Release/bridgic 0.4.1#195
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* first version of bridgic-cognitive
* Add test cases. Add the dependency of pyyaml
* Adjust the CognitiveContext to support dynamically adding the field summary for users.
* In AgentAutoma, add the context object initialization step. When using the context, there is no need to initialize it separately each time.
* Modify the Cognitive test cases to integration tests.
* Upgrade the ContextSkills interface. When necessary, override the add method yourself to automatically initialize the corresponding data structure. Adjust the package name, and expose all objects only through the __init__ module.
* Add a mechanism for setting the default Agent Automa initialization model as the default model for all thinking steps.
* Add some record information printing of the execution process.
* Adjust the capacity of the short-term memory for the historical record context from 5 to 20.
* The long-term memory compression logic for adjusting the historical record context has been updated, and the compression buffer has been increased to 10 entries. Memory compression is triggered only when the long-term memory buffer is full. Otherwise, these memories will be displayed as summaries, just like short-term memories.
* Adjust the log output length of _observation
* Fix the bug in AgentAutoma where the verbose parameter also controls verbose_prompt.
* Adjust the log output control of observation. The execution log will be output only when it is necessary, and the decision to output it will be based on the observation results.
* When selecting tools in the Default mode, add an observation step.
* Optimize the issue of repeatedly requested progressive disclosure information such as the already disclosed skills.
* Add the 'until' interface, which allows you to directly set the condition under which a thinking step should stop during runtime, and decouple the judgment condition from the context object at the same time.
* Let the thinking steps support the dynamic setting tools and skills.
* Let the context object determine which information is disclosed when the model is invoked and which is not.
* Adjust the context structure, remove the finish tag, and add the observation result and the status of each tool invocation. Add the default task termination judgment interface.
* Adjust prompt
* Adjust the prompt description to be consistent.
* Add data structure validation during the execution process of CognitveWorker.
* remove useless code
* Adapt the initialization of AgentAutoma to GraphAutoma.
* Allow the context type of AgentAutoma to be freely initialized.arun(goal=x) > ctx_init={goal: y} > context class default
* add test
* adjust arguments
* The goal of the context object can also be empty when it is adjusted. Fix the bug in ASLAutoma that does not retain the initialization parameters of custom Automa.
* Fix the bug where the AgentAutoma initialization context object fails to correctly identify field types.
* Adjust the context object initialization logic to be compatible with the direct initialization by passing either a list or an Exposure type.
* Feature/cognitive optimization (#183)
* Remove the Default mode
* Unify and abstract the observation and execution of thinking steps to a higher level, allowing the thinking steps to engage in pure reasoning.
* Reconstruct the prompt assembly logic for cognitive workers to make it more stable.
* Refactor cognitive framework: safety, naming, and prompt quality improvements.
* All the refactoring for the parts of cognitive that were not easy to use has been completed, and a large number of issues have been fixed.
* Adjust prompt design
---------
Co-authored-by: lynnlink <>
* Realize the replay function during the agent's execution process.
* Start to solve the system design problem of amphibious vehicles.
* Continue to optimize the system design of amphibious vehicles.
* Refactor the AgentAutoma module to make it well-organized and have a clear structure.
* Complete the design of the task structure marking during the agent's operation process.
* Adjust the code structure to further clarify the internal abstract relationships.
* Adjust the code for testing, fix the bugs, and then start the formal design of the workflow conversion operation.
* Add the implementation to the workflow and complete the code structure refactoring.
* Unify the parameter names of the sequential and loop tags. Adjust the entry method when running the workflow.
* Adjust the code structure; unify the log output.
* Adjust log
* Adjust the log output to be more structured to facilitate problem location.
* Adjust log
* fix workflow get empty action bug
* Adjust metadata and log
* Start to design loop workflow.
* Adjust the code structure of the workflow; Implement a feature that ensures the internal behavior of a specific model is not recorded. Implement dynamic degradation strategies for the workflow execution process.
* Debug the implementation of the loop to enable the loop condition and parameter updates to be implemented through code.
* Debug the implementation of the loop to enable the loop condition and parameter updates to be implemented through code.
* Reconstruct AgentAtuoma; The re-implemented AgentAutoma amphibious strategy
* Reorganize the code structure to make the current design of the cognitive module clearer.
* Adjust code
* Adjust test case
* Add a fallback plan: When the workflow encounters an error, it should be able to automatically switch back to the agent mode to solve the problem independently.
* Fix the bug where the framework swallows the exception thrown by the tool execution. Adjust the code structure
* Improve the trace information of the Agent execution process; Refactor the code structure and remove unnecessary classes.
* Adjust code
* first version of amphibipus agent system
* Refactor the naming of the bridgic-cognitive package and all its internal classes and methods; Add descriptor syntactic sugar when arranging the agent design pattern.
* Adjust the hook method of the thinking unit itself to the delegate design pattern; Make the declaration of steps in the workflow more concise.
* Add the capability of route selection for operation modes; Improve the design of the local degradation agent
* Create the bridgic-amphibious package
* Create the bridgic-amphibious package and add project configuration.
* Adjust doc-string and add Readme.md
* Adjust test case
---------
Co-authored-by: lynnlink <>
* first version of bridgic-cognitive
* Add test cases. Add the dependency of pyyaml
* Adjust the CognitiveContext to support dynamically adding the field summary for users.
* In AgentAutoma, add the context object initialization step. When using the context, there is no need to initialize it separately each time.
* Modify the Cognitive test cases to integration tests.
* Upgrade the ContextSkills interface. When necessary, override the add method yourself to automatically initialize the corresponding data structure. Adjust the package name, and expose all objects only through the __init__ module.
* Add a mechanism for setting the default Agent Automa initialization model as the default model for all thinking steps.
* Add some record information printing of the execution process.
* Adjust the capacity of the short-term memory for the historical record context from 5 to 20.
* The long-term memory compression logic for adjusting the historical record context has been updated, and the compression buffer has been increased to 10 entries. Memory compression is triggered only when the long-term memory buffer is full. Otherwise, these memories will be displayed as summaries, just like short-term memories.
* Adjust the log output length of _observation
* Fix the bug in AgentAutoma where the verbose parameter also controls verbose_prompt.
* Adjust the log output control of observation. The execution log will be output only when it is necessary, and the decision to output it will be based on the observation results.
* When selecting tools in the Default mode, add an observation step.
* Optimize the issue of repeatedly requested progressive disclosure information such as the already disclosed skills.
* Add the 'until' interface, which allows you to directly set the condition under which a thinking step should stop during runtime, and decouple the judgment condition from the context object at the same time.
* Let the thinking steps support the dynamic setting tools and skills.
* Let the context object determine which information is disclosed when the model is invoked and which is not.
* Adjust the context structure, remove the finish tag, and add the observation result and the status of each tool invocation. Add the default task termination judgment interface.
* Adjust prompt
* Adjust the prompt description to be consistent.
* Add data structure validation during the execution process of CognitveWorker.
* remove useless code
* Adapt the initialization of AgentAutoma to GraphAutoma.
* Allow the context type of AgentAutoma to be freely initialized.arun(goal=x) > ctx_init={goal: y} > context class default
* add test
* adjust arguments
* The goal of the context object can also be empty when it is adjusted. Fix the bug in ASLAutoma that does not retain the initialization parameters of custom Automa.
* Fix the bug where the AgentAutoma initialization context object fails to correctly identify field types.
* Adjust the context object initialization logic to be compatible with the direct initialization by passing either a list or an Exposure type.
* Feature/cognitive optimization (#183)
* Remove the Default mode
* Unify and abstract the observation and execution of thinking steps to a higher level, allowing the thinking steps to engage in pure reasoning.
* Reconstruct the prompt assembly logic for cognitive workers to make it more stable.
* Refactor cognitive framework: safety, naming, and prompt quality improvements.
* All the refactoring for the parts of cognitive that were not easy to use has been completed, and a large number of issues have been fixed.
* Adjust prompt design
---------
Co-authored-by: lynnlink <>
* Realize the replay function during the agent's execution process.
* Start to solve the system design problem of amphibious vehicles.
* Continue to optimize the system design of amphibious vehicles.
* Refactor the AgentAutoma module to make it well-organized and have a clear structure.
* Complete the design of the task structure marking during the agent's operation process.
* Adjust the code structure to further clarify the internal abstract relationships.
* Adjust the code for testing, fix the bugs, and then start the formal design of the workflow conversion operation.
* Add the implementation to the workflow and complete the code structure refactoring.
* Unify the parameter names of the sequential and loop tags. Adjust the entry method when running the workflow.
* Adjust the code structure; unify the log output.
* Adjust log
* Adjust the log output to be more structured to facilitate problem location.
* Adjust log
* fix workflow get empty action bug
* Adjust metadata and log
* Start to design loop workflow.
* Adjust the code structure of the workflow; Implement a feature that ensures the internal behavior of a specific model is not recorded. Implement dynamic degradation strategies for the workflow execution process.
* Debug the implementation of the loop to enable the loop condition and parameter updates to be implemented through code.
* Debug the implementation of the loop to enable the loop condition and parameter updates to be implemented through code.
* Reconstruct AgentAtuoma; The re-implemented AgentAutoma amphibious strategy
* Reorganize the code structure to make the current design of the cognitive module clearer.
* Adjust code
* Adjust test case
* Add a fallback plan: When the workflow encounters an error, it should be able to automatically switch back to the agent mode to solve the problem independently.
* Fix the bug where the framework swallows the exception thrown by the tool execution. Adjust the code structure
* Improve the trace information of the Agent execution process; Refactor the code structure and remove unnecessary classes.
* Adjust code
* first version of amphibipus agent system
* Refactor the naming of the bridgic-cognitive package and all its internal classes and methods; Add descriptor syntactic sugar when arranging the agent design pattern.
* Adjust the hook method of the thinking unit itself to the delegate design pattern; Make the declaration of steps in the workflow more concise.
* Add the capability of route selection for operation modes; Improve the design of the local degradation agent
* Create the bridgic-amphibious package
* Create the bridgic-amphibious package and add project configuration.
* Adjust doc-string and add Readme.md
* Adjust test case
* All tutorial files that have been added with the bridgic-amphibious module; The web page tutorial configuration is complete. Next, we'll start reviewing the content to ensure it runs smoothly.
* The final output form of optimizing the amphibious system: the agent uses the final summary as the result; for the workflow, the user can set the result by themselves.
* Complete quick start toturial
* Complete cognitive worker tutorial
* Adjust code
* Complete think tutorial
* Complete think tutorial
* Complete agent mode tutorial
* Complete context and exposure
* Complete custom otc (observaton-thinking-action) tutorial
* Complete cognitive policies tutorial
* Complete phase annotation tutorial
* Complete cognitive history tutorial
* Complete traceing tutorial
* first version bridgic-amphibious tutorial
* Adjust the output of the results. Fix the bug of parameter passing for the threshold of downgrade times. Add an after_action hook
* Complete API reference of birdgic-amphibious
---------
Co-authored-by: lynnlink <>
* first version of bridgic-cognitive
* Add test cases. Add the dependency of pyyaml
* Adjust the CognitiveContext to support dynamically adding the field summary for users.
* In AgentAutoma, add the context object initialization step. When using the context, there is no need to initialize it separately each time.
* Modify the Cognitive test cases to integration tests.
* Upgrade the ContextSkills interface. When necessary, override the add method yourself to automatically initialize the corresponding data structure. Adjust the package name, and expose all objects only through the __init__ module.
* Add a mechanism for setting the default Agent Automa initialization model as the default model for all thinking steps.
* Add some record information printing of the execution process.
* Adjust the capacity of the short-term memory for the historical record context from 5 to 20.
* The long-term memory compression logic for adjusting the historical record context has been updated, and the compression buffer has been increased to 10 entries. Memory compression is triggered only when the long-term memory buffer is full. Otherwise, these memories will be displayed as summaries, just like short-term memories.
* Adjust the log output length of _observation
* Fix the bug in AgentAutoma where the verbose parameter also controls verbose_prompt.
* Adjust the log output control of observation. The execution log will be output only when it is necessary, and the decision to output it will be based on the observation results.
* When selecting tools in the Default mode, add an observation step.
* Optimize the issue of repeatedly requested progressive disclosure information such as the already disclosed skills.
* Add the 'until' interface, which allows you to directly set the condition under which a thinking step should stop during runtime, and decouple the judgment condition from the context object at the same time.
* Let the thinking steps support the dynamic setting tools and skills.
* Let the context object determine which information is disclosed when the model is invoked and which is not.
* Adjust the context structure, remove the finish tag, and add the observation result and the status of each tool invocation. Add the default task termination judgment interface.
* Adjust prompt
* Adjust the prompt description to be consistent.
* Add data structure validation during the execution process of CognitveWorker.
* remove useless code
* Adapt the initialization of AgentAutoma to GraphAutoma.
* Allow the context type of AgentAutoma to be freely initialized.arun(goal=x) > ctx_init={goal: y} > context class default
* add test
* adjust arguments
* The goal of the context object can also be empty when it is adjusted. Fix the bug in ASLAutoma that does not retain the initialization parameters of custom Automa.
* Fix the bug where the AgentAutoma initialization context object fails to correctly identify field types.
* Adjust the context object initialization logic to be compatible with the direct initialization by passing either a list or an Exposure type.
* Feature/cognitive optimization (#183)
* Remove the Default mode
* Unify and abstract the observation and execution of thinking steps to a higher level, allowing the thinking steps to engage in pure reasoning.
* Reconstruct the prompt assembly logic for cognitive workers to make it more stable.
* Refactor cognitive framework: safety, naming, and prompt quality improvements.
* All the refactoring for the parts of cognitive that were not easy to use has been completed, and a large number of issues have been fixed.
* Adjust prompt design
---------
Co-authored-by: lynnlink <>
* Realize the replay function during the agent's execution process.
* Start to solve the system design problem of amphibious vehicles.
* Continue to optimize the system design of amphibious vehicles.
* Refactor the AgentAutoma module to make it well-organized and have a clear structure.
* Complete the design of the task structure marking during the agent's operation process.
* Adjust the code structure to further clarify the internal abstract relationships.
* Adjust the code for testing, fix the bugs, and then start the formal design of the workflow conversion operation.
* Add the implementation to the workflow and complete the code structure refactoring.
* Unify the parameter names of the sequential and loop tags. Adjust the entry method when running the workflow.
* Adjust the code structure; unify the log output.
* Adjust log
* Adjust the log output to be more structured to facilitate problem location.
* Adjust log
* fix workflow get empty action bug
* Adjust metadata and log
* Start to design loop workflow.
* Adjust the code structure of the workflow; Implement a feature that ensures the internal behavior of a specific model is not recorded. Implement dynamic degradation strategies for the workflow execution process.
* Debug the implementation of the loop to enable the loop condition and parameter updates to be implemented through code.
* Debug the implementation of the loop to enable the loop condition and parameter updates to be implemented through code.
* Reconstruct AgentAtuoma; The re-implemented AgentAutoma amphibious strategy
* Reorganize the code structure to make the current design of the cognitive module clearer.
* Adjust code
* Adjust test case
* Add a fallback plan: When the workflow encounters an error, it should be able to automatically switch back to the agent mode to solve the problem independently.
* Fix the bug where the framework swallows the exception thrown by the tool execution. Adjust the code structure
* Improve the trace information of the Agent execution process; Refactor the code structure and remove unnecessary classes.
* Adjust code
* first version of amphibipus agent system
* Refactor the naming of the bridgic-cognitive package and all its internal classes and methods; Add descriptor syntactic sugar when arranging the agent design pattern.
* Adjust the hook method of the thinking unit itself to the delegate design pattern; Make the declaration of steps in the workflow more concise.
* Add the capability of route selection for operation modes; Improve the design of the local degradation agent
* Create the bridgic-amphibious package
* Create the bridgic-amphibious package and add project configuration.
* Adjust doc-string and add Readme.md
* Adjust test case
* All tutorial files that have been added with the bridgic-amphibious module; The web page tutorial configuration is complete. Next, we'll start reviewing the content to ensure it runs smoothly.
* The final output form of optimizing the amphibious system: the agent uses the final summary as the result; for the workflow, the user can set the result by themselves.
* Complete quick start toturial
* Complete cognitive worker tutorial
* Adjust code
* Complete think tutorial
* Complete think tutorial
* Complete agent mode tutorial
* Complete context and exposure
* Complete custom otc (observaton-thinking-action) tutorial
* Complete cognitive policies tutorial
* Complete phase annotation tutorial
* Complete cognitive history tutorial
* Complete traceing tutorial
* first version bridgic-amphibious tutorial
* Adjust the output of the results. Fix the bug of parameter passing for the threshold of downgrade times. Add an after_action hook
* Complete API reference of birdgic-amphibious
* Add the bridgic-amphibious CLI scaffolding to create a project; Eliminate the redundant design for the dedicated intelligent agent as the pathfinding intelligent agent; Adjust the code structure
* Add bridgic-amphibious skill
* Adjust doc for removing sequential, loop
* Adjust the workflow to switch back to the agent mode and customize the context settings. By default, the context is clean.
* Update birdigic-amphibious skill for AgentCall
* Fix AgentCall history arguments bug
* Fix AgentAutoma arun bug when context and kwargs arguments pass
* Adjust the project structure and project code generated by the scaffolding.
* Reconstruct the type of objects generated in on_workflow to make the code more uniform and facilitate the maintenance and upgrade of on_workflow.
* Bridgic-amphibipus supports human-in-loop and awaits operation.
* Adjust doc
* Adjust bridgic-amphibious skills
* Adjust log level
* fix: align naming and tighten amphibious module
* refactor(amphibious): rename AMPHIFLOW, fix AUTO dispatch, relax LLM requirement
* refactor(amphibious): rename AMPHIFLOW, fix AUTO dispatch, relax LLM requirement
* Sync skills
* chore(skills): sync bridgic-amphibious, add bridgic-llms, support BRIDGIC_DEV_INDEX
* chore(skills): sync bridgic-amphibious, add bridgic-llms, support BRIDGIC_DEV_INDEX
* Adjust skill to ensure python environment to create bridgic-amphibious projrcy
* Adjust skill to ensure python environment to create bridgic-amphibious project
* Adjust skills
* Adjust skills
* Fix: Resolve the error in environment initialization
* Fix: Resolve the error in environment initialization
* Updata bridgic-amphibious skill .ini
* Adjust the order of tutorials
* Rename human_request_tool to request_human_tool (inner ask_human to request_human) for naming consistency, auto inject request_human_tool into every agent context tools during arun with name based dedupe so explicit passing still works and the LLM can call request_human in any mode without extra wiring, drop will_fallback from the public arun signature since AMPHIFLOW now always enables fallback and users should switch to RunMode WORKFLOW to opt out, freeze the builtin tools registry as an immutable tuple to prevent runtime mutation, and sync README tutorials skill docs and HITL tests including new coverage for builtin injection across agent workflow step fallback and full agent fallback paths.
* Remove redundant file
---------
Co-authored-by: lynnlink <>
* docs: add Twitter and Discord links to Community section * docs: remove WeChat group information and associated QR code from README
…old (#194) * ci: gate PRs to main on skills install-deps check - Add .github/workflows/skill-install-deps-check.yml that discovers each skills/*/scripts/deps*.ini dynamically and, per config, runs install-deps.sh in a fresh mktemp -d, asserting the DEPS_READY marker. A policy-guard step rejects any section whose source is not "default", so only public-PyPI references can reach main. - Rewrite skills/README.md as the top-level index for the skills system: skill list, directory layout, installer usage, deps.ini grammar, and the CI gate convention. * fix: handle generator-internal exceptions in on_workflow Helper code or inline logic between yields in on_workflow() previously crashed the entire run because the inner try only caught StopAsyncIteration. The generator is unrecoverable after a raise (asend would fail), so the fix mirrors full-fallback semantics: WORKFLOW mode re-raises; AMPHIFLOW mode hands off to on_agent(ctx); AMPHIFLOW without an on_agent override surfaces a clear RuntimeError tagged with the failing step index. * refactor: simplify scaffold to a single amphi.py with architecture stub Replace the multi-file project layout (task.md, config.py, tools.py, workers.py, agents.py + skills/result/log dirs) with a single amphi.py emitted directly into the target directory. Runtime concerns (.env, __main__) are out of scope for the scaffold. The new template surfaces the framework's main building blocks — custom CognitiveContext, AmphibiousAutoma subclass, think_unit, CognitiveWorker, and both on_agent / on_workflow stubs — so a user discovers the architecture by reading the file rather than from external docstrings. CLI: drop required -n/--name; bridgic-amphibious create now writes ./amphi.py (or under --base-dir). --task is injected as a top-level "# Task: ..." comment when provided. * docs: align bridgic-amphibious skill with new scaffold and helper-error semantics Update SKILL.md and references to match recent code changes: - Scaffold CLI no longer takes -n/--name; only emits a single amphi.py in the target directory. Update CLI examples, flag table, and the Generated structure block accordingly. Add a brief note on the Python API signature. - Workflow Fallback Mechanism now distinguishes ActionCall tool failures (existing step-level fallback path) from generator-internal exceptions (helper / inline logic raises), which are unrecoverable and route to full on_agent fallback (or re-raise in pure WORKFLOW). * fix: tolerate AI-generated stub overrides of template hooks Empty `pass`-body overrides of `before_action` / `after_action` / `on_workflow` no longer break the framework: - worker `before_action` / `after_action` returning `None` is treated identically to `_DELEGATE` so the agent-level hook still runs - agent `before_action` returning `None` is treated as passthrough, preserving the original `decision_result` - `_has_workflow()` now requires `inspect.isasyncgenfunction`, so a coroutine-body `on_workflow: pass` is recognized as "not overridden" and `RunMode.AUTO` falls back to the agent path instead of crashing on the async-generator protocol * fix: tolerate stub overrides for observation and action_custom_output Extends the previous stub-override compatibility pass to two more hooks where an AI-generated `pass` body would silently corrupt behavior (rather than crash loudly): - `CognitiveWorker.observation` returning `None` is now treated identically to `_DELEGATE`, so the agent-level observation fallback still runs instead of `None` being written into `ctx.observation`. - `AmphibiousAutoma.action_custom_output` returning `None` is now treated as passthrough, preserving the original `decision_result` so a typed `output_schema` value is not silently dropped. Two new tests cover the canonical AI-generated `pass` shape for each hook. --------- Co-authored-by: lynnlink <>
bridgic-amphibious uses yaml at runtime to parse SKILL.md frontmatter, but the pyyaml dependency was declared on bridgic-core. Since core is not being republished this release, users installing bridgic-amphibious 0.1.1 from PyPI would receive the existing bridgic-core 0.3.0 wheel without pyyaml and hit ModuleNotFoundError when yaml.safe_load runs. Move pyyaml>=6.0.3 to amphibious where it is actually used, and drop the incorrect declaration from core. This also realigns core's source pyproject with the already-published bridgic-core 0.3.0 wheel on PyPI.
…e history main has fba9f55 (Release/bridgic 0.4.0 squash) which already contains the content of the original commits still living on dev (#189/#190/#191). Use the 'ours' merge strategy to record main as merged while keeping the release branch's actual file state, so PR #195's merge-base advances to main's tip and shows only the genuine v0.4.1 changes.
NiceCode666
approved these changes
Apr 29, 2026
tsingfei
approved these changes
Apr 29, 2026
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🚀 Bridgic v0.4.1 Release
Overview
A patch release on top of v0.4.0 focused on hardening
bridgic-amphibiousand fixing a latent runtime dependency issue inherited from when amphibious was first split out of the cognitive module. After this release, end users installingbridgicfrom PyPI receive a self-sufficientbridgic-amphibiousthat no longer relies on transitive luck forpyyaml.🐛 Fixes
pyyamldependency frombridgic-coretobridgic-amphibious, where it is actually used.bridgic-amphibious/_context.pydoesimport yaml/yaml.safe_loadwhile parsingSKILL.mdfrontmatter, but the dependency had been declared onbridgic-core. Becausebridgic-corewas not republished in v0.4.0, the declaration never reached PyPI — installingbridgic-amphibious 0.1.0could surfaceModuleNotFoundError: No module named 'yaml'whenever skill loading was triggered. The fix moves the declaration tobridgic-amphibiousand realignsbridgic-core's sourcepyproject.tomlwith the already-published 0.3.0 wheel on PyPI.observation/action_custom_output(fix: harden amphibious template hooks & realign skills with new scaffold #194).on_workflow(fix: harden amphibious template hooks & realign skills with new scaffold #194).🛠️ Refactor
amphi.pywith an architecture stub, and realign skills with the new scaffold (fix: harden amphibious template hooks & realign skills with new scaffold #194).📚 Docs
bridgic-amphibiousskill documentation with the new scaffold and helper-error semantics (fix: harden amphibious template hooks & realign skills with new scaffold #194).⚙️ CI
mainon the skills install-deps check (fix: harden amphibious template hooks & realign skills with new scaffold #194).📦 Updated Packages
bridgic==0.4.1bridgic-amphibious==0.1.1