data collection rule java\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;java&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-java/skills/azure-monitor-ingestion-java&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-monitor-ingestion-py&quot;],&quot;description&quot;:[0,&quot;Azure Monitor Ingestion SDK for Python. Use for sending custom logs to Log Analytics workspace via Logs Ingestion API.\nTriggers: \&quot;azure-monitor-ingestion\&quot;, \&quot;LogsIngestionClient\&quot;, \&quot;custom logs\&quot;, \&quot;DCR\&quot;, \&quot;data collection rule\&quot;, \&quot;Log Analytics\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;py&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-python/skills/azure-monitor-ingestion-py&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-monitor-opentelemetry-exporter-java&quot;],&quot;description&quot;:[0,&quot;Azure Monitor OpenTelemetry Exporter for Java. Export OpenTelemetry traces, metrics, and logs to Azure Monitor/Application Insights.\nTriggers: \&quot;AzureMonitorExporter java\&quot;, \&quot;opentelemetry azure java\&quot;, \&quot;application insights java otel\&quot;, \&quot;azure monitor tracing java\&quot;.\nNote: This package is DEPRECATED. Migrate to azure-monitor-opentelemetry-autoconfigure.\n&quot;],&quot;lang&quot;:[0,&quot;java&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-java/skills/azure-monitor-opentelemetry-exporter-java&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-monitor-opentelemetry-exporter-py&quot;],&quot;description&quot;:[0,&quot;Azure Monitor OpenTelemetry Exporter for Python. Use for low-level OpenTelemetry export to Application Insights.\nTriggers: \&quot;azure-monitor-opentelemetry-exporter\&quot;, \&quot;AzureMonitorTraceExporter\&quot;, \&quot;AzureMonitorMetricExporter\&quot;, \&quot;AzureMonitorLogExporter\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;py&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-python/skills/azure-monitor-opentelemetry-exporter-py&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-monitor-opentelemetry-py&quot;],&quot;description&quot;:[0,&quot;Azure Monitor OpenTelemetry Distro for Python. Use for one-line Application Insights setup with auto-instrumentation.\nTriggers: \&quot;azure-monitor-opentelemetry\&quot;, \&quot;configure_azure_monitor\&quot;, \&quot;Application Insights\&quot;, \&quot;OpenTelemetry distro\&quot;, \&quot;auto-instrumentation\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;py&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-python/skills/azure-monitor-opentelemetry-py&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-monitor-opentelemetry-ts&quot;],&quot;description&quot;:[0,&quot;Instrument applications with Azure Monitor and OpenTelemetry for JavaScript (@azure/monitor-opentelemetry). Use when adding distributed tracing, metrics, and logs to Node.js applications with Application Insights.&quot;],&quot;lang&quot;:[0,&quot;ts&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-typescript/skills/azure-monitor-opentelemetry-ts&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-monitor-query-java&quot;],&quot;description&quot;:[0,&quot;Azure Monitor Query SDK for Java. Execute Kusto queries against Log Analytics workspaces and query metrics from Azure resources.\nTriggers: \&quot;LogsQueryClient java\&quot;, \&quot;MetricsQueryClient java\&quot;, \&quot;kusto query java\&quot;, \&quot;log analytics java\&quot;, \&quot;azure monitor query java\&quot;.\nNote: This package is deprecated. Migrate to azure-monitor-query-logs and azure-monitor-query-metrics.\n&quot;],&quot;lang&quot;:[0,&quot;java&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-java/skills/azure-monitor-query-java&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-monitor-query-py&quot;],&quot;description&quot;:[0,&quot;Azure Monitor Query SDK for Python. Use for querying Log Analytics workspaces and Azure Monitor metrics.\nTriggers: \&quot;azure-monitor-query\&quot;, \&quot;LogsQueryClient\&quot;, \&quot;MetricsQueryClient\&quot;, \&quot;Log Analytics\&quot;, \&quot;Kusto queries\&quot;, \&quot;Azure metrics\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;py&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-python/skills/azure-monitor-query-py&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-postgres-ts&quot;],&quot;description&quot;:[0,&quot;Connect to Azure Database for PostgreSQL Flexible Server from Node.js/TypeScript using the pg (node-postgres) package. Use for PostgreSQL queries, connection pooling, transactions, and Microsoft Entra ID (passwordless) authentication. Triggers: \&quot;PostgreSQL\&quot;, \&quot;postgres\&quot;, \&quot;pg client\&quot;, \&quot;node-postgres\&quot;, \&quot;Azure PostgreSQL connection\&quot;, \&quot;PostgreSQL TypeScript\&quot;, \&quot;pg Pool\&quot;, \&quot;passwordless postgres\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;ts&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-typescript/skills/azure-postgres-ts&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-resource-manager-cosmosdb-dotnet&quot;],&quot;description&quot;:[0,&quot;Azure Resource Manager SDK for Cosmos DB in .NET. Use for MANAGEMENT PLANE operations: creating/managing Cosmos DB accounts, databases, containers, throughput settings, and RBAC via Azure Resource Manager. NOT for data plane operations (CRUD on documents) - use Microsoft.Azure.Cosmos for that. Triggers: \&quot;Cosmos DB account\&quot;, \&quot;create Cosmos account\&quot;, \&quot;manage Cosmos resources\&quot;, \&quot;ARM Cosmos\&quot;, \&quot;CosmosDBAccountResource\&quot;, \&quot;provision Cosmos DB\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;dotnet&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-dotnet/skills/azure-resource-manager-cosmosdb-dotnet&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-resource-manager-durabletask-dotnet&quot;],&quot;description&quot;:[0,&quot;Azure Resource Manager SDK for Durable Task Scheduler in .NET. Use for MANAGEMENT PLANE operations: creating/managing Durable Task Schedulers, Task Hubs, and retention policies via Azure Resource Manager. Triggers: \&quot;Durable Task Scheduler\&quot;, \&quot;create scheduler\&quot;, \&quot;task hub\&quot;, \&quot;DurableTaskSchedulerResource\&quot;, \&quot;provision Durable Task\&quot;, \&quot;orchestration scheduler\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;dotnet&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-dotnet/skills/azure-resource-manager-durabletask-dotnet&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-resource-manager-mysql-dotnet&quot;],&quot;description&quot;:[0,&quot;Azure MySQL Flexible Server SDK for .NET. Database management for MySQL Flexible Server deployments. Use for creating servers, databases, firewall rules, configurations, backups, and high availability. Triggers: \&quot;MySQL\&quot;, \&quot;MySqlFlexibleServer\&quot;, \&quot;MySQL Flexible Server\&quot;, \&quot;Azure Database for MySQL\&quot;, \&quot;MySQL database management\&quot;, \&quot;MySQL firewall\&quot;, \&quot;MySQL backup\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;dotnet&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-dotnet/skills/azure-resource-manager-mysql-dotnet&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-resource-manager-playwright-dotnet&quot;],&quot;description&quot;:[0,&quot;Azure Resource Manager SDK for Microsoft Playwright Testing in .NET. Use for MANAGEMENT PLANE operations: creating/managing Playwright Testing workspaces, checking name availability, and managing workspace quotas via Azure Resource Manager. NOT for running Playwright tests - use Azure.Developer.MicrosoftPlaywrightTesting.NUnit for that. Triggers: \&quot;Playwright workspace\&quot;, \&quot;create Playwright Testing workspace\&quot;, \&quot;manage Playwright resources\&quot;, \&quot;ARM Playwright\&quot;, \&quot;PlaywrightWorkspaceResource\&quot;, \&quot;provision Playwright Testing\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;dotnet&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-dotnet/skills/azure-resource-manager-playwright-dotnet&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-resource-manager-postgresql-dotnet&quot;],&quot;description&quot;:[0,&quot;Azure PostgreSQL Flexible Server SDK for .NET. Database management for PostgreSQL Flexible Server deployments. Use for creating servers, databases, firewall rules, configurations, backups, and high availability. Triggers: \&quot;PostgreSQL\&quot;, \&quot;PostgreSqlFlexibleServer\&quot;, \&quot;PostgreSQL Flexible Server\&quot;, \&quot;Azure Database for PostgreSQL\&quot;, \&quot;PostgreSQL database management\&quot;, \&quot;PostgreSQL firewall\&quot;, \&quot;PostgreSQL backup\&quot;, \&quot;Postgres\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;dotnet&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-dotnet/skills/azure-resource-manager-postgresql-dotnet&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-resource-manager-redis-dotnet&quot;],&quot;description&quot;:[0,&quot;Azure Resource Manager SDK for Redis in .NET. Use for MANAGEMENT PLANE operations: creating/managing Azure Cache for Redis instances, firewall rules, access keys, patch schedules, linked servers (geo-replication), and private endpoints via Azure Resource Manager. NOT for data plane operations (get/set keys, pub/sub) - use StackExchange.Redis for that. Triggers: \&quot;Redis cache\&quot;, \&quot;create Redis\&quot;, \&quot;manage Redis\&quot;, \&quot;ARM Redis\&quot;, \&quot;RedisResource\&quot;, \&quot;provision Redis\&quot;, \&quot;Azure Cache for Redis\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;dotnet&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-dotnet/skills/azure-resource-manager-redis-dotnet&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-resource-manager-sql-dotnet&quot;],&quot;description&quot;:[0,&quot;Azure Resource Manager SDK for Azure SQL in .NET. Use for MANAGEMENT PLANE operations: creating/managing SQL servers, databases, elastic pools, firewall rules, and failover groups via Azure Resource Manager. NOT for data plane operations (executing queries) - use Microsoft.Data.SqlClient for that. Triggers: \&quot;SQL server\&quot;, \&quot;create SQL database\&quot;, \&quot;manage SQL resources\&quot;, \&quot;ARM SQL\&quot;, \&quot;SqlServerResource\&quot;, \&quot;provision Azure SQL\&quot;, \&quot;elastic pool\&quot;, \&quot;firewall rule\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;dotnet&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-dotnet/skills/azure-resource-manager-sql-dotnet&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-search-documents-dotnet&quot;],&quot;description&quot;:[0,&quot;Azure AI Search SDK for .NET (Azure.Search.Documents). Use for building search applications with full-text, vector, semantic, and hybrid search. Covers SearchClient (queries, document CRUD), SearchIndexClient (index management), and SearchIndexerClient (indexers, skillsets). Triggers: \&quot;Azure Search .NET\&quot;, \&quot;SearchClient\&quot;, \&quot;SearchIndexClient\&quot;, \&quot;vector search C#\&quot;, \&quot;semantic search .NET\&quot;, \&quot;hybrid search\&quot;, \&quot;Azure.Search.Documents\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;dotnet&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-dotnet/skills/azure-search-documents-dotnet&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-search-documents-py&quot;],&quot;description&quot;:[0,&quot;Azure AI Search SDK for Python. Use for vector search, hybrid search, semantic ranking, indexing, and skillsets.\nTriggers: \&quot;azure-search-documents\&quot;, \&quot;SearchClient\&quot;, \&quot;SearchIndexClient\&quot;, \&quot;vector search\&quot;, \&quot;hybrid search\&quot;, \&quot;semantic search\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;py&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-python/skills/azure-search-documents-py&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-search-documents-ts&quot;],&quot;description&quot;:[0,&quot;Build search applications using Azure AI Search SDK for JavaScript (@azure/search-documents). Use when creating/managing indexes, implementing vector/hybrid search, semantic ranking, or building agentic retrieval with knowledge bases.&quot;],&quot;lang&quot;:[0,&quot;ts&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-typescript/skills/azure-search-documents-ts&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-security-keyvault-keys-dotnet&quot;],&quot;description&quot;:[0,&quot;Azure Key Vault Keys SDK for .NET. Client library for managing cryptographic keys in Azure Key Vault and Managed HSM. Use for key creation, rotation, encryption, decryption, signing, and verification. Triggers: \&quot;Key Vault keys\&quot;, \&quot;KeyClient\&quot;, \&quot;CryptographyClient\&quot;, \&quot;RSA key\&quot;, \&quot;EC key\&quot;, \&quot;encrypt decrypt .NET\&quot;, \&quot;key rotation\&quot;, \&quot;HSM\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;dotnet&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-dotnet/skills/azure-security-keyvault-keys-dotnet&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-security-keyvault-keys-java&quot;],&quot;description&quot;:[0,&quot;Azure Key Vault Keys Java SDK for cryptographic key management. Use when creating, managing, or using RSA/EC keys, performing encrypt/decrypt/sign/verify operations, or working with HSM-backed keys.&quot;],&quot;lang&quot;:[0,&quot;java&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-java/skills/azure-security-keyvault-keys-java&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-security-keyvault-secrets-java&quot;],&quot;description&quot;:[0,&quot;Azure Key Vault Secrets Java SDK for secret management. Use when storing, retrieving, or managing passwords, API keys, connection strings, or other sensitive configuration data.&quot;],&quot;lang&quot;:[0,&quot;java&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-java/skills/azure-security-keyvault-secrets-java&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-servicebus-dotnet&quot;],&quot;description&quot;:[0,&quot;Azure Service Bus SDK for .NET. Enterprise messaging with queues, topics, subscriptions, and sessions. Use for reliable message delivery, pub/sub patterns, dead letter handling, and background processing. Triggers: \&quot;Service Bus\&quot;, \&quot;ServiceBusClient\&quot;, \&quot;ServiceBusSender\&quot;, \&quot;ServiceBusReceiver\&quot;, \&quot;ServiceBusProcessor\&quot;, \&quot;message queue\&quot;, \&quot;pub/sub .NET\&quot;, \&quot;dead letter queue\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;dotnet&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-dotnet/skills/azure-servicebus-dotnet&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-servicebus-py&quot;],&quot;description&quot;:[0,&quot;Azure Service Bus SDK for Python messaging. Use for queues, topics, subscriptions, and enterprise messaging patterns.\nTriggers: \&quot;service bus\&quot;, \&quot;ServiceBusClient\&quot;, \&quot;queue\&quot;, \&quot;topic\&quot;, \&quot;subscription\&quot;, \&quot;message broker\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;py&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-python/skills/azure-servicebus-py&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-servicebus-ts&quot;],&quot;description&quot;:[0,&quot;Build messaging applications using Azure Service Bus SDK for JavaScript (@azure/service-bus). Use when implementing queues, topics/subscriptions, message sessions, dead-letter handling, or enterprise messaging patterns.&quot;],&quot;lang&quot;:[0,&quot;ts&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-typescript/skills/azure-servicebus-ts&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-speech-to-text-rest-py&quot;],&quot;description&quot;:[0,&quot;Azure Speech to Text REST API for short audio (Python). Use for simple speech recognition of audio files up to 60 seconds without the Speech SDK.\nTriggers: \&quot;speech to text REST\&quot;, \&quot;short audio transcription\&quot;, \&quot;speech recognition REST API\&quot;, \&quot;STT REST\&quot;, \&quot;recognize speech REST\&quot;.\nDO NOT USE FOR: Long audio (&gt;60 seconds), real-time streaming, batch transcription, custom speech models, speech translation. Use Speech SDK or Batch Transcription API instead.&quot;],&quot;lang&quot;:[0,&quot;py&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-python/skills/azure-speech-to-text-rest-py&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-storage-blob-java&quot;],&quot;description&quot;:[0,&quot;Build blob storage applications with Azure Storage Blob SDK for Java. Use when uploading, downloading, or managing files in Azure Blob Storage, working with containers, or implementing streaming data operations.&quot;],&quot;lang&quot;:[0,&quot;java&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-java/skills/azure-storage-blob-java&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-storage-blob-py&quot;],&quot;description&quot;:[0,&quot;Azure Blob Storage SDK for Python. Use for uploading, downloading, listing blobs, managing containers, and blob lifecycle.\nTriggers: \&quot;blob storage\&quot;, \&quot;BlobServiceClient\&quot;, \&quot;ContainerClient\&quot;, \&quot;BlobClient\&quot;, \&quot;upload blob\&quot;, \&quot;download blob\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;py&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-python/skills/azure-storage-blob-py&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-storage-blob-rust&quot;],&quot;description&quot;:[0,&quot;Azure Blob Storage SDK for Rust. Use for uploading, downloading, and managing blobs and containers.\nTriggers: \&quot;blob storage rust\&quot;, \&quot;BlobClient rust\&quot;, \&quot;upload blob rust\&quot;, \&quot;download blob rust\&quot;, \&quot;container rust\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;rust&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-rust/skills/azure-storage-blob-rust&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-storage-blob-ts&quot;],&quot;description&quot;:[0,&quot;Azure Blob Storage JavaScript/TypeScript SDK (@azure/storage-blob) for blob operations. Use for uploading, downloading, listing, and managing blobs and containers. Supports block blobs, append blobs, page blobs, SAS tokens, and streaming. Triggers: \&quot;blob storage\&quot;, \&quot;@azure/storage-blob\&quot;, \&quot;BlobServiceClient\&quot;, \&quot;ContainerClient\&quot;, \&quot;upload blob\&quot;, \&quot;download blob\&quot;, \&quot;SAS token\&quot;, \&quot;block blob\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;ts&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-typescript/skills/azure-storage-blob-ts&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-storage-file-datalake-py&quot;],&quot;description&quot;:[0,&quot;Azure Data Lake Storage Gen2 SDK for Python. Use for hierarchical file systems, big data analytics, and file/directory operations.\nTriggers: \&quot;data lake\&quot;, \&quot;DataLakeServiceClient\&quot;, \&quot;FileSystemClient\&quot;, \&quot;ADLS Gen2\&quot;, \&quot;hierarchical namespace\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;py&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-python/skills/azure-storage-file-datalake-py&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-storage-file-share-py&quot;],&quot;description&quot;:[0,&quot;Azure Storage File Share SDK for Python. Use for SMB file shares, directories, and file operations in the cloud.\nTriggers: \&quot;azure-storage-file-share\&quot;, \&quot;ShareServiceClient\&quot;, \&quot;ShareClient\&quot;, \&quot;file share\&quot;, \&quot;SMB\&quot;.&quot;],&quot;lang&quot;:[0,&quot;py&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-python/skills/azure-storage-file-share-py&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-storage-file-share-ts&quot;],&quot;description&quot;:[0,&quot;Azure File Share JavaScript/TypeScript SDK (@azure/storage-file-share) for SMB file share operations. Use for creating shares, managing directories, uploading/downloading files, and handling file metadata. Supports Azure Files SMB protocol scenarios. Triggers: \&quot;file share\&quot;, \&quot;@azure/storage-file-share\&quot;, \&quot;ShareServiceClient\&quot;, \&quot;ShareClient\&quot;, \&quot;SMB\&quot;, \&quot;Azure Files\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;ts&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-typescript/skills/azure-storage-file-share-ts&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-storage-queue-py&quot;],&quot;description&quot;:[0,&quot;Azure Queue Storage SDK for Python. Use for reliable message queuing, task distribution, and asynchronous processing.\nTriggers: \&quot;queue storage\&quot;, \&quot;QueueServiceClient\&quot;, \&quot;QueueClient\&quot;, \&quot;message queue\&quot;, \&quot;dequeue\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;py&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-python/skills/azure-storage-queue-py&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-storage-queue-ts&quot;],&quot;description&quot;:[0,&quot;Azure Queue Storage JavaScript/TypeScript SDK (@azure/storage-queue) for message queue operations. Use for sending, receiving, peeking, and deleting messages in queues. Supports visibility timeout, message encoding, and batch operations. Triggers: \&quot;queue storage\&quot;, \&quot;@azure/storage-queue\&quot;, \&quot;QueueServiceClient\&quot;, \&quot;QueueClient\&quot;, \&quot;send message\&quot;, \&quot;receive message\&quot;, \&quot;dequeue\&quot;, \&quot;visibility timeout\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;ts&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-typescript/skills/azure-storage-queue-ts&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-web-pubsub-ts&quot;],&quot;description&quot;:[0,&quot;Build real-time messaging applications using Azure Web PubSub SDKs for JavaScript (@azure/web-pubsub, @azure/web-pubsub-client). Use when implementing WebSocket-based real-time features, pub/sub messaging, group chat, or live notifications.&quot;],&quot;lang&quot;:[0,&quot;ts&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-typescript/skills/azure-web-pubsub-ts&quot;]}],[0,{&quot;name&quot;:[0,&quot;cloud-solution-architect&quot;],&quot;description&quot;:[0,&quot;Transform the agent into a Cloud Solution Architect following Azure Architecture Center best practices. Use when designing cloud architectures, reviewing system designs, selecting architecture styles, applying cloud design patterns, making technology choices, or conducting Well-Architected Framework reviews.&quot;],&quot;lang&quot;:[0,&quot;core&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/skills/cloud-solution-architect&quot;]}],[0,{&quot;name&quot;:[0,&quot;continual-learning&quot;],&quot;description&quot;:[0,&quot;Guide for implementing continual learning in AI coding agents — hooks, memory scoping, reflection patterns. Use when setting up learning infrastructure for agents.&quot;],&quot;lang&quot;:[0,&quot;core&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/skills/continual-learning&quot;]}],[0,{&quot;name&quot;:[0,&quot;copilot-sdk&quot;],&quot;description&quot;:[0,&quot;Build applications powered by GitHub Copilot using the Copilot SDK. Use when creating programmatic integrations with Copilot across Node.js/TypeScript, Python, Go, or .NET. Covers session management, custom tools, streaming, hooks, MCP servers, BYOK providers, session persistence, custom agents, skills, and deployment patterns. Requires GitHub Copilot CLI installed and a GitHub Copilot subscription (unless using BYOK).&quot;],&quot;lang&quot;:[0,&quot;core&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/skills/copilot-sdk&quot;]}],[0,{&quot;name&quot;:[0,&quot;entra-agent-id&quot;],&quot;description&quot;:[0,&quot;Microsoft Entra Agent ID (preview) for creating OAuth2-capable AI agent identities via Microsoft Graph beta API.\nCovers Agent Identity Blueprints, BlueprintPrincipals, Agent Identities, required permissions, sponsors, and Workload Identity Federation.\nIncludes Microsoft Entra SDK for AgentID (containerized sidecar) for polyglot agent authentication (Docker/Kubernetes), 3P agent integration, autonomous and interactive agent patterns.\nTriggers: \&quot;agent identity\&quot;, \&quot;agent id\&quot;, \&quot;Agent Identity Blueprint\&quot;, \&quot;BlueprintPrincipal\&quot;, \&quot;entra agent\&quot;, \&quot;agent identity provisioning\&quot;, \&quot;Graph agent identity\&quot;, \&quot;entra sidecar\&quot;, \&quot;agent id sidecar\&quot;, \&quot;auth sidecar\&quot;, \&quot;3P agent\&quot;, \&quot;third-party agent identity\&quot;, \&quot;polyglot agent auth\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;core&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/skills/entra-agent-id&quot;]}],[0,{&quot;name&quot;:[0,&quot;fastapi-router-py&quot;],&quot;description&quot;:[0,&quot;Create FastAPI routers with CRUD operations, authentication dependencies, and proper response models. Use when building REST API endpoints, creating new routes, implementing CRUD operations, or adding authenticated endpoints in FastAPI applications.&quot;],&quot;lang&quot;:[0,&quot;py&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-python/skills/fastapi-router-py&quot;]}],[0,{&quot;name&quot;:[0,&quot;frontend-design-review&quot;],&quot;description&quot;:[0,&quot;Review and create distinctive, production-grade frontend interfaces with high design quality and design system compliance. Evaluates using three pillars: frictionless insight-to-action, quality craft, and trustworthy building. USE FOR: PR reviews, design reviews, accessibility audits, design system compliance checks, creative frontend design, UI code review, component reviews, responsive design checks, theme testing, and creating memorable UI. DO NOT USE FOR: Backend API reviews, database schema reviews, infrastructure or DevOps work, pure business logic without UI, or non-frontend code.\n&quot;],&quot;lang&quot;:[0,&quot;core&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/skills/frontend-design-review&quot;]}],[0,{&quot;name&quot;:[0,&quot;frontend-ui-dark-ts&quot;],&quot;description&quot;:[0,&quot;Build dark-themed React applications using Tailwind CSS with custom theming, glassmorphism effects, and Framer Motion animations. Use when creating dashboards, admin panels, or data-rich interfaces with a refined dark aesthetic.&quot;],&quot;lang&quot;:[0,&quot;ts&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-typescript/skills/frontend-ui-dark-ts&quot;]}],[0,{&quot;name&quot;:[0,&quot;github-issue-creator&quot;],&quot;description&quot;:[0,&quot;Convert raw notes, error logs, voice dictation, or screenshots into crisp GitHub-flavored markdown issue reports. Use when the user pastes bug info, error messages, or informal descriptions and wants a structured GitHub issue. Supports images/GIFs for visual evidence.&quot;],&quot;lang&quot;:[0,&quot;core&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/skills/github-issue-creator&quot;]}],[0,{&quot;name&quot;:[0,&quot;hosted-agents-v2-py&quot;],&quot;description&quot;:[0,&quot;Build Microsoft Foundry hosted agents on the refreshed public preview across Python and C#.\nUse when creating containerized agents with Responses or Invocations protocols, Agent Framework,\nLangGraph, BYO/custom code, agent.yaml, azd ai agent, or AIProjectClient HostedAgentDefinition.\nTriggers: \&quot;hosted agent\&quot;, \&quot;Foundry hosted agent\&quot;, \&quot;Responses protocol\&quot;, \&quot;Invocations protocol\&quot;,\n\&quot;ResponsesAgentServerHost\&quot;, \&quot;InvocationAgentServerHost\&quot;, \&quot;ResponsesHostServer\&quot;,\n\&quot;HostedAgentDefinition\&quot;, \&quot;agent.yaml\&quot;, \&quot;azd ai agent\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;py&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-python/skills/hosted-agents-v2-py&quot;]}],[0,{&quot;name&quot;:[0,&quot;kql&quot;],&quot;description&quot;:[0,&quot;KQL language expertise for writing correct, efficient Kusto Query Language queries. Covers syntax gotchas, join patterns, dynamic types, datetime pitfalls, regex patterns, serialization, memory management, result-size discipline, and advanced functions (geo, vector, graph). USE THIS SKILL whenever writing, debugging, or reviewing KQL queries — even simple ones — because the gotchas section prevents the most common errors that waste tool calls and cause expensive retry cascades. Trigger on: KQL, Kusto, ADX, Azure Data Explorer, Fabric Real-Time Intelligence, EventHouse, Log Analytics, log analysis, data exploration, time series, anomaly detection, summarize, where clause, join, extend, project, let statement, parse operator, extract function, any mention of pipe-forward query syntax.&quot;],&quot;lang&quot;:[0,&quot;core&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/skills/kql&quot;]}],[0,{&quot;name&quot;:[0,&quot;m365-agents-dotnet&quot;],&quot;description&quot;:[0,&quot;Microsoft 365 Agents SDK for .NET. Build multichannel agents for Teams/M365/Copilot Studio with ASP.NET Core hosting, AgentApplication routing, and MSAL-based auth. Triggers: \&quot;Microsoft 365 Agents SDK\&quot;, \&quot;Microsoft.Agents\&quot;, \&quot;AddAgentApplicationOptions\&quot;, \&quot;AgentApplication\&quot;, \&quot;AddAgentAspNetAuthentication\&quot;, \&quot;Copilot Studio client\&quot;, \&quot;IAgentHttpAdapter\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;dotnet&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-dotnet/skills/m365-agents-dotnet&quot;]}],[0,{&quot;name&quot;:[0,&quot;m365-agents-py&quot;],&quot;description&quot;:[0,&quot;Microsoft 365 Agents SDK for Python. Build multichannel agents for Teams/M365/Copilot Studio with aiohttp hosting, AgentApplication routing, streaming responses, and MSAL-based auth. Triggers: \&quot;Microsoft 365 Agents SDK\&quot;, \&quot;microsoft_agents\&quot;, \&quot;AgentApplication\&quot;, \&quot;start_agent_process\&quot;, \&quot;TurnContext\&quot;, \&quot;Copilot Studio client\&quot;, \&quot;CloudAdapter\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;py&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-python/skills/m365-agents-py&quot;]}],[0,{&quot;name&quot;:[0,&quot;m365-agents-ts&quot;],&quot;description&quot;:[0,&quot;Microsoft 365 Agents SDK for TypeScript/Node.js. Build multichannel agents for Teams/M365/Copilot Studio with AgentApplication routing, Express hosting, streaming responses, and Copilot Studio client integration. Triggers: \&quot;Microsoft 365 Agents SDK\&quot;, \&quot;@microsoft/agents-hosting\&quot;, \&quot;AgentApplication\&quot;, \&quot;startServer\&quot;, \&quot;streamingResponse\&quot;, \&quot;Copilot Studio client\&quot;, \&quot;@microsoft/agents-copilotstudio-client\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;ts&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-typescript/skills/m365-agents-ts&quot;]}],[0,{&quot;name&quot;:[0,&quot;mcp-builder&quot;],&quot;description&quot;:[0,&quot;Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP), Node/TypeScript (MCP SDK), or C#/.NET (Microsoft MCP SDK).&quot;],&quot;lang&quot;:[0,&quot;core&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/skills/mcp-builder&quot;]}],[0,{&quot;name&quot;:[0,&quot;microsoft-azure-webjobs-extensions-authentication-events-dotnet&quot;],&quot;description&quot;:[0,&quot;Microsoft Entra Authentication Events SDK for .NET. Azure Functions triggers for custom authentication extensions. Use for token enrichment, custom claims, attribute collection, and OTP customization in Entra ID. Triggers: \&quot;Authentication Events\&quot;, \&quot;WebJobsAuthenticationEventsTrigger\&quot;, \&quot;OnTokenIssuanceStart\&quot;, \&quot;OnAttributeCollectionStart\&quot;, \&quot;custom claims\&quot;, \&quot;token enrichment\&quot;, \&quot;Entra custom extension\&quot;, \&quot;authentication extension\&quot;.&quot;],&quot;lang&quot;:[0,&quot;dotnet&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-dotnet/skills/microsoft-azure-webjobs-extensions-authentication-events-dotnet&quot;]}],[0,{&quot;name&quot;:[0,&quot;microsoft-docs&quot;],&quot;description&quot;:[0,&quot;Understand Microsoft technologies by querying official documentation. Use whenever the user asks how something works, wants tutorials, needs configuration options, limits, quotas, or best practices for any Microsoft technology (Azure, .NET, M365, Windows, Power Platform, etc.)—even if they don&#39;t mention \&quot;docs.\&quot; If the question is about understanding a concept rather than writing code, this is the right skill.&quot;],&quot;lang&quot;:[0,&quot;core&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/skills/microsoft-docs&quot;]}],[0,{&quot;name&quot;:[0,&quot;podcast-generation&quot;],&quot;description&quot;:[0,&quot;Generate AI-powered podcast-style audio narratives using Azure OpenAI&#39;s GPT Realtime Mini model via WebSocket. Use when building text-to-speech features, audio narrative generation, podcast creation from content, or integrating with Azure OpenAI Realtime API for real audio output. Covers full-stack implementation from React frontend to Python FastAPI backend with WebSocket streaming.&quot;],&quot;lang&quot;:[0,&quot;core&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/skills/podcast-generation&quot;]}],[0,{&quot;name&quot;:[0,&quot;pydantic-models-py&quot;],&quot;description&quot;:[0,&quot;Create Pydantic models following the multi-model pattern with Base, Create, Update, Response, and InDB variants. Use when defining API request/response schemas, database models, or data validation in Python applications using Pydantic v2.&quot;],&quot;lang&quot;:[0,&quot;py&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-python/skills/pydantic-models-py&quot;]}],[0,{&quot;name&quot;:[0,&quot;react-flow-node-ts&quot;],&quot;description&quot;:[0,&quot;Create React Flow node components with TypeScript types, handles, and Zustand integration. Use when building custom nodes for React Flow canvas, creating visual workflow editors, or implementing node-based UI components.&quot;],&quot;lang&quot;:[0,&quot;ts&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-typescript/skills/react-flow-node-ts&quot;]}],[0,{&quot;name&quot;:[0,&quot;skill-creator&quot;],&quot;description&quot;:[0,&quot;Guide for creating effective skills for AI coding agents working with Azure SDKs and Microsoft Foundry services. Use when creating new skills or updating existing skills.&quot;],&quot;lang&quot;:[0,&quot;core&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/skills/skill-creator&quot;]}],[0,{&quot;name&quot;:[0,&quot;zustand-store-ts&quot;],&quot;description&quot;:[0,&quot;Create Zustand stores with TypeScript, subscribeWithSelector middleware, and proper state/action separation. Use when building React state management, creating global stores, or implementing reactive state patterns with Zustand.&quot;],&quot;lang&quot;:[0,&quot;ts&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-typescript/skills/zustand-store-ts&quot;]}]]],&quot;data-astro-cid-j7pv25f6&quot;:[0,true]}" ssr client="load" opts="{&quot;name&quot;:&quot;SkillsSection&quot;,&quot;value&quot;:true}" await-children><section style="padding:var(--space-md) 0 var(--space-2xl)"><div style="margin-bottom:var(--space-lg)"><div style="display:flex;align-items:center;gap:var(--space-md);padding:var(--space-sm) var(--space-md);min-width:240px;max-width:400px;background:var(--bg-secondary);border:1px solid var(--border-primary);border-radius:var(--radius-lg);transition:all var(--transition-fast)"><svg width="16" height="16" viewBox="0 0 24 24" fill="none" stroke="var(--text-muted)" stroke-width="2" style="flex-shrink:0"><circle cx="11" cy="11" r="8"></circle><path d="m21 21-4.35-4.35"></path></svg><input type="text" placeholder="Search skills by name or description..." data-testid="search-input" style="flex:1;background:transparent;border:none;outline:none;font-size:var(--text-sm);color:var(--text-primary)" value=""/><kbd style="padding:2px 6px;font-size:var(--text-xs);background:var(--bg-tertiary);border:1px solid var(--border-primary);border-radius:var(--radius-sm);color:var(--text-secondary)">⌘K</kbd></div></div><div style="margin-bottom:var(--space-md)"><div style="display:flex;flex-wrap:wrap;gap:var(--space-sm);padding:var(--space-xs);background:var(--bg-secondary);border-radius:var(--radius-lg);border:1px solid var(--border-primary)"><button style="display:flex;align-items:center;gap:var(--space-xs);padding:var(--space-sm) var(--space-md);font-size:var(--text-sm);font-weight:500;color:var(--text-primary);background:var(--bg-tertiary);border:none;border-radius:var(--radius-md);cursor:pointer;transition:all var(--transition-fast);position:relative"><span>All</span><span style="font-size:var(--text-xs);color:var(--accent);font-weight:600;min-width:20px;text-align:center">140</span><span style="position:absolute;bottom:2px;left:50%;transform:translateX(-50%);width:24px;height:2px;background:var(--accent);border-radius:1px"></span></button><button style="display:flex;align-items:center;gap:var(--space-xs);padding:var(--space-sm) var(--space-md);font-size:var(--text-sm);font-weight:500;color:var(--text-secondary);background:transparent;border:none;border-radius:var(--radius-md);cursor:pointer;transition:all var(--transition-fast);position:relative"><span style="width:8px;height:8px;border-radius:50%;background:var(--lang-core);flex-shrink:0"></span><span>Core</span><span style="font-size:var(--text-xs);color:var(--text-muted);font-weight:600;min-width:20px;text-align:center">11</span></button><button style="display:flex;align-items:center;gap:var(--space-xs);padding:var(--space-sm) var(--space-md);font-size:var(--text-sm);font-weight:500;color:var(--text-secondary);background:transparent;border:none;border-radius:var(--radius-md);cursor:pointer;transition:all var(--transition-fast);position:relative"><span style="width:8px;height:8px;border-radius:50%;background:var(--lang-python);flex-shrink:0"></span><span>Python</span><span style="font-size:var(--text-xs);color:var(--text-muted);font-weight:600;min-width:20px;text-align:center">42</span></button><button style="display:flex;align-items:center;gap:var(--space-xs);padding:var(--space-sm) var(--space-md);font-size:var(--text-sm);font-weight:500;color:var(--text-secondary);background:transparent;border:none;border-radius:var(--radius-md);cursor:pointer;transition:all var(--transition-fast);position:relative"><span style="width:8px;height:8px;border-radius:50%;background:var(--lang-dotnet);flex-shrink:0"></span><span>.NET</span><span style="font-size:var(--text-xs);color:var(--text-muted);font-weight:600;min-width:20px;text-align:center">29</span></button><button style="display:flex;align-items:center;gap:var(--space-xs);padding:var(--space-sm) var(--space-md);font-size:var(--text-sm);font-weight:500;color:var(--text-secondary);background:transparent;border:none;border-radius:var(--radius-md);cursor:pointer;transition:all var(--transition-fast);position:relative"><span style="width:8px;height:8px;border-radius:50%;background:var(--lang-typescript);flex-shrink:0"></span><span>TypeScript</span><span style="font-size:var(--text-xs);color:var(--text-muted);font-weight:600;min-width:20px;text-align:center">25</span></button><button style="display:flex;align-items:center;gap:var(--space-xs);padding:var(--space-sm) var(--space-md);font-size:var(--text-sm);font-weight:500;color:var(--text-secondary);background:transparent;border:none;border-radius:var(--radius-md);cursor:pointer;transition:all var(--transition-fast);position:relative"><span style="width:8px;height:8px;border-radius:50%;background:var(--lang-java);flex-shrink:0"></span><span>Java</span><span style="font-size:var(--text-xs);color:var(--text-muted);font-weight:600;min-width:20px;text-align:center">26</span></button><button style="display:flex;align-items:center;gap:var(--space-xs);padding:var(--space-sm) var(--space-md);font-size:var(--text-sm);font-weight:500;color:var(--text-secondary);background:transparent;border:none;border-radius:var(--radius-md);cursor:pointer;transition:all var(--transition-fast);position:relative"><span style="width:8px;height:8px;border-radius:50%;background:var(--lang-rust);flex-shrink:0"></span><span>Rust</span><span style="font-size:var(--text-xs);color:var(--text-muted);font-weight:600;min-width:20px;text-align:center">7</span></button></div></div><div style="margin-bottom:var(--space-xl)"><div style="display:flex;flex-wrap:wrap;gap:var(--space-xs)"><button style="display:inline-flex;align-items:center;gap:var(--space-xs);padding:6px 12px;font-size:var(--text-xs);font-weight:500;color:var(--text-primary);background:var(--accent-glow);border:1px solid var(--accent);border-radius:9999px;cursor:pointer;transition:all var(--transition-fast);text-transform:capitalize"><span>All</span><span style="font-size:10px;color:var(--accent);font-weight:600">140</span></button><button style="display:inline-flex;align-items:center;gap:var(--space-xs);padding:6px 12px;font-size:var(--text-xs);font-weight:500;color:var(--text-secondary);background:var(--bg-secondary);border:1px solid var(--border-primary);border-radius:9999px;cursor:pointer;transition:all var(--transition-fast);text-transform:capitalize"><span>General</span><span style="font-size:10px;color:var(--text-muted);font-weight:600">140</span></button></div></div><div style="margin-bottom:var(--space-lg);font-size:var(--text-sm);color:var(--text-secondary)">Showing <span style="color:var(--accent);font-weight:600">140</span> skills</div><div style="position:relative"><div class="skills-grid" style="max-height:464px;overflow:hidden;transition:max-height 0.3s ease-out"><div style="display:block;background:var(--bg-card);border:1px solid var(--border-primary);border-radius:var(--radius-lg);padding:var(--space-lg);transition:all var(--transition-base);cursor:pointer"><div style="display:flex;flex-direction:column;gap:var(--space-md);height:100%"><div style="display:flex;align-items:flex-start;justify-content:space-between;gap:var(--space-sm)"><h3 style="font-size:var(--text-base);font-weight:600;color:var(--text-primary);margin:0;line-height:1.4;word-break:break-word">agent-framework-azure-ai-py</h3><span style="display:inline-flex;align-items:center;padding:2px 8px;font-size:var(--text-xs);font-weight:500;border-radius:var(--radius-sm);text-transform:uppercase;letter-spacing:0.05em;background:rgba(96, 165, 250, 0.15);color:#60a5fa;border:1px solid rgba(96, 165, 250, 0.3);flex-shrink:0">Python</span></div><p style="font-size:var(--text-sm);color:var(--text-secondary);margin:0;line-height:1.6;flex:1;display:-webkit-box;-webkit-line-clamp:3;-webkit-box-orient:vertical;overflow:hidden">Build Azure AI Foundry agents using the Microsoft Agent Framework Python SDK (agent-framework-azure-ai). Use when creating persistent agents with AzureAIAgentsProvider, using hosted tools (code interpreter, file search, web search), integrating MCP servers, managing conversation threads, or implementing streaming responses. Covers function tools, structured outputs, and multi-tool agents.</p><div style="display:flex;align-items:center;justify-content:space-between;gap:var(--space-sm);margin-top:auto"><span style="font-size:var(--text-xs);color:var(--text-muted);text-transform:capitalize">general</span><button style="display:inline-flex;align-items:center;gap:4px;padding:4px 8px;font-size:var(--text-xs);font-weight:500;color:var(--text-secondary);background:var(--bg-tertiary);border:1px solid var(--border-primary);border-radius:var(--radius-sm);cursor:pointer;transition:all var(--transition-fast)" title="Copy: npx skills add microsoft/skills --skill agent-framework-azure-ai-py"><svg width="12" height="12" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2"><rect x="9" y="9" width="13" height="13" rx="2" ry="2"></rect><path d="M5 15H4a2 2 0 01-2-2V4a2 2 0 012-2h9a2 2 0 012 2v1"></path></svg>Copy</button></div></div></div><div style="display:block;background:var(--bg-card);border:1px solid var(--border-primary);border-radius:var(--radius-lg);padding:var(--space-lg);transition:all var(--transition-base);cursor:pointer"><div style="display:flex;flex-direction:column;gap:var(--space-md);height:100%"><div style="display:flex;align-items:flex-start;justify-content:space-between;gap:var(--space-sm)"><h3 style="font-size:var(--text-base);font-weight:600;color:var(--text-primary);margin:0;line-height:1.4;word-break:break-word">agents-v2-py</h3><span style="display:inline-flex;align-items:center;padding:2px 8px;font-size:var(--text-xs);font-weight:500;border-radius:var(--radius-sm);text-transform:uppercase;letter-spacing:0.05em;background:rgba(96, 165, 250, 0.15);color:#60a5fa;border:1px solid rgba(96, 165, 250, 0.3);flex-shrink:0">Python</span></div><p style="font-size:var(--text-sm);color:var(--text-secondary);margin:0;line-height:1.6;flex:1;display:-webkit-box;-webkit-line-clamp:3;-webkit-box-orient:vertical;overflow:hidden">Maintain legacy initial-preview Foundry container agents built with Azure AI Projects.
+</a> </div> </nav> <div class="mobile-nav-overlay" id="mobile-nav-overlay" data-astro-cid-sckkx6r4></div> <main class="main" id="main-content" data-astro-cid-sckkx6r4>  <div class="container" data-astro-cid-j7pv25f6> <!-- Skills Section with Filters --> <style>astro-island,astro-slot,astro-static-slot{display:contents}</style><script>(()=>{var e=async t=>{await(await t())()};(self.Astro||(self.Astro={})).load=e;window.dispatchEvent(new Event("astro:load"));})();</script><script>(()=>{var A=Object.defineProperty;var g=(i,o,a)=>o in i?A(i,o,{enumerable:!0,configurable:!0,writable:!0,value:a}):i[o]=a;var d=(i,o,a)=>g(i,typeof o!="symbol"?o+"":o,a);{let i={0:t=>m(t),1:t=>a(t),2:t=>new RegExp(t),3:t=>new Date(t),4:t=>new Map(a(t)),5:t=>new Set(a(t)),6:t=>BigInt(t),7:t=>new URL(t),8:t=>new Uint8Array(t),9:t=>new Uint16Array(t),10:t=>new Uint32Array(t),11:t=>1/0*t},o=t=>{let[l,e]=t;return l in i?i[l](e):void 0},a=t=>t.map(o),m=t=>typeof t!="object"||t===null?t:Object.fromEntries(Object.entries(t).map(([l,e])=>[l,o(e)]));class y extends HTMLElement{constructor(){super(...arguments);d(this,"Component");d(this,"hydrator");d(this,"hydrate",async()=>{var b;if(!this.hydrator||!this.isConnected)return;let e=(b=this.parentElement)==null?void 0:b.closest("astro-island[ssr]");if(e){e.addEventListener("astro:hydrate",this.hydrate,{once:!0});return}let c=this.querySelectorAll("astro-slot"),n={},h=this.querySelectorAll("template[data-astro-template]");for(let r of h){let s=r.closest(this.tagName);s!=null&&s.isSameNode(this)&&(n[r.getAttribute("data-astro-template")||"default"]=r.innerHTML,r.remove())}for(let r of c){let s=r.closest(this.tagName);s!=null&&s.isSameNode(this)&&(n[r.getAttribute("name")||"default"]=r.innerHTML)}let p;try{p=this.hasAttribute("props")?m(JSON.parse(this.getAttribute("props"))):{}}catch(r){let s=this.getAttribute("component-url")||"<unknown>",v=this.getAttribute("component-export");throw v&&(s+=` (export ${v})`),console.error(`[hydrate] Error parsing props for component ${s}`,this.getAttribute("props"),r),r}let u;await this.hydrator(this)(this.Component,p,n,{client:this.getAttribute("client")}),this.removeAttribute("ssr"),this.dispatchEvent(new CustomEvent("astro:hydrate"))});d(this,"unmount",()=>{this.isConnected||this.dispatchEvent(new CustomEvent("astro:unmount"))})}disconnectedCallback(){document.removeEventListener("astro:after-swap",this.unmount),document.addEventListener("astro:after-swap",this.unmount,{once:!0})}connectedCallback(){if(!this.hasAttribute("await-children")||document.readyState==="interactive"||document.readyState==="complete")this.childrenConnectedCallback();else{let e=()=>{document.removeEventListener("DOMContentLoaded",e),c.disconnect(),this.childrenConnectedCallback()},c=new MutationObserver(()=>{var n;((n=this.lastChild)==null?void 0:n.nodeType)===Node.COMMENT_NODE&&this.lastChild.nodeValue==="astro:end"&&(this.lastChild.remove(),e())});c.observe(this,{childList:!0}),document.addEventListener("DOMContentLoaded",e)}}async childrenConnectedCallback(){let e=this.getAttribute("before-hydration-url");e&&await import(e),this.start()}async start(){let e=JSON.parse(this.getAttribute("opts")),c=this.getAttribute("client");if(Astro[c]===void 0){window.addEventListener(`astro:${c}`,()=>this.start(),{once:!0});return}try{await Astro[c](async()=>{let n=this.getAttribute("renderer-url"),[h,{default:p}]=await Promise.all([import(this.getAttribute("component-url")),n?import(n):()=>()=>{}]),u=this.getAttribute("component-export")||"default";if(!u.includes("."))this.Component=h[u];else{this.Component=h;for(let f of u.split("."))this.Component=this.Component[f]}return this.hydrator=p,this.hydrate},e,this)}catch(n){console.error(`[astro-island] Error hydrating ${this.getAttribute("component-url")}`,n)}}attributeChangedCallback(){this.hydrate()}}d(y,"observedAttributes",["props"]),customElements.get("astro-island")||customElements.define("astro-island",y)}})();</script><astro-island uid="Z2hXjXG" prefix="r1" component-url="/skills/_astro/SkillsSection.JkW9vxqE.js" component-export="SkillsSection" renderer-url="/skills/_astro/client.Dc9Vh3na.js" props="{&quot;skills&quot;:[1,[[0,{&quot;name&quot;:[0,&quot;agent-framework-azure-ai-py&quot;],&quot;description&quot;:[0,&quot;Build Azure AI Foundry agents using the Microsoft Agent Framework Python SDK (agent-framework-azure-ai). Use when creating persistent agents with AzureAIAgentsProvider, using hosted tools (code interpreter, file search, web search), integrating MCP servers, managing conversation threads, or implementing streaming responses. Covers function tools, structured outputs, and multi-tool agents.&quot;],&quot;lang&quot;:[0,&quot;py&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-python/skills/agent-framework-azure-ai-py&quot;]}],[0,{&quot;name&quot;:[0,&quot;agents-v2-py&quot;],&quot;description&quot;:[0,&quot;Maintain legacy initial-preview Foundry container agents built with Azure AI Projects.\nUse only for pre-refreshed hosted-agent implementations. For current hosted agents with\nResponses or Invocations protocols, use hosted-agents-v2-py.\nTriggers: \&quot;legacy container agent\&quot;, \&quot;initial preview hosted agent\&quot;,\n\&quot;pre-refreshed Foundry Agent\&quot;, \&quot;legacy agent create_version\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;py&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-python/skills/agents-v2-py&quot;]}],[0,{&quot;name&quot;:[0,&quot;applicationinsights-web-ts&quot;],&quot;description&quot;:[0,&quot;Instrument browser/web apps with the Application Insights JavaScript SDK (@microsoft/applicationinsights-web). Use for Real User Monitoring (RUM) — page views, clicks, AJAX/fetch dependencies, exceptions, custom events, and browser-side GenAI agent traces correlated to backend OpenTelemetry traces. Covers SDK Loader Script and npm setup, framework extensions (React, React Native, Angular), Click Analytics, telemetry initializers, and OTel GenAI semantic conventions for agent/tool/model spans emitted from the browser.&quot;],&quot;lang&quot;:[0,&quot;ts&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-typescript/skills/applicationinsights-web-ts&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-ai-agents-persistent-dotnet&quot;],&quot;description&quot;:[0,&quot;Azure AI Agents Persistent SDK for .NET. Low-level SDK for creating and managing AI agents with threads, messages, runs, and tools. Use for agent CRUD, conversation threads, streaming responses, function calling, file search, and code interpreter. Triggers: \&quot;PersistentAgentsClient\&quot;, \&quot;persistent agents\&quot;, \&quot;agent threads\&quot;, \&quot;agent runs\&quot;, \&quot;streaming agents\&quot;, \&quot;function calling agents .NET\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;dotnet&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-dotnet/skills/azure-ai-agents-persistent-dotnet&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-ai-agents-persistent-java&quot;],&quot;description&quot;:[0,&quot;Azure AI Agents Persistent SDK for Java. Low-level SDK for creating and managing AI agents with threads, messages, runs, and tools.\nTriggers: \&quot;PersistentAgentsClient\&quot;, \&quot;persistent agents java\&quot;, \&quot;agent threads java\&quot;, \&quot;agent runs java\&quot;, \&quot;streaming agents java\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;java&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-java/skills/azure-ai-agents-persistent-java&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-ai-anomalydetector-java&quot;],&quot;description&quot;:[0,&quot;Build anomaly detection applications with Azure AI Anomaly Detector SDK for Java. 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Use when moderating user-generated content, detecting hate speech, violence, sexual content, or self-harm, or managing custom blocklists.&quot;],&quot;lang&quot;:[0,&quot;ts&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-typescript/skills/azure-ai-contentsafety-ts&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-ai-contentunderstanding-py&quot;],&quot;description&quot;:[0,&quot;Azure AI Content Understanding SDK for Python. Use for multimodal content extraction from documents, images, audio, and video.\nTriggers: \&quot;azure-ai-contentunderstanding\&quot;, \&quot;ContentUnderstandingClient\&quot;, \&quot;multimodal analysis\&quot;, \&quot;document extraction\&quot;, \&quot;video analysis\&quot;, \&quot;audio transcription\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;py&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-python/skills/azure-ai-contentunderstanding-py&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-ai-document-intelligence-dotnet&quot;],&quot;description&quot;:[0,&quot;Azure AI Document Intelligence SDK for .NET. Extract text, tables, and structured data from documents using prebuilt and custom models. Use for invoice processing, receipt extraction, ID document analysis, and custom document models. Triggers: \&quot;Document Intelligence\&quot;, \&quot;DocumentIntelligenceClient\&quot;, \&quot;form recognizer\&quot;, \&quot;invoice extraction\&quot;, \&quot;receipt OCR\&quot;, \&quot;document analysis .NET\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;dotnet&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-dotnet/skills/azure-ai-document-intelligence-dotnet&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-ai-document-intelligence-ts&quot;],&quot;description&quot;:[0,&quot;Extract text, tables, and structured data from documents using Azure Document Intelligence (@azure-rest/ai-document-intelligence). Use when processing invoices, receipts, IDs, forms, or building custom document models.&quot;],&quot;lang&quot;:[0,&quot;ts&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-typescript/skills/azure-ai-document-intelligence-ts&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-ai-formrecognizer-java&quot;],&quot;description&quot;:[0,&quot;Azure AI Document Intelligence SDK for Java (com.azure:azure-ai-documentintelligence).\nUse for extracting text, tables, key-value pairs from documents, receipts, invoices, IDs, or building custom document models.\nTriggers: \&quot;document intelligence java\&quot;, \&quot;form recognizer java\&quot;, \&quot;extract text from PDF java\&quot;, \&quot;OCR document java\&quot;,\n\&quot;analyze invoice receipt java\&quot;, \&quot;custom document model java\&quot;, \&quot;document classification java\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;java&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-java/skills/azure-ai-formrecognizer-java&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-ai-language-conversations-py&quot;],&quot;description&quot;:[0,&quot;Implement Conversational Language Understanding (CLU) using the azure-ai-language-conversations Python SDK. Use when working with ConversationAnalysisClient to analyze conversation intent and entities, building NLP features, or integrating language understanding into applications.&quot;],&quot;lang&quot;:[0,&quot;py&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-python/skills/azure-ai-language-conversations-py&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-ai-ml-py&quot;],&quot;description&quot;:[0,&quot;Azure Machine Learning SDK v2 for Python. Use for ML workspaces, jobs, models, datasets, compute, and pipelines.\nTriggers: \&quot;azure-ai-ml\&quot;, \&quot;MLClient\&quot;, \&quot;workspace\&quot;, \&quot;model registry\&quot;, \&quot;training jobs\&quot;, \&quot;datasets\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;py&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-python/skills/azure-ai-ml-py&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-ai-openai-dotnet&quot;],&quot;description&quot;:[0,&quot;Azure OpenAI SDK for .NET. Client library for Azure OpenAI and OpenAI services. Use for chat completions, embeddings, image generation, audio transcription, and assistants. Triggers: \&quot;Azure OpenAI\&quot;, \&quot;AzureOpenAIClient\&quot;, \&quot;ChatClient\&quot;, \&quot;chat completions .NET\&quot;, \&quot;GPT-4\&quot;, \&quot;embeddings\&quot;, \&quot;DALL-E\&quot;, \&quot;Whisper\&quot;, \&quot;OpenAI .NET\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;dotnet&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-dotnet/skills/azure-ai-openai-dotnet&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-ai-projects-dotnet&quot;],&quot;description&quot;:[0,&quot;Azure AI Projects SDK for .NET. High-level client for Azure AI Foundry projects including agents, connections, datasets, deployments, evaluations, and indexes. Use for AI Foundry project management, versioned agents, and orchestration. Triggers: \&quot;AI Projects\&quot;, \&quot;AIProjectClient\&quot;, \&quot;Foundry project\&quot;, \&quot;versioned agents\&quot;, \&quot;evaluations\&quot;, \&quot;datasets\&quot;, \&quot;connections\&quot;, \&quot;deployments .NET\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;dotnet&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-dotnet/skills/azure-ai-projects-dotnet&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-ai-projects-java&quot;],&quot;description&quot;:[0,&quot;Azure AI Projects SDK for Java. High-level SDK for Azure AI Foundry project management including connections, datasets, indexes, and evaluations.\nTriggers: \&quot;AIProjectClient java\&quot;, \&quot;azure ai projects java\&quot;, \&quot;Foundry project java\&quot;, \&quot;ConnectionsClient\&quot;, \&quot;DatasetsClient\&quot;, \&quot;IndexesClient\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;java&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-java/skills/azure-ai-projects-java&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-ai-projects-py&quot;],&quot;description&quot;:[0,&quot;Build AI applications using the Azure AI Projects Python SDK (azure-ai-projects). Use when working with Foundry project clients, creating versioned agents with PromptAgentDefinition, running evaluations, managing connections/deployments/datasets/indexes, or using OpenAI-compatible clients. This is the high-level Foundry SDK - for low-level agent operations, use azure-ai-agents-python skill.&quot;],&quot;lang&quot;:[0,&quot;py&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-python/skills/azure-ai-projects-py&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-ai-projects-ts&quot;],&quot;description&quot;:[0,&quot;Build AI applications using Azure AI Projects SDK for JavaScript (@azure/ai-projects). Use when working with Foundry project clients, agents, connections, deployments, datasets, indexes, evaluations, or getting OpenAI clients.&quot;],&quot;lang&quot;:[0,&quot;ts&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-typescript/skills/azure-ai-projects-ts&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-ai-textanalytics-py&quot;],&quot;description&quot;:[0,&quot;Azure AI Text Analytics SDK for sentiment analysis, entity recognition, key phrases, language detection, PII, and healthcare NLP. Use for natural language processing on text.\nTriggers: \&quot;text analytics\&quot;, \&quot;sentiment analysis\&quot;, \&quot;entity recognition\&quot;, \&quot;key phrase\&quot;, \&quot;PII detection\&quot;, \&quot;TextAnalyticsClient\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;py&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-python/skills/azure-ai-textanalytics-py&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-ai-transcription-py&quot;],&quot;description&quot;:[0,&quot;Azure AI Transcription SDK for Python. Use for real-time and batch speech-to-text transcription with timestamps and diarization.\nTriggers: \&quot;transcription\&quot;, \&quot;speech to text\&quot;, \&quot;Azure AI Transcription\&quot;, \&quot;TranscriptionClient\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;py&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-python/skills/azure-ai-transcription-py&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-ai-translation-document-py&quot;],&quot;description&quot;:[0,&quot;Azure AI Document Translation SDK for batch translation of documents with format preservation. Use for translating Word, PDF, Excel, PowerPoint, and other document formats at scale.\nTriggers: \&quot;document translation\&quot;, \&quot;batch translation\&quot;, \&quot;translate documents\&quot;, \&quot;DocumentTranslationClient\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;py&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-python/skills/azure-ai-translation-document-py&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-ai-translation-text-py&quot;],&quot;description&quot;:[0,&quot;Azure AI Text Translation SDK for real-time text translation, transliteration, language detection, and dictionary lookup. Use for translating text content in applications.\nTriggers: \&quot;text translation\&quot;, \&quot;translator\&quot;, \&quot;translate text\&quot;, \&quot;transliterate\&quot;, \&quot;TextTranslationClient\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;py&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-python/skills/azure-ai-translation-text-py&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-ai-translation-ts&quot;],&quot;description&quot;:[0,&quot;Build translation applications using Azure Translation SDKs for JavaScript (@azure-rest/ai-translation-text, @azure-rest/ai-translation-document). Use when implementing text translation, transliteration, language detection, or batch document translation.&quot;],&quot;lang&quot;:[0,&quot;ts&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-typescript/skills/azure-ai-translation-ts&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-ai-vision-imageanalysis-java&quot;],&quot;description&quot;:[0,&quot;Build image analysis applications with Azure AI Vision SDK for Java. Use when implementing image captioning, OCR text extraction, object detection, tagging, or smart cropping.&quot;],&quot;lang&quot;:[0,&quot;java&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-java/skills/azure-ai-vision-imageanalysis-java&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-ai-vision-imageanalysis-py&quot;],&quot;description&quot;:[0,&quot;Azure AI Vision Image Analysis SDK for captions, tags, objects, OCR, people detection, and smart cropping. Use for computer vision and image understanding tasks.\nTriggers: \&quot;image analysis\&quot;, \&quot;computer vision\&quot;, \&quot;OCR\&quot;, \&quot;object detection\&quot;, \&quot;ImageAnalysisClient\&quot;, \&quot;image caption\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;py&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-python/skills/azure-ai-vision-imageanalysis-py&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-ai-voicelive-dotnet&quot;],&quot;description&quot;:[0,&quot;Azure AI Voice Live SDK for .NET. Build real-time voice AI applications with bidirectional WebSocket communication. Use for voice assistants, conversational AI, real-time speech-to-speech, and voice-enabled chatbots. Triggers: \&quot;voice live\&quot;, \&quot;real-time voice\&quot;, \&quot;VoiceLiveClient\&quot;, \&quot;VoiceLiveSession\&quot;, \&quot;voice assistant .NET\&quot;, \&quot;bidirectional audio\&quot;, \&quot;speech-to-speech\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;dotnet&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-dotnet/skills/azure-ai-voicelive-dotnet&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-ai-voicelive-java&quot;],&quot;description&quot;:[0,&quot;Azure AI VoiceLive SDK for Java. Real-time bidirectional voice conversations with AI assistants using WebSocket.\nTriggers: \&quot;VoiceLiveClient java\&quot;, \&quot;voice assistant java\&quot;, \&quot;real-time voice java\&quot;, \&quot;audio streaming java\&quot;, \&quot;voice activity detection java\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;java&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-java/skills/azure-ai-voicelive-java&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-ai-voicelive-py&quot;],&quot;description&quot;:[0,&quot;Build real-time voice AI applications using Azure AI Voice Live SDK (azure-ai-voicelive). Use this skill when creating Python applications that need real-time bidirectional audio communication with Azure AI, including voice assistants, voice-enabled chatbots, real-time speech-to-speech translation, voice-driven avatars, or any WebSocket-based audio streaming with AI models. Supports Server VAD (Voice Activity Detection), turn-based conversation, function calling, MCP tools, avatar integration, and transcription.&quot;],&quot;lang&quot;:[0,&quot;py&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-python/skills/azure-ai-voicelive-py&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-ai-voicelive-ts&quot;],&quot;description&quot;:[0,&quot;Azure AI Voice Live SDK for JavaScript/TypeScript. Build real-time voice AI applications with bidirectional WebSocket communication. Use for voice assistants, conversational AI, real-time speech-to-speech, and voice-enabled chatbots in Node.js or browser environments. Triggers: \&quot;voice live\&quot;, \&quot;real-time voice\&quot;, \&quot;VoiceLiveClient\&quot;, \&quot;VoiceLiveSession\&quot;, \&quot;voice assistant TypeScript\&quot;, \&quot;bidirectional audio\&quot;, \&quot;speech-to-speech JavaScript\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;ts&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-typescript/skills/azure-ai-voicelive-ts&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-appconfiguration-java&quot;],&quot;description&quot;:[0,&quot;Azure App Configuration SDK for Java. Centralized application configuration management with key-value settings, feature flags, and snapshots.\nTriggers: \&quot;ConfigurationClient java\&quot;, \&quot;app configuration java\&quot;, \&quot;feature flag java\&quot;, \&quot;configuration setting java\&quot;, \&quot;azure config java\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;java&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-java/skills/azure-appconfiguration-java&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-appconfiguration-py&quot;],&quot;description&quot;:[0,&quot;Azure App Configuration SDK for Python. Use for centralized configuration management, feature flags, and dynamic settings.\nTriggers: \&quot;azure-appconfiguration\&quot;, \&quot;AzureAppConfigurationClient\&quot;, \&quot;feature flags\&quot;, \&quot;configuration\&quot;, \&quot;key-value settings\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;py&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-python/skills/azure-appconfiguration-py&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-appconfiguration-ts&quot;],&quot;description&quot;:[0,&quot;Build applications using Azure App Configuration SDK for JavaScript (@azure/app-configuration). Use when working with configuration settings, feature flags, Key Vault references, dynamic refresh, or centralized configuration management.&quot;],&quot;lang&quot;:[0,&quot;ts&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-typescript/skills/azure-appconfiguration-ts&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-communication-callautomation-java&quot;],&quot;description&quot;:[0,&quot;Build call automation workflows with Azure Communication Services Call Automation Java SDK. Use when implementing IVR systems, call routing, call recording, DTMF recognition, text-to-speech, or AI-powered call flows.&quot;],&quot;lang&quot;:[0,&quot;java&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-java/skills/azure-communication-callautomation-java&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-communication-callingserver-java&quot;],&quot;description&quot;:[0,&quot;Azure Communication Services CallingServer (legacy) Java SDK. Note - This SDK is deprecated. Use azure-communication-callautomation instead for new projects. Only use this skill when maintaining legacy code.&quot;],&quot;lang&quot;:[0,&quot;java&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-java/skills/azure-communication-callingserver-java&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-communication-chat-java&quot;],&quot;description&quot;:[0,&quot;Build real-time chat applications with Azure Communication Services Chat Java SDK. Use when implementing chat threads, messaging, participants, read receipts, typing notifications, or real-time chat features.&quot;],&quot;lang&quot;:[0,&quot;java&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-java/skills/azure-communication-chat-java&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-communication-common-java&quot;],&quot;description&quot;:[0,&quot;Azure Communication Services common utilities for Java. Use when working with CommunicationTokenCredential, user identifiers, token refresh, or shared authentication across ACS services.&quot;],&quot;lang&quot;:[0,&quot;java&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-java/skills/azure-communication-common-java&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-communication-sms-java&quot;],&quot;description&quot;:[0,&quot;Send SMS messages with Azure Communication Services SMS Java SDK. Use when implementing SMS notifications, alerts, OTP delivery, bulk messaging, or delivery reports.&quot;],&quot;lang&quot;:[0,&quot;java&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-java/skills/azure-communication-sms-java&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-compute-batch-java&quot;],&quot;description&quot;:[0,&quot;Azure Batch SDK for Java. Run large-scale parallel and HPC batch jobs with pools, jobs, tasks, and compute nodes.\nTriggers: \&quot;BatchClient java\&quot;, \&quot;azure batch java\&quot;, \&quot;batch pool java\&quot;, \&quot;batch job java\&quot;, \&quot;HPC java\&quot;, \&quot;parallel computing java\&quot;.&quot;],&quot;lang&quot;:[0,&quot;java&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-java/skills/azure-compute-batch-java&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-containerregistry-py&quot;],&quot;description&quot;:[0,&quot;Azure Container Registry SDK for Python. Use for managing container images, artifacts, and repositories.\nTriggers: \&quot;azure-containerregistry\&quot;, \&quot;ContainerRegistryClient\&quot;, \&quot;container images\&quot;, \&quot;docker registry\&quot;, \&quot;ACR\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;py&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-python/skills/azure-containerregistry-py&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-cosmos-db-py&quot;],&quot;description&quot;:[0,&quot;Build Azure Cosmos DB NoSQL services with Python/FastAPI following production-grade patterns. Use when implementing database client setup with dual auth (DefaultAzureCredential + emulator), service layer classes with CRUD operations, partition key strategies, parameterized queries, or TDD patterns for Cosmos. Triggers on phrases like \&quot;Cosmos DB\&quot;, \&quot;NoSQL database\&quot;, \&quot;document store\&quot;, \&quot;add persistence\&quot;, \&quot;database service layer\&quot;, or \&quot;Python Cosmos SDK\&quot;.&quot;],&quot;lang&quot;:[0,&quot;py&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-python/skills/azure-cosmos-db-py&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-cosmos-java&quot;],&quot;description&quot;:[0,&quot;Azure Cosmos DB SDK for Java. NoSQL database operations with global distribution, multi-model support, and reactive patterns.\nTriggers: \&quot;CosmosClient java\&quot;, \&quot;CosmosAsyncClient\&quot;, \&quot;cosmos database java\&quot;, \&quot;cosmosdb java\&quot;, \&quot;document database java\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;java&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-java/skills/azure-cosmos-java&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-cosmos-py&quot;],&quot;description&quot;:[0,&quot;Azure Cosmos DB SDK for Python (NoSQL API). Use for document CRUD, queries, containers, and globally distributed data.\nTriggers: \&quot;cosmos db\&quot;, \&quot;CosmosClient\&quot;, \&quot;container\&quot;, \&quot;document\&quot;, \&quot;NoSQL\&quot;, \&quot;partition key\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;py&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-python/skills/azure-cosmos-py&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-cosmos-rust&quot;],&quot;description&quot;:[0,&quot;Azure Cosmos DB SDK for Rust (NoSQL API). Use for document CRUD, queries, containers, and globally distributed data.\nTriggers: \&quot;cosmos db rust\&quot;, \&quot;CosmosClient rust\&quot;, \&quot;container\&quot;, \&quot;document rust\&quot;, \&quot;NoSQL rust\&quot;, \&quot;partition key\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;rust&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-rust/skills/azure-cosmos-rust&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-cosmos-ts&quot;],&quot;description&quot;:[0,&quot;Azure Cosmos DB JavaScript/TypeScript SDK (@azure/cosmos) for data plane operations. Use for CRUD operations on documents, queries, bulk operations, and container management. Triggers: \&quot;Cosmos DB\&quot;, \&quot;@azure/cosmos\&quot;, \&quot;CosmosClient\&quot;, \&quot;document CRUD\&quot;, \&quot;NoSQL queries\&quot;, \&quot;bulk operations\&quot;, \&quot;partition key\&quot;, \&quot;container.items\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;ts&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-typescript/skills/azure-cosmos-ts&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-data-tables-java&quot;],&quot;description&quot;:[0,&quot;Build table storage applications with Azure Tables SDK for Java. Use when working with Azure Table Storage or Cosmos DB Table API for NoSQL key-value data, schemaless storage, or structured data at scale.&quot;],&quot;lang&quot;:[0,&quot;java&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-java/skills/azure-data-tables-java&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-data-tables-py&quot;],&quot;description&quot;:[0,&quot;Azure Tables SDK for Python (Storage and Cosmos DB). Use for NoSQL key-value storage, entity CRUD, and batch operations.\nTriggers: \&quot;table storage\&quot;, \&quot;TableServiceClient\&quot;, \&quot;TableClient\&quot;, \&quot;entities\&quot;, \&quot;PartitionKey\&quot;, \&quot;RowKey\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;py&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-python/skills/azure-data-tables-py&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-eventgrid-dotnet&quot;],&quot;description&quot;:[0,&quot;Azure Event Grid SDK for .NET. Client library for publishing and consuming events with Azure Event Grid. Use for event-driven architectures, pub/sub messaging, CloudEvents, and EventGridEvents. Triggers: \&quot;Event Grid\&quot;, \&quot;EventGridPublisherClient\&quot;, \&quot;CloudEvent\&quot;, \&quot;EventGridEvent\&quot;, \&quot;publish events .NET\&quot;, \&quot;event-driven\&quot;, \&quot;pub/sub\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;dotnet&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-dotnet/skills/azure-eventgrid-dotnet&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-eventgrid-java&quot;],&quot;description&quot;:[0,&quot;Build event-driven applications with Azure Event Grid SDK for Java. Use when publishing events, implementing pub/sub patterns, or integrating with Azure services via events.&quot;],&quot;lang&quot;:[0,&quot;java&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-java/skills/azure-eventgrid-java&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-eventgrid-py&quot;],&quot;description&quot;:[0,&quot;Azure Event Grid SDK for Python. Use for publishing events, handling CloudEvents, and event-driven architectures.\nTriggers: \&quot;event grid\&quot;, \&quot;EventGridPublisherClient\&quot;, \&quot;CloudEvent\&quot;, \&quot;EventGridEvent\&quot;, \&quot;publish events\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;py&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-python/skills/azure-eventgrid-py&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-eventhub-dotnet&quot;],&quot;description&quot;:[0,&quot;Azure Event Hubs SDK for .NET. Use for high-throughput event streaming: sending events (EventHubProducerClient, EventHubBufferedProducerClient), receiving events (EventProcessorClient with checkpointing), partition management, and real-time data ingestion. Triggers: \&quot;Event Hubs\&quot;, \&quot;event streaming\&quot;, \&quot;EventHubProducerClient\&quot;, \&quot;EventProcessorClient\&quot;, \&quot;send events\&quot;, \&quot;receive events\&quot;, \&quot;checkpointing\&quot;, \&quot;partition\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;dotnet&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-dotnet/skills/azure-eventhub-dotnet&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-eventhub-java&quot;],&quot;description&quot;:[0,&quot;Build real-time streaming applications with Azure Event Hubs SDK for Java. Use when implementing event streaming, high-throughput data ingestion, or building event-driven architectures.&quot;],&quot;lang&quot;:[0,&quot;java&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-java/skills/azure-eventhub-java&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-eventhub-py&quot;],&quot;description&quot;:[0,&quot;Azure Event Hubs SDK for Python streaming. Use for high-throughput event ingestion, producers, consumers, and checkpointing.\nTriggers: \&quot;event hubs\&quot;, \&quot;EventHubProducerClient\&quot;, \&quot;EventHubConsumerClient\&quot;, \&quot;streaming\&quot;, \&quot;partitions\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;py&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-python/skills/azure-eventhub-py&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-eventhub-rust&quot;],&quot;description&quot;:[0,&quot;Azure Event Hubs SDK for Rust. Use for sending and receiving events, streaming data ingestion.\nTriggers: \&quot;event hubs rust\&quot;, \&quot;ProducerClient rust\&quot;, \&quot;ConsumerClient rust\&quot;, \&quot;send event rust\&quot;, \&quot;streaming rust\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;rust&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-rust/skills/azure-eventhub-rust&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-eventhub-ts&quot;],&quot;description&quot;:[0,&quot;Build event streaming applications using Azure Event Hubs SDK for JavaScript (@azure/event-hubs). Use when implementing high-throughput event ingestion, real-time analytics, IoT telemetry, or event-driven architectures with partitioned consumers.&quot;],&quot;lang&quot;:[0,&quot;ts&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-typescript/skills/azure-eventhub-ts&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-identity-dotnet&quot;],&quot;description&quot;:[0,&quot;Azure Identity library for .NET. Authentication library for Azure SDK clients using Microsoft Entra ID. Use for DefaultAzureCredential, managed identity, service principals, and developer credentials. Triggers: \&quot;Azure Identity\&quot;, \&quot;DefaultAzureCredential\&quot;, \&quot;ManagedIdentityCredential\&quot;, \&quot;ClientSecretCredential\&quot;, \&quot;authentication .NET\&quot;, \&quot;Azure auth\&quot;, \&quot;credential chain\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;dotnet&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-dotnet/skills/azure-identity-dotnet&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-identity-java&quot;],&quot;description&quot;:[0,&quot;Azure Identity library for Java authentication with Azure services. Use when implementing DefaultAzureCredential, managed identity, service principal, or any Azure authentication pattern in Java applications.&quot;],&quot;lang&quot;:[0,&quot;java&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-java/skills/azure-identity-java&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-identity-py&quot;],&quot;description&quot;:[0,&quot;Azure Identity SDK for Python authentication with Microsoft Entra ID. Use for DefaultAzureCredential, managed identity, service principals, and token caching.\nTriggers: \&quot;azure-identity\&quot;, \&quot;DefaultAzureCredential\&quot;, \&quot;authentication\&quot;, \&quot;managed identity\&quot;, \&quot;service principal\&quot;, \&quot;credential\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;py&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-python/skills/azure-identity-py&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-identity-rust&quot;],&quot;description&quot;:[0,&quot;Azure Identity SDK for Rust authentication. Use for DeveloperToolsCredential, ManagedIdentityCredential, ClientSecretCredential, and token-based authentication.\nTriggers: \&quot;azure-identity\&quot;, \&quot;DeveloperToolsCredential\&quot;, \&quot;authentication rust\&quot;, \&quot;managed identity rust\&quot;, \&quot;credential rust\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;rust&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-rust/skills/azure-identity-rust&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-identity-ts&quot;],&quot;description&quot;:[0,&quot;Authenticate to Azure services using Azure Identity library for JavaScript (@azure/identity). Use when configuring authentication with DefaultAzureCredential, managed identity, service principals, or interactive browser login.&quot;],&quot;lang&quot;:[0,&quot;ts&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-typescript/skills/azure-identity-ts&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-keyvault-certificates-rust&quot;],&quot;description&quot;:[0,&quot;Azure Key Vault Certificates SDK for Rust. Use for creating, importing, and managing certificates.\nTriggers: \&quot;keyvault certificates rust\&quot;, \&quot;CertificateClient rust\&quot;, \&quot;create certificate rust\&quot;, \&quot;import certificate rust\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;rust&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-rust/skills/azure-keyvault-certificates-rust&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-keyvault-keys-rust&quot;],&quot;description&quot;:[0,&quot;Azure Key Vault Keys SDK for Rust. Use for creating, managing, and using cryptographic keys.\nTriggers: \&quot;keyvault keys rust\&quot;, \&quot;KeyClient rust\&quot;, \&quot;create key rust\&quot;, \&quot;encrypt rust\&quot;, \&quot;sign rust\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;rust&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-rust/skills/azure-keyvault-keys-rust&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-keyvault-keys-ts&quot;],&quot;description&quot;:[0,&quot;Manage cryptographic keys using Azure Key Vault Keys SDK for JavaScript (@azure/keyvault-keys). Use when creating, encrypting/decrypting, signing, or rotating keys.&quot;],&quot;lang&quot;:[0,&quot;ts&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-typescript/skills/azure-keyvault-keys-ts&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-keyvault-py&quot;],&quot;description&quot;:[0,&quot;Azure Key Vault SDK for Python. Use for secrets, keys, and certificates management with secure storage.\nTriggers: \&quot;key vault\&quot;, \&quot;SecretClient\&quot;, \&quot;KeyClient\&quot;, \&quot;CertificateClient\&quot;, \&quot;secrets\&quot;, \&quot;encryption keys\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;py&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-python/skills/azure-keyvault-py&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-keyvault-secrets-rust&quot;],&quot;description&quot;:[0,&quot;Azure Key Vault Secrets SDK for Rust. Use for storing and retrieving secrets, passwords, and API keys.\nTriggers: \&quot;keyvault secrets rust\&quot;, \&quot;SecretClient rust\&quot;, \&quot;get secret rust\&quot;, \&quot;set secret rust\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;rust&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-rust/skills/azure-keyvault-secrets-rust&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-keyvault-secrets-ts&quot;],&quot;description&quot;:[0,&quot;Manage secrets using Azure Key Vault Secrets SDK for JavaScript (@azure/keyvault-secrets). Use when storing and retrieving application secrets or configuration values.&quot;],&quot;lang&quot;:[0,&quot;ts&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-typescript/skills/azure-keyvault-secrets-ts&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-maps-search-dotnet&quot;],&quot;description&quot;:[0,&quot;Azure Maps SDK for .NET. Location-based services including geocoding, routing, rendering, geolocation, and weather. Use for address search, directions, map tiles, IP geolocation, and weather data. Triggers: \&quot;Azure Maps\&quot;, \&quot;MapsSearchClient\&quot;, \&quot;MapsRoutingClient\&quot;, \&quot;MapsRenderingClient\&quot;, \&quot;geocoding .NET\&quot;, \&quot;route directions\&quot;, \&quot;map tiles\&quot;, \&quot;geolocation\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;dotnet&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-dotnet/skills/azure-maps-search-dotnet&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-messaging-webpubsub-java&quot;],&quot;description&quot;:[0,&quot;Build real-time web applications with Azure Web PubSub SDK for Java. Use when implementing WebSocket-based messaging, live updates, chat applications, or server-to-client push notifications.&quot;],&quot;lang&quot;:[0,&quot;java&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-java/skills/azure-messaging-webpubsub-java&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-messaging-webpubsubservice-py&quot;],&quot;description&quot;:[0,&quot;Azure Web PubSub Service SDK for Python. Use for real-time messaging, WebSocket connections, and pub/sub patterns.\nTriggers: \&quot;azure-messaging-webpubsubservice\&quot;, \&quot;WebPubSubServiceClient\&quot;, \&quot;real-time\&quot;, \&quot;WebSocket\&quot;, \&quot;pub/sub\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;py&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-python/skills/azure-messaging-webpubsubservice-py&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-mgmt-apicenter-dotnet&quot;],&quot;description&quot;:[0,&quot;Azure API Center SDK for .NET. Centralized API inventory management with governance, versioning, and discovery. Use for creating API services, workspaces, APIs, versions, definitions, environments, deployments, and metadata schemas. Triggers: \&quot;API Center\&quot;, \&quot;ApiCenterService\&quot;, \&quot;ApiCenterWorkspace\&quot;, \&quot;ApiCenterApi\&quot;, \&quot;API inventory\&quot;, \&quot;API governance\&quot;, \&quot;API versioning\&quot;, \&quot;API catalog\&quot;, \&quot;API discovery\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;dotnet&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-dotnet/skills/azure-mgmt-apicenter-dotnet&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-mgmt-apicenter-py&quot;],&quot;description&quot;:[0,&quot;Azure API Center Management SDK for Python. Use for managing API inventory, metadata, and governance across your organization.\nTriggers: \&quot;azure-mgmt-apicenter\&quot;, \&quot;ApiCenterMgmtClient\&quot;, \&quot;API Center\&quot;, \&quot;API inventory\&quot;, \&quot;API governance\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;py&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-python/skills/azure-mgmt-apicenter-py&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-mgmt-apimanagement-dotnet&quot;],&quot;description&quot;:[0,&quot;Azure Resource Manager SDK for API Management in .NET. Use for MANAGEMENT PLANE operations: creating/managing APIM services, APIs, products, subscriptions, policies, users, groups, gateways, and backends via Azure Resource Manager. Triggers: \&quot;API Management\&quot;, \&quot;APIM service\&quot;, \&quot;create APIM\&quot;, \&quot;manage APIs\&quot;, \&quot;ApiManagementServiceResource\&quot;, \&quot;API policies\&quot;, \&quot;APIM products\&quot;, \&quot;APIM subscriptions\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;dotnet&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-dotnet/skills/azure-mgmt-apimanagement-dotnet&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-mgmt-apimanagement-py&quot;],&quot;description&quot;:[0,&quot;Azure API Management SDK for Python. Use for managing APIM services, APIs, products, subscriptions, and policies.\nTriggers: \&quot;azure-mgmt-apimanagement\&quot;, \&quot;ApiManagementClient\&quot;, \&quot;APIM\&quot;, \&quot;API gateway\&quot;, \&quot;API Management\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;py&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-python/skills/azure-mgmt-apimanagement-py&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-mgmt-applicationinsights-dotnet&quot;],&quot;description&quot;:[0,&quot;Azure Application Insights SDK for .NET. Application performance monitoring and observability resource management. Use for creating Application Insights components, web tests, workbooks, analytics items, and API keys. Triggers: \&quot;Application Insights\&quot;, \&quot;ApplicationInsights\&quot;, \&quot;App Insights\&quot;, \&quot;APM\&quot;, \&quot;application monitoring\&quot;, \&quot;web tests\&quot;, \&quot;availability tests\&quot;, \&quot;workbooks\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;dotnet&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-dotnet/skills/azure-mgmt-applicationinsights-dotnet&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-mgmt-arizeaiobservabilityeval-dotnet&quot;],&quot;description&quot;:[0,&quot;Azure Resource Manager SDK for Arize AI Observability and Evaluation (.NET). Use when managing Arize AI organizations \non Azure via Azure Marketplace, creating/updating/deleting Arize resources, or integrating Arize ML observability \ninto .NET applications. Triggers: \&quot;Arize AI\&quot;, \&quot;ML observability\&quot;, \&quot;ArizeAIObservabilityEval\&quot;, \&quot;Arize organization\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;dotnet&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-dotnet/skills/azure-mgmt-arizeaiobservabilityeval-dotnet&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-mgmt-botservice-dotnet&quot;],&quot;description&quot;:[0,&quot;Azure Resource Manager SDK for Bot Service in .NET. Management plane operations for creating and managing Azure Bot resources, channels (Teams, DirectLine, Slack), and connection settings. Triggers: \&quot;Bot Service\&quot;, \&quot;BotResource\&quot;, \&quot;Azure Bot\&quot;, \&quot;DirectLine channel\&quot;, \&quot;Teams channel\&quot;, \&quot;bot management .NET\&quot;, \&quot;create bot\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;dotnet&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-dotnet/skills/azure-mgmt-botservice-dotnet&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-mgmt-botservice-py&quot;],&quot;description&quot;:[0,&quot;Azure Bot Service Management SDK for Python. Use for creating, managing, and configuring Azure Bot Service resources.\nTriggers: \&quot;azure-mgmt-botservice\&quot;, \&quot;AzureBotService\&quot;, \&quot;bot management\&quot;, \&quot;conversational AI\&quot;, \&quot;bot channels\&quot;.&quot;],&quot;lang&quot;:[0,&quot;py&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-python/skills/azure-mgmt-botservice-py&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-mgmt-fabric-dotnet&quot;],&quot;description&quot;:[0,&quot;Azure Resource Manager SDK for Fabric in .NET. Use for MANAGEMENT PLANE operations: provisioning, scaling, suspending/resuming Microsoft Fabric capacities, checking name availability, and listing SKUs via Azure Resource Manager. Triggers: \&quot;Fabric capacity\&quot;, \&quot;create capacity\&quot;, \&quot;suspend capacity\&quot;, \&quot;resume capacity\&quot;, \&quot;Fabric SKU\&quot;, \&quot;provision Fabric\&quot;, \&quot;ARM Fabric\&quot;, \&quot;FabricCapacityResource\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;dotnet&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-dotnet/skills/azure-mgmt-fabric-dotnet&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-mgmt-fabric-py&quot;],&quot;description&quot;:[0,&quot;Azure Fabric Management SDK for Python. Use for managing Microsoft Fabric capacities and resources.\nTriggers: \&quot;azure-mgmt-fabric\&quot;, \&quot;FabricMgmtClient\&quot;, \&quot;Fabric capacity\&quot;, \&quot;Microsoft Fabric\&quot;, \&quot;Power BI capacity\&quot;.&quot;],&quot;lang&quot;:[0,&quot;py&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-python/skills/azure-mgmt-fabric-py&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-mgmt-mongodbatlas-dotnet&quot;],&quot;description&quot;:[0,&quot;Manage MongoDB Atlas Organizations as Azure ARM resources using Azure.ResourceManager.MongoDBAtlas SDK. Use when creating, updating, listing, or deleting MongoDB Atlas organizations through Azure Marketplace integration. This SDK manages the Azure-side organization resource, not Atlas clusters/databases directly.&quot;],&quot;lang&quot;:[0,&quot;dotnet&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-dotnet/skills/azure-mgmt-mongodbatlas-dotnet&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-mgmt-weightsandbiases-dotnet&quot;],&quot;description&quot;:[0,&quot;Azure Weights &amp; Biases SDK for .NET. ML experiment tracking and model management via Azure Marketplace. Use for creating W&amp;B instances, managing SSO, marketplace integration, and ML observability. Triggers: \&quot;Weights and Biases\&quot;, \&quot;W&amp;B\&quot;, \&quot;WeightsAndBiases\&quot;, \&quot;ML experiment tracking\&quot;, \&quot;model registry\&quot;, \&quot;experiment management\&quot;, \&quot;wandb\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;dotnet&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-dotnet/skills/azure-mgmt-weightsandbiases-dotnet&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-microsoft-playwright-testing-ts&quot;],&quot;description&quot;:[0,&quot;Run Playwright tests at scale using Azure Playwright Workspaces (formerly Microsoft Playwright Testing). Use when scaling browser tests across cloud-hosted browsers, integrating with CI/CD pipelines, or publishing test results to the Azure portal.&quot;],&quot;lang&quot;:[0,&quot;ts&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-typescript/skills/azure-microsoft-playwright-testing-ts&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-monitor-ingestion-java&quot;],&quot;description&quot;:[0,&quot;Azure Monitor Ingestion SDK for Java. Send custom logs to Azure Monitor via Data Collection Rules (DCR) and Data Collection Endpoints (DCE).\nTriggers: \&quot;LogsIngestionClient java\&quot;, \&quot;azure monitor ingestion java\&quot;, \&quot;custom logs java\&quot;, \&quot;DCR java\&quot;, \&quot;data collection rule java\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;java&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-java/skills/azure-monitor-ingestion-java&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-monitor-ingestion-py&quot;],&quot;description&quot;:[0,&quot;Azure Monitor Ingestion SDK for Python. Use for sending custom logs to Log Analytics workspace via Logs Ingestion API.\nTriggers: \&quot;azure-monitor-ingestion\&quot;, \&quot;LogsIngestionClient\&quot;, \&quot;custom logs\&quot;, \&quot;DCR\&quot;, \&quot;data collection rule\&quot;, \&quot;Log Analytics\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;py&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-python/skills/azure-monitor-ingestion-py&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-monitor-opentelemetry-exporter-java&quot;],&quot;description&quot;:[0,&quot;Azure Monitor OpenTelemetry Exporter for Java. Export OpenTelemetry traces, metrics, and logs to Azure Monitor/Application Insights.\nTriggers: \&quot;AzureMonitorExporter java\&quot;, \&quot;opentelemetry azure java\&quot;, \&quot;application insights java otel\&quot;, \&quot;azure monitor tracing java\&quot;.\nNote: This package is DEPRECATED. Migrate to azure-monitor-opentelemetry-autoconfigure.\n&quot;],&quot;lang&quot;:[0,&quot;java&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-java/skills/azure-monitor-opentelemetry-exporter-java&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-monitor-opentelemetry-exporter-py&quot;],&quot;description&quot;:[0,&quot;Azure Monitor OpenTelemetry Exporter for Python. Use for low-level OpenTelemetry export to Application Insights.\nTriggers: \&quot;azure-monitor-opentelemetry-exporter\&quot;, \&quot;AzureMonitorTraceExporter\&quot;, \&quot;AzureMonitorMetricExporter\&quot;, \&quot;AzureMonitorLogExporter\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;py&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-python/skills/azure-monitor-opentelemetry-exporter-py&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-monitor-opentelemetry-py&quot;],&quot;description&quot;:[0,&quot;Azure Monitor OpenTelemetry Distro for Python. Use for one-line Application Insights setup with auto-instrumentation.\nTriggers: \&quot;azure-monitor-opentelemetry\&quot;, \&quot;configure_azure_monitor\&quot;, \&quot;Application Insights\&quot;, \&quot;OpenTelemetry distro\&quot;, \&quot;auto-instrumentation\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;py&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-python/skills/azure-monitor-opentelemetry-py&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-monitor-opentelemetry-ts&quot;],&quot;description&quot;:[0,&quot;Instrument applications with Azure Monitor and OpenTelemetry for JavaScript (@azure/monitor-opentelemetry). Use when adding distributed tracing, metrics, and logs to Node.js applications with Application Insights.&quot;],&quot;lang&quot;:[0,&quot;ts&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-typescript/skills/azure-monitor-opentelemetry-ts&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-monitor-query-java&quot;],&quot;description&quot;:[0,&quot;Azure Monitor Query SDK for Java. Execute Kusto queries against Log Analytics workspaces and query metrics from Azure resources.\nTriggers: \&quot;LogsQueryClient java\&quot;, \&quot;MetricsQueryClient java\&quot;, \&quot;kusto query java\&quot;, \&quot;log analytics java\&quot;, \&quot;azure monitor query java\&quot;.\nNote: This package is deprecated. Migrate to azure-monitor-query-logs and azure-monitor-query-metrics.\n&quot;],&quot;lang&quot;:[0,&quot;java&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-java/skills/azure-monitor-query-java&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-monitor-query-py&quot;],&quot;description&quot;:[0,&quot;Azure Monitor Query SDK for Python. Use for querying Log Analytics workspaces and Azure Monitor metrics.\nTriggers: \&quot;azure-monitor-query\&quot;, \&quot;LogsQueryClient\&quot;, \&quot;MetricsQueryClient\&quot;, \&quot;Log Analytics\&quot;, \&quot;Kusto queries\&quot;, \&quot;Azure metrics\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;py&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-python/skills/azure-monitor-query-py&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-postgres-ts&quot;],&quot;description&quot;:[0,&quot;Connect to Azure Database for PostgreSQL Flexible Server from Node.js/TypeScript using the pg (node-postgres) package. Use for PostgreSQL queries, connection pooling, transactions, and Microsoft Entra ID (passwordless) authentication. Triggers: \&quot;PostgreSQL\&quot;, \&quot;postgres\&quot;, \&quot;pg client\&quot;, \&quot;node-postgres\&quot;, \&quot;Azure PostgreSQL connection\&quot;, \&quot;PostgreSQL TypeScript\&quot;, \&quot;pg Pool\&quot;, \&quot;passwordless postgres\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;ts&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-typescript/skills/azure-postgres-ts&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-resource-manager-cosmosdb-dotnet&quot;],&quot;description&quot;:[0,&quot;Azure Resource Manager SDK for Cosmos DB in .NET. Use for MANAGEMENT PLANE operations: creating/managing Cosmos DB accounts, databases, containers, throughput settings, and RBAC via Azure Resource Manager. NOT for data plane operations (CRUD on documents) - use Microsoft.Azure.Cosmos for that. Triggers: \&quot;Cosmos DB account\&quot;, \&quot;create Cosmos account\&quot;, \&quot;manage Cosmos resources\&quot;, \&quot;ARM Cosmos\&quot;, \&quot;CosmosDBAccountResource\&quot;, \&quot;provision Cosmos DB\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;dotnet&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-dotnet/skills/azure-resource-manager-cosmosdb-dotnet&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-resource-manager-durabletask-dotnet&quot;],&quot;description&quot;:[0,&quot;Azure Resource Manager SDK for Durable Task Scheduler in .NET. Use for MANAGEMENT PLANE operations: creating/managing Durable Task Schedulers, Task Hubs, and retention policies via Azure Resource Manager. Triggers: \&quot;Durable Task Scheduler\&quot;, \&quot;create scheduler\&quot;, \&quot;task hub\&quot;, \&quot;DurableTaskSchedulerResource\&quot;, \&quot;provision Durable Task\&quot;, \&quot;orchestration scheduler\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;dotnet&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-dotnet/skills/azure-resource-manager-durabletask-dotnet&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-resource-manager-mysql-dotnet&quot;],&quot;description&quot;:[0,&quot;Azure MySQL Flexible Server SDK for .NET. Database management for MySQL Flexible Server deployments. Use for creating servers, databases, firewall rules, configurations, backups, and high availability. Triggers: \&quot;MySQL\&quot;, \&quot;MySqlFlexibleServer\&quot;, \&quot;MySQL Flexible Server\&quot;, \&quot;Azure Database for MySQL\&quot;, \&quot;MySQL database management\&quot;, \&quot;MySQL firewall\&quot;, \&quot;MySQL backup\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;dotnet&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-dotnet/skills/azure-resource-manager-mysql-dotnet&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-resource-manager-playwright-dotnet&quot;],&quot;description&quot;:[0,&quot;Azure Resource Manager SDK for Microsoft Playwright Testing in .NET. Use for MANAGEMENT PLANE operations: creating/managing Playwright Testing workspaces, checking name availability, and managing workspace quotas via Azure Resource Manager. NOT for running Playwright tests - use Azure.Developer.MicrosoftPlaywrightTesting.NUnit for that. Triggers: \&quot;Playwright workspace\&quot;, \&quot;create Playwright Testing workspace\&quot;, \&quot;manage Playwright resources\&quot;, \&quot;ARM Playwright\&quot;, \&quot;PlaywrightWorkspaceResource\&quot;, \&quot;provision Playwright Testing\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;dotnet&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-dotnet/skills/azure-resource-manager-playwright-dotnet&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-resource-manager-postgresql-dotnet&quot;],&quot;description&quot;:[0,&quot;Azure PostgreSQL Flexible Server SDK for .NET. Database management for PostgreSQL Flexible Server deployments. Use for creating servers, databases, firewall rules, configurations, backups, and high availability. Triggers: \&quot;PostgreSQL\&quot;, \&quot;PostgreSqlFlexibleServer\&quot;, \&quot;PostgreSQL Flexible Server\&quot;, \&quot;Azure Database for PostgreSQL\&quot;, \&quot;PostgreSQL database management\&quot;, \&quot;PostgreSQL firewall\&quot;, \&quot;PostgreSQL backup\&quot;, \&quot;Postgres\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;dotnet&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-dotnet/skills/azure-resource-manager-postgresql-dotnet&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-resource-manager-redis-dotnet&quot;],&quot;description&quot;:[0,&quot;Azure Resource Manager SDK for Redis in .NET. Use for MANAGEMENT PLANE operations: creating/managing Azure Cache for Redis instances, firewall rules, access keys, patch schedules, linked servers (geo-replication), and private endpoints via Azure Resource Manager. NOT for data plane operations (get/set keys, pub/sub) - use StackExchange.Redis for that. Triggers: \&quot;Redis cache\&quot;, \&quot;create Redis\&quot;, \&quot;manage Redis\&quot;, \&quot;ARM Redis\&quot;, \&quot;RedisResource\&quot;, \&quot;provision Redis\&quot;, \&quot;Azure Cache for Redis\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;dotnet&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-dotnet/skills/azure-resource-manager-redis-dotnet&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-resource-manager-sql-dotnet&quot;],&quot;description&quot;:[0,&quot;Azure Resource Manager SDK for Azure SQL in .NET. Use for MANAGEMENT PLANE operations: creating/managing SQL servers, databases, elastic pools, firewall rules, and failover groups via Azure Resource Manager. NOT for data plane operations (executing queries) - use Microsoft.Data.SqlClient for that. Triggers: \&quot;SQL server\&quot;, \&quot;create SQL database\&quot;, \&quot;manage SQL resources\&quot;, \&quot;ARM SQL\&quot;, \&quot;SqlServerResource\&quot;, \&quot;provision Azure SQL\&quot;, \&quot;elastic pool\&quot;, \&quot;firewall rule\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;dotnet&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-dotnet/skills/azure-resource-manager-sql-dotnet&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-search-documents-dotnet&quot;],&quot;description&quot;:[0,&quot;Azure AI Search SDK for .NET (Azure.Search.Documents). Use for building search applications with full-text, vector, semantic, and hybrid search. Covers SearchClient (queries, document CRUD), SearchIndexClient (index management), and SearchIndexerClient (indexers, skillsets). Triggers: \&quot;Azure Search .NET\&quot;, \&quot;SearchClient\&quot;, \&quot;SearchIndexClient\&quot;, \&quot;vector search C#\&quot;, \&quot;semantic search .NET\&quot;, \&quot;hybrid search\&quot;, \&quot;Azure.Search.Documents\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;dotnet&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-dotnet/skills/azure-search-documents-dotnet&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-search-documents-py&quot;],&quot;description&quot;:[0,&quot;Azure AI Search SDK for Python. Use for vector search, hybrid search, semantic ranking, indexing, and skillsets.\nTriggers: \&quot;azure-search-documents\&quot;, \&quot;SearchClient\&quot;, \&quot;SearchIndexClient\&quot;, \&quot;vector search\&quot;, \&quot;hybrid search\&quot;, \&quot;semantic search\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;py&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-python/skills/azure-search-documents-py&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-search-documents-ts&quot;],&quot;description&quot;:[0,&quot;Build search applications using Azure AI Search SDK for JavaScript (@azure/search-documents). Use when creating/managing indexes, implementing vector/hybrid search, semantic ranking, or building agentic retrieval with knowledge bases.&quot;],&quot;lang&quot;:[0,&quot;ts&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-typescript/skills/azure-search-documents-ts&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-security-keyvault-keys-dotnet&quot;],&quot;description&quot;:[0,&quot;Azure Key Vault Keys SDK for .NET. Client library for managing cryptographic keys in Azure Key Vault and Managed HSM. Use for key creation, rotation, encryption, decryption, signing, and verification. Triggers: \&quot;Key Vault keys\&quot;, \&quot;KeyClient\&quot;, \&quot;CryptographyClient\&quot;, \&quot;RSA key\&quot;, \&quot;EC key\&quot;, \&quot;encrypt decrypt .NET\&quot;, \&quot;key rotation\&quot;, \&quot;HSM\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;dotnet&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-dotnet/skills/azure-security-keyvault-keys-dotnet&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-security-keyvault-keys-java&quot;],&quot;description&quot;:[0,&quot;Azure Key Vault Keys Java SDK for cryptographic key management. Use when creating, managing, or using RSA/EC keys, performing encrypt/decrypt/sign/verify operations, or working with HSM-backed keys.&quot;],&quot;lang&quot;:[0,&quot;java&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-java/skills/azure-security-keyvault-keys-java&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-security-keyvault-secrets-java&quot;],&quot;description&quot;:[0,&quot;Azure Key Vault Secrets Java SDK for secret management. Use when storing, retrieving, or managing passwords, API keys, connection strings, or other sensitive configuration data.&quot;],&quot;lang&quot;:[0,&quot;java&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-java/skills/azure-security-keyvault-secrets-java&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-servicebus-dotnet&quot;],&quot;description&quot;:[0,&quot;Azure Service Bus SDK for .NET. Enterprise messaging with queues, topics, subscriptions, and sessions. Use for reliable message delivery, pub/sub patterns, dead letter handling, and background processing. Triggers: \&quot;Service Bus\&quot;, \&quot;ServiceBusClient\&quot;, \&quot;ServiceBusSender\&quot;, \&quot;ServiceBusReceiver\&quot;, \&quot;ServiceBusProcessor\&quot;, \&quot;message queue\&quot;, \&quot;pub/sub .NET\&quot;, \&quot;dead letter queue\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;dotnet&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-dotnet/skills/azure-servicebus-dotnet&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-servicebus-py&quot;],&quot;description&quot;:[0,&quot;Azure Service Bus SDK for Python messaging. Use for queues, topics, subscriptions, and enterprise messaging patterns.\nTriggers: \&quot;service bus\&quot;, \&quot;ServiceBusClient\&quot;, \&quot;queue\&quot;, \&quot;topic\&quot;, \&quot;subscription\&quot;, \&quot;message broker\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;py&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-python/skills/azure-servicebus-py&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-servicebus-ts&quot;],&quot;description&quot;:[0,&quot;Build messaging applications using Azure Service Bus SDK for JavaScript (@azure/service-bus). Use when implementing queues, topics/subscriptions, message sessions, dead-letter handling, or enterprise messaging patterns.&quot;],&quot;lang&quot;:[0,&quot;ts&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-typescript/skills/azure-servicebus-ts&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-speech-to-text-rest-py&quot;],&quot;description&quot;:[0,&quot;Azure Speech to Text REST API for short audio (Python). Use for simple speech recognition of audio files up to 60 seconds without the Speech SDK.\nTriggers: \&quot;speech to text REST\&quot;, \&quot;short audio transcription\&quot;, \&quot;speech recognition REST API\&quot;, \&quot;STT REST\&quot;, \&quot;recognize speech REST\&quot;.\nDO NOT USE FOR: Long audio (&gt;60 seconds), real-time streaming, batch transcription, custom speech models, speech translation. Use Speech SDK or Batch Transcription API instead.&quot;],&quot;lang&quot;:[0,&quot;py&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-python/skills/azure-speech-to-text-rest-py&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-storage-blob-java&quot;],&quot;description&quot;:[0,&quot;Build blob storage applications with Azure Storage Blob SDK for Java. Use when uploading, downloading, or managing files in Azure Blob Storage, working with containers, or implementing streaming data operations.&quot;],&quot;lang&quot;:[0,&quot;java&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-java/skills/azure-storage-blob-java&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-storage-blob-py&quot;],&quot;description&quot;:[0,&quot;Azure Blob Storage SDK for Python. Use for uploading, downloading, listing blobs, managing containers, and blob lifecycle.\nTriggers: \&quot;blob storage\&quot;, \&quot;BlobServiceClient\&quot;, \&quot;ContainerClient\&quot;, \&quot;BlobClient\&quot;, \&quot;upload blob\&quot;, \&quot;download blob\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;py&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-python/skills/azure-storage-blob-py&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-storage-blob-rust&quot;],&quot;description&quot;:[0,&quot;Azure Blob Storage SDK for Rust. Use for uploading, downloading, and managing blobs and containers.\nTriggers: \&quot;blob storage rust\&quot;, \&quot;BlobClient rust\&quot;, \&quot;upload blob rust\&quot;, \&quot;download blob rust\&quot;, \&quot;container rust\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;rust&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-rust/skills/azure-storage-blob-rust&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-storage-blob-ts&quot;],&quot;description&quot;:[0,&quot;Azure Blob Storage JavaScript/TypeScript SDK (@azure/storage-blob) for blob operations. Use for uploading, downloading, listing, and managing blobs and containers. Supports block blobs, append blobs, page blobs, SAS tokens, and streaming. Triggers: \&quot;blob storage\&quot;, \&quot;@azure/storage-blob\&quot;, \&quot;BlobServiceClient\&quot;, \&quot;ContainerClient\&quot;, \&quot;upload blob\&quot;, \&quot;download blob\&quot;, \&quot;SAS token\&quot;, \&quot;block blob\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;ts&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-typescript/skills/azure-storage-blob-ts&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-storage-file-datalake-py&quot;],&quot;description&quot;:[0,&quot;Azure Data Lake Storage Gen2 SDK for Python. Use for hierarchical file systems, big data analytics, and file/directory operations.\nTriggers: \&quot;data lake\&quot;, \&quot;DataLakeServiceClient\&quot;, \&quot;FileSystemClient\&quot;, \&quot;ADLS Gen2\&quot;, \&quot;hierarchical namespace\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;py&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-python/skills/azure-storage-file-datalake-py&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-storage-file-share-py&quot;],&quot;description&quot;:[0,&quot;Azure Storage File Share SDK for Python. Use for SMB file shares, directories, and file operations in the cloud.\nTriggers: \&quot;azure-storage-file-share\&quot;, \&quot;ShareServiceClient\&quot;, \&quot;ShareClient\&quot;, \&quot;file share\&quot;, \&quot;SMB\&quot;.&quot;],&quot;lang&quot;:[0,&quot;py&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-python/skills/azure-storage-file-share-py&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-storage-file-share-ts&quot;],&quot;description&quot;:[0,&quot;Azure File Share JavaScript/TypeScript SDK (@azure/storage-file-share) for SMB file share operations. Use for creating shares, managing directories, uploading/downloading files, and handling file metadata. Supports Azure Files SMB protocol scenarios. Triggers: \&quot;file share\&quot;, \&quot;@azure/storage-file-share\&quot;, \&quot;ShareServiceClient\&quot;, \&quot;ShareClient\&quot;, \&quot;SMB\&quot;, \&quot;Azure Files\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;ts&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-typescript/skills/azure-storage-file-share-ts&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-storage-queue-py&quot;],&quot;description&quot;:[0,&quot;Azure Queue Storage SDK for Python. Use for reliable message queuing, task distribution, and asynchronous processing.\nTriggers: \&quot;queue storage\&quot;, \&quot;QueueServiceClient\&quot;, \&quot;QueueClient\&quot;, \&quot;message queue\&quot;, \&quot;dequeue\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;py&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-python/skills/azure-storage-queue-py&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-storage-queue-ts&quot;],&quot;description&quot;:[0,&quot;Azure Queue Storage JavaScript/TypeScript SDK (@azure/storage-queue) for message queue operations. Use for sending, receiving, peeking, and deleting messages in queues. Supports visibility timeout, message encoding, and batch operations. Triggers: \&quot;queue storage\&quot;, \&quot;@azure/storage-queue\&quot;, \&quot;QueueServiceClient\&quot;, \&quot;QueueClient\&quot;, \&quot;send message\&quot;, \&quot;receive message\&quot;, \&quot;dequeue\&quot;, \&quot;visibility timeout\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;ts&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-typescript/skills/azure-storage-queue-ts&quot;]}],[0,{&quot;name&quot;:[0,&quot;azure-web-pubsub-ts&quot;],&quot;description&quot;:[0,&quot;Build real-time messaging applications using Azure Web PubSub SDKs for JavaScript (@azure/web-pubsub, @azure/web-pubsub-client). Use when implementing WebSocket-based real-time features, pub/sub messaging, group chat, or live notifications.&quot;],&quot;lang&quot;:[0,&quot;ts&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-typescript/skills/azure-web-pubsub-ts&quot;]}],[0,{&quot;name&quot;:[0,&quot;cloud-solution-architect&quot;],&quot;description&quot;:[0,&quot;Transform the agent into a Cloud Solution Architect following Azure Architecture Center best practices. Use when designing cloud architectures, reviewing system designs, selecting architecture styles, applying cloud design patterns, making technology choices, or conducting Well-Architected Framework reviews.&quot;],&quot;lang&quot;:[0,&quot;core&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/skills/cloud-solution-architect&quot;]}],[0,{&quot;name&quot;:[0,&quot;continual-learning&quot;],&quot;description&quot;:[0,&quot;Guide for implementing continual learning in AI coding agents — hooks, memory scoping, reflection patterns. Use when setting up learning infrastructure for agents.&quot;],&quot;lang&quot;:[0,&quot;core&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/skills/continual-learning&quot;]}],[0,{&quot;name&quot;:[0,&quot;copilot-sdk&quot;],&quot;description&quot;:[0,&quot;Build applications powered by GitHub Copilot using the Copilot SDK. Use when creating programmatic integrations with Copilot across Node.js/TypeScript, Python, Go, or .NET. Covers session management, custom tools, streaming, hooks, MCP servers, BYOK providers, session persistence, custom agents, skills, and deployment patterns. Requires GitHub Copilot CLI installed and a GitHub Copilot subscription (unless using BYOK).&quot;],&quot;lang&quot;:[0,&quot;core&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/skills/copilot-sdk&quot;]}],[0,{&quot;name&quot;:[0,&quot;entra-agent-id&quot;],&quot;description&quot;:[0,&quot;Microsoft Entra Agent ID (preview) for creating OAuth2-capable AI agent identities via Microsoft Graph beta API.\nCovers Agent Identity Blueprints, BlueprintPrincipals, Agent Identities, required permissions, sponsors, and Workload Identity Federation.\nIncludes Microsoft Entra SDK for AgentID (containerized sidecar) for polyglot agent authentication (Docker/Kubernetes), 3P agent integration, autonomous and interactive agent patterns.\nTriggers: \&quot;agent identity\&quot;, \&quot;agent id\&quot;, \&quot;Agent Identity Blueprint\&quot;, \&quot;BlueprintPrincipal\&quot;, \&quot;entra agent\&quot;, \&quot;agent identity provisioning\&quot;, \&quot;Graph agent identity\&quot;, \&quot;entra sidecar\&quot;, \&quot;agent id sidecar\&quot;, \&quot;auth sidecar\&quot;, \&quot;3P agent\&quot;, \&quot;third-party agent identity\&quot;, \&quot;polyglot agent auth\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;core&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/skills/entra-agent-id&quot;]}],[0,{&quot;name&quot;:[0,&quot;fastapi-router-py&quot;],&quot;description&quot;:[0,&quot;Create FastAPI routers with CRUD operations, authentication dependencies, and proper response models. Use when building REST API endpoints, creating new routes, implementing CRUD operations, or adding authenticated endpoints in FastAPI applications.&quot;],&quot;lang&quot;:[0,&quot;py&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-python/skills/fastapi-router-py&quot;]}],[0,{&quot;name&quot;:[0,&quot;frontend-design-review&quot;],&quot;description&quot;:[0,&quot;Review and create distinctive, production-grade frontend interfaces with high design quality and design system compliance. Evaluates using three pillars: frictionless insight-to-action, quality craft, and trustworthy building. USE FOR: PR reviews, design reviews, accessibility audits, design system compliance checks, creative frontend design, UI code review, component reviews, responsive design checks, theme testing, and creating memorable UI. DO NOT USE FOR: Backend API reviews, database schema reviews, infrastructure or DevOps work, pure business logic without UI, or non-frontend code.\n&quot;],&quot;lang&quot;:[0,&quot;core&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/skills/frontend-design-review&quot;]}],[0,{&quot;name&quot;:[0,&quot;frontend-ui-dark-ts&quot;],&quot;description&quot;:[0,&quot;Build dark-themed React applications using Tailwind CSS with custom theming, glassmorphism effects, and Framer Motion animations. Use when creating dashboards, admin panels, or data-rich interfaces with a refined dark aesthetic.&quot;],&quot;lang&quot;:[0,&quot;ts&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-typescript/skills/frontend-ui-dark-ts&quot;]}],[0,{&quot;name&quot;:[0,&quot;github-issue-creator&quot;],&quot;description&quot;:[0,&quot;Convert raw notes, error logs, voice dictation, or screenshots into crisp GitHub-flavored markdown issue reports. Use when the user pastes bug info, error messages, or informal descriptions and wants a structured GitHub issue. Supports images/GIFs for visual evidence.&quot;],&quot;lang&quot;:[0,&quot;core&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/skills/github-issue-creator&quot;]}],[0,{&quot;name&quot;:[0,&quot;hosted-agents-v2-py&quot;],&quot;description&quot;:[0,&quot;Build Microsoft Foundry hosted agents on the refreshed public preview across Python and C#.\nUse when creating containerized agents with Responses or Invocations protocols, Agent Framework,\nLangGraph, BYO/custom code, agent.yaml, azd ai agent, or AIProjectClient HostedAgentDefinition.\nTriggers: \&quot;hosted agent\&quot;, \&quot;Foundry hosted agent\&quot;, \&quot;Responses protocol\&quot;, \&quot;Invocations protocol\&quot;,\n\&quot;ResponsesAgentServerHost\&quot;, \&quot;InvocationAgentServerHost\&quot;, \&quot;ResponsesHostServer\&quot;,\n\&quot;HostedAgentDefinition\&quot;, \&quot;agent.yaml\&quot;, \&quot;azd ai agent\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;py&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-python/skills/hosted-agents-v2-py&quot;]}],[0,{&quot;name&quot;:[0,&quot;kql&quot;],&quot;description&quot;:[0,&quot;KQL language expertise for writing correct, efficient Kusto Query Language queries. Covers syntax gotchas, join patterns, dynamic types, datetime pitfalls, regex patterns, serialization, memory management, result-size discipline, and advanced functions (geo, vector, graph). USE THIS SKILL whenever writing, debugging, or reviewing KQL queries — even simple ones — because the gotchas section prevents the most common errors that waste tool calls and cause expensive retry cascades. Trigger on: KQL, Kusto, ADX, Azure Data Explorer, Fabric Real-Time Intelligence, EventHouse, Log Analytics, log analysis, data exploration, time series, anomaly detection, summarize, where clause, join, extend, project, let statement, parse operator, extract function, any mention of pipe-forward query syntax.&quot;],&quot;lang&quot;:[0,&quot;core&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/skills/kql&quot;]}],[0,{&quot;name&quot;:[0,&quot;m365-agents-dotnet&quot;],&quot;description&quot;:[0,&quot;Microsoft 365 Agents SDK for .NET. Build multichannel agents for Teams/M365/Copilot Studio with ASP.NET Core hosting, AgentApplication routing, and MSAL-based auth. Triggers: \&quot;Microsoft 365 Agents SDK\&quot;, \&quot;Microsoft.Agents\&quot;, \&quot;AddAgentApplicationOptions\&quot;, \&quot;AgentApplication\&quot;, \&quot;AddAgentAspNetAuthentication\&quot;, \&quot;Copilot Studio client\&quot;, \&quot;IAgentHttpAdapter\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;dotnet&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-dotnet/skills/m365-agents-dotnet&quot;]}],[0,{&quot;name&quot;:[0,&quot;m365-agents-py&quot;],&quot;description&quot;:[0,&quot;Microsoft 365 Agents SDK for Python. Build multichannel agents for Teams/M365/Copilot Studio with aiohttp hosting, AgentApplication routing, streaming responses, and MSAL-based auth. Triggers: \&quot;Microsoft 365 Agents SDK\&quot;, \&quot;microsoft_agents\&quot;, \&quot;AgentApplication\&quot;, \&quot;start_agent_process\&quot;, \&quot;TurnContext\&quot;, \&quot;Copilot Studio client\&quot;, \&quot;CloudAdapter\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;py&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-python/skills/m365-agents-py&quot;]}],[0,{&quot;name&quot;:[0,&quot;m365-agents-ts&quot;],&quot;description&quot;:[0,&quot;Microsoft 365 Agents SDK for TypeScript/Node.js. Build multichannel agents for Teams/M365/Copilot Studio with AgentApplication routing, Express hosting, streaming responses, and Copilot Studio client integration. Triggers: \&quot;Microsoft 365 Agents SDK\&quot;, \&quot;@microsoft/agents-hosting\&quot;, \&quot;AgentApplication\&quot;, \&quot;startServer\&quot;, \&quot;streamingResponse\&quot;, \&quot;Copilot Studio client\&quot;, \&quot;@microsoft/agents-copilotstudio-client\&quot;.\n&quot;],&quot;lang&quot;:[0,&quot;ts&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-typescript/skills/m365-agents-ts&quot;]}],[0,{&quot;name&quot;:[0,&quot;mcp-builder&quot;],&quot;description&quot;:[0,&quot;Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP), Node/TypeScript (MCP SDK), or C#/.NET (Microsoft MCP SDK).&quot;],&quot;lang&quot;:[0,&quot;core&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/skills/mcp-builder&quot;]}],[0,{&quot;name&quot;:[0,&quot;microsoft-azure-webjobs-extensions-authentication-events-dotnet&quot;],&quot;description&quot;:[0,&quot;Microsoft Entra Authentication Events SDK for .NET. Azure Functions triggers for custom authentication extensions. Use for token enrichment, custom claims, attribute collection, and OTP customization in Entra ID. Triggers: \&quot;Authentication Events\&quot;, \&quot;WebJobsAuthenticationEventsTrigger\&quot;, \&quot;OnTokenIssuanceStart\&quot;, \&quot;OnAttributeCollectionStart\&quot;, \&quot;custom claims\&quot;, \&quot;token enrichment\&quot;, \&quot;Entra custom extension\&quot;, \&quot;authentication extension\&quot;.&quot;],&quot;lang&quot;:[0,&quot;dotnet&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-dotnet/skills/microsoft-azure-webjobs-extensions-authentication-events-dotnet&quot;]}],[0,{&quot;name&quot;:[0,&quot;microsoft-docs&quot;],&quot;description&quot;:[0,&quot;Understand Microsoft technologies by querying official documentation. Use whenever the user asks how something works, wants tutorials, needs configuration options, limits, quotas, or best practices for any Microsoft technology (Azure, .NET, M365, Windows, Power Platform, etc.)—even if they don&#39;t mention \&quot;docs.\&quot; If the question is about understanding a concept rather than writing code, this is the right skill.&quot;],&quot;lang&quot;:[0,&quot;core&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/skills/microsoft-docs&quot;]}],[0,{&quot;name&quot;:[0,&quot;podcast-generation&quot;],&quot;description&quot;:[0,&quot;Generate AI-powered podcast-style audio narratives using Azure OpenAI&#39;s GPT Realtime Mini model via WebSocket. Use when building text-to-speech features, audio narrative generation, podcast creation from content, or integrating with Azure OpenAI Realtime API for real audio output. Covers full-stack implementation from React frontend to Python FastAPI backend with WebSocket streaming.&quot;],&quot;lang&quot;:[0,&quot;core&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/skills/podcast-generation&quot;]}],[0,{&quot;name&quot;:[0,&quot;pydantic-models-py&quot;],&quot;description&quot;:[0,&quot;Create Pydantic models following the multi-model pattern with Base, Create, Update, Response, and InDB variants. Use when defining API request/response schemas, database models, or data validation in Python applications using Pydantic v2.&quot;],&quot;lang&quot;:[0,&quot;py&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-python/skills/pydantic-models-py&quot;]}],[0,{&quot;name&quot;:[0,&quot;react-flow-node-ts&quot;],&quot;description&quot;:[0,&quot;Create React Flow node components with TypeScript types, handles, and Zustand integration. Use when building custom nodes for React Flow canvas, creating visual workflow editors, or implementing node-based UI components.&quot;],&quot;lang&quot;:[0,&quot;ts&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-typescript/skills/react-flow-node-ts&quot;]}],[0,{&quot;name&quot;:[0,&quot;skill-creator&quot;],&quot;description&quot;:[0,&quot;Guide for creating effective skills for AI coding agents working with Azure SDKs and Microsoft Foundry services. Use when creating new skills or updating existing skills.&quot;],&quot;lang&quot;:[0,&quot;core&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/skills/skill-creator&quot;]}],[0,{&quot;name&quot;:[0,&quot;zustand-store-ts&quot;],&quot;description&quot;:[0,&quot;Create Zustand stores with TypeScript, subscribeWithSelector middleware, and proper state/action separation. Use when building React state management, creating global stores, or implementing reactive state patterns with Zustand.&quot;],&quot;lang&quot;:[0,&quot;ts&quot;],&quot;category&quot;:[0,&quot;general&quot;],&quot;path&quot;:[0,&quot;.github/plugins/azure-sdk-typescript/skills/zustand-store-ts&quot;]}]]],&quot;data-astro-cid-j7pv25f6&quot;:[0,true]}" ssr client="load" opts="{&quot;name&quot;:&quot;SkillsSection&quot;,&quot;value&quot;:true}" await-children><section style="padding:var(--space-md) 0 var(--space-2xl)"><div style="margin-bottom:var(--space-lg)"><div style="display:flex;align-items:center;gap:var(--space-md);padding:var(--space-sm) var(--space-md);min-width:240px;max-width:400px;background:var(--bg-secondary);border:1px solid var(--border-primary);border-radius:var(--radius-lg);transition:all var(--transition-fast)"><svg width="16" height="16" viewBox="0 0 24 24" fill="none" stroke="var(--text-muted)" stroke-width="2" style="flex-shrink:0"><circle cx="11" cy="11" r="8"></circle><path d="m21 21-4.35-4.35"></path></svg><input type="text" placeholder="Search skills by name or description..." data-testid="search-input" style="flex:1;background:transparent;border:none;outline:none;font-size:var(--text-sm);color:var(--text-primary)" value=""/><kbd style="padding:2px 6px;font-size:var(--text-xs);background:var(--bg-tertiary);border:1px solid var(--border-primary);border-radius:var(--radius-sm);color:var(--text-secondary)">⌘K</kbd></div></div><div style="margin-bottom:var(--space-md)"><div style="display:flex;flex-wrap:wrap;gap:var(--space-sm);padding:var(--space-xs);background:var(--bg-secondary);border-radius:var(--radius-lg);border:1px solid var(--border-primary)"><button style="display:flex;align-items:center;gap:var(--space-xs);padding:var(--space-sm) var(--space-md);font-size:var(--text-sm);font-weight:500;color:var(--text-primary);background:var(--bg-tertiary);border:none;border-radius:var(--radius-md);cursor:pointer;transition:all var(--transition-fast);position:relative"><span>All</span><span style="font-size:var(--text-xs);color:var(--accent);font-weight:600;min-width:20px;text-align:center">140</span><span style="position:absolute;bottom:2px;left:50%;transform:translateX(-50%);width:24px;height:2px;background:var(--accent);border-radius:1px"></span></button><button style="display:flex;align-items:center;gap:var(--space-xs);padding:var(--space-sm) var(--space-md);font-size:var(--text-sm);font-weight:500;color:var(--text-secondary);background:transparent;border:none;border-radius:var(--radius-md);cursor:pointer;transition:all var(--transition-fast);position:relative"><span style="width:8px;height:8px;border-radius:50%;background:var(--lang-core);flex-shrink:0"></span><span>Core</span><span style="font-size:var(--text-xs);color:var(--text-muted);font-weight:600;min-width:20px;text-align:center">11</span></button><button style="display:flex;align-items:center;gap:var(--space-xs);padding:var(--space-sm) var(--space-md);font-size:var(--text-sm);font-weight:500;color:var(--text-secondary);background:transparent;border:none;border-radius:var(--radius-md);cursor:pointer;transition:all var(--transition-fast);position:relative"><span style="width:8px;height:8px;border-radius:50%;background:var(--lang-python);flex-shrink:0"></span><span>Python</span><span style="font-size:var(--text-xs);color:var(--text-muted);font-weight:600;min-width:20px;text-align:center">42</span></button><button style="display:flex;align-items:center;gap:var(--space-xs);padding:var(--space-sm) var(--space-md);font-size:var(--text-sm);font-weight:500;color:var(--text-secondary);background:transparent;border:none;border-radius:var(--radius-md);cursor:pointer;transition:all var(--transition-fast);position:relative"><span style="width:8px;height:8px;border-radius:50%;background:var(--lang-dotnet);flex-shrink:0"></span><span>.NET</span><span style="font-size:var(--text-xs);color:var(--text-muted);font-weight:600;min-width:20px;text-align:center">29</span></button><button style="display:flex;align-items:center;gap:var(--space-xs);padding:var(--space-sm) var(--space-md);font-size:var(--text-sm);font-weight:500;color:var(--text-secondary);background:transparent;border:none;border-radius:var(--radius-md);cursor:pointer;transition:all var(--transition-fast);position:relative"><span style="width:8px;height:8px;border-radius:50%;background:var(--lang-typescript);flex-shrink:0"></span><span>TypeScript</span><span style="font-size:var(--text-xs);color:var(--text-muted);font-weight:600;min-width:20px;text-align:center">25</span></button><button style="display:flex;align-items:center;gap:var(--space-xs);padding:var(--space-sm) var(--space-md);font-size:var(--text-sm);font-weight:500;color:var(--text-secondary);background:transparent;border:none;border-radius:var(--radius-md);cursor:pointer;transition:all var(--transition-fast);position:relative"><span style="width:8px;height:8px;border-radius:50%;background:var(--lang-java);flex-shrink:0"></span><span>Java</span><span style="font-size:var(--text-xs);color:var(--text-muted);font-weight:600;min-width:20px;text-align:center">26</span></button><button style="display:flex;align-items:center;gap:var(--space-xs);padding:var(--space-sm) var(--space-md);font-size:var(--text-sm);font-weight:500;color:var(--text-secondary);background:transparent;border:none;border-radius:var(--radius-md);cursor:pointer;transition:all var(--transition-fast);position:relative"><span style="width:8px;height:8px;border-radius:50%;background:var(--lang-rust);flex-shrink:0"></span><span>Rust</span><span style="font-size:var(--text-xs);color:var(--text-muted);font-weight:600;min-width:20px;text-align:center">7</span></button></div></div><div style="margin-bottom:var(--space-xl)"><div style="display:flex;flex-wrap:wrap;gap:var(--space-xs)"><button style="display:inline-flex;align-items:center;gap:var(--space-xs);padding:6px 12px;font-size:var(--text-xs);font-weight:500;color:var(--text-primary);background:var(--accent-glow);border:1px solid var(--accent);border-radius:9999px;cursor:pointer;transition:all var(--transition-fast);text-transform:capitalize"><span>All</span><span style="font-size:10px;color:var(--accent);font-weight:600">140</span></button><button style="display:inline-flex;align-items:center;gap:var(--space-xs);padding:6px 12px;font-size:var(--text-xs);font-weight:500;color:var(--text-secondary);background:var(--bg-secondary);border:1px solid var(--border-primary);border-radius:9999px;cursor:pointer;transition:all var(--transition-fast);text-transform:capitalize"><span>General</span><span style="font-size:10px;color:var(--text-muted);font-weight:600">140</span></button></div></div><div style="margin-bottom:var(--space-lg);font-size:var(--text-sm);color:var(--text-secondary)">Showing <span style="color:var(--accent);font-weight:600">140</span> skills</div><div style="position:relative"><div class="skills-grid" style="max-height:464px;overflow:hidden;transition:max-height 0.3s ease-out"><div style="display:block;background:var(--bg-card);border:1px solid var(--border-primary);border-radius:var(--radius-lg);padding:var(--space-lg);transition:all var(--transition-base);cursor:pointer"><div style="display:flex;flex-direction:column;gap:var(--space-md);height:100%"><div style="display:flex;align-items:flex-start;justify-content:space-between;gap:var(--space-sm)"><h3 style="font-size:var(--text-base);font-weight:600;color:var(--text-primary);margin:0;line-height:1.4;word-break:break-word">agent-framework-azure-ai-py</h3><span style="display:inline-flex;align-items:center;padding:2px 8px;font-size:var(--text-xs);font-weight:500;border-radius:var(--radius-sm);text-transform:uppercase;letter-spacing:0.05em;background:rgba(96, 165, 250, 0.15);color:#60a5fa;border:1px solid rgba(96, 165, 250, 0.3);flex-shrink:0">Python</span></div><p style="font-size:var(--text-sm);color:var(--text-secondary);margin:0;line-height:1.6;flex:1;display:-webkit-box;-webkit-line-clamp:3;-webkit-box-orient:vertical;overflow:hidden">Build Azure AI Foundry agents using the Microsoft Agent Framework Python SDK (agent-framework-azure-ai). Use when creating persistent agents with AzureAIAgentsProvider, using hosted tools (code interpreter, file search, web search), integrating MCP servers, managing conversation threads, or implementing streaming responses. Covers function tools, structured outputs, and multi-tool agents.</p><div style="display:flex;align-items:center;justify-content:space-between;gap:var(--space-sm);margin-top:auto"><span style="font-size:var(--text-xs);color:var(--text-muted);text-transform:capitalize">general</span><button style="display:inline-flex;align-items:center;gap:4px;padding:4px 8px;font-size:var(--text-xs);font-weight:500;color:var(--text-secondary);background:var(--bg-tertiary);border:1px solid var(--border-primary);border-radius:var(--radius-sm);cursor:pointer;transition:all var(--transition-fast)" title="Copy: npx skills add microsoft/skills --skill agent-framework-azure-ai-py"><svg width="12" height="12" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2"><rect x="9" y="9" width="13" height="13" rx="2" ry="2"></rect><path d="M5 15H4a2 2 0 01-2-2V4a2 2 0 012-2h9a2 2 0 012 2v1"></path></svg>Copy</button></div></div></div><div style="display:block;background:var(--bg-card);border:1px solid var(--border-primary);border-radius:var(--radius-lg);padding:var(--space-lg);transition:all var(--transition-base);cursor:pointer"><div style="display:flex;flex-direction:column;gap:var(--space-md);height:100%"><div style="display:flex;align-items:flex-start;justify-content:space-between;gap:var(--space-sm)"><h3 style="font-size:var(--text-base);font-weight:600;color:var(--text-primary);margin:0;line-height:1.4;word-break:break-word">agents-v2-py</h3><span style="display:inline-flex;align-items:center;padding:2px 8px;font-size:var(--text-xs);font-weight:500;border-radius:var(--radius-sm);text-transform:uppercase;letter-spacing:0.05em;background:rgba(96, 165, 250, 0.15);color:#60a5fa;border:1px solid rgba(96, 165, 250, 0.3);flex-shrink:0">Python</span></div><p style="font-size:var(--text-sm);color:var(--text-secondary);margin:0;line-height:1.6;flex:1;display:-webkit-box;-webkit-line-clamp:3;-webkit-box-orient:vertical;overflow:hidden">Maintain legacy initial-preview Foundry container agents built with Azure AI Projects.
 Use only for pre-refreshed hosted-agent implementations. For current hosted agents with
 Responses or Invocations protocols, use hosted-agents-v2-py.
 Triggers: &quot;legacy container agent&quot;, &quot;initial preview hosted agent&quot;,