fix: 管理员种子匹配缺口 + 通用技能 SKILL.md 走 system prompt 缓存

backend/app/db/seed.py

@@ -3,6 +3,7 @@
 import sys
 from pathlib import Path
 
+from sqlalchemy import or_
 from sqlmodel import Session, select
 
 from app import paths
@@ -856,8 +857,14 @@ def seed_demo_data(session: Session) -> None:
         )
 
     # 桌面/单机版默认管理员账号（admin / admin）。权限只读取数据库 role 字段。
+    # 用 id=="admin" 或 username=="admin" 匹配：管理员可能被改了用户名（id 仍是
+    # admin）也可能种子时 id 就不是 admin（username 仍是 admin）——只认一边会在
+    # 另一边插入重复行，撞 id 主键或 (tenant_id, username) 唯一约束。
     admin_user = session.exec(
-        select(User).where(User.tenant_id == "tenant_demo", User.username == "admin")
+        select(User).where(
+            User.tenant_id == "tenant_demo",
+            or_(User.id == "admin", User.username == "admin"),
+        )
     ).first()
     if not admin_user:
         session.add(

backend/app/general_skills/runner.py

@@ -295,7 +295,6 @@ def _generate_plan(
                 "name": skill.name,
                 "description": skill.description,
                 "homepage": skill.homepage,
-                "markdown": skill.skill_markdown,
                 "package": _skill_package_payload(skill),
             },
             "runtime": {
@@ -321,7 +320,7 @@ def _generate_plan(
         )
         with llm_operation("general_skill.plan"):
             raw = LLMClient(_with_min_tokens(model_config, GENERAL_SKILL_MAX_TOKENS)).generate_json(
-                unified_system_prompt(),
+                unified_system_prompt(skill.skill_markdown),
                 payload,
             )
         plan = GeneralSkillExecutionPlan.model_validate(raw)
@@ -455,7 +454,6 @@ def _repair_plan(
                 "name": skill.name,
                 "description": skill.description,
                 "homepage": skill.homepage,
-                "markdown": skill.skill_markdown,
                 "package": _skill_package_payload(skill),
             },
             "runtime": {
@@ -482,7 +480,7 @@ def _repair_plan(
         )
         with llm_operation("general_skill.repair", attempt=next_attempt):
             raw = LLMClient(_with_min_tokens(model_config, GENERAL_SKILL_MAX_TOKENS)).generate_json(
-                unified_system_prompt(),
+                unified_system_prompt(skill.skill_markdown),
                 payload,
             )
         plan = GeneralSkillExecutionPlan.model_validate(raw)
@@ -727,7 +725,6 @@ def _review_execution_result(
                 "name": skill.name,
                 "description": skill.description,
                 "homepage": skill.homepage,
-                "markdown": _truncate(skill.skill_markdown, 6000),
                 "package": _skill_package_payload(skill, preview_limit=6000),
             },
             "runner": {
@@ -752,7 +749,7 @@ def _review_execution_result(
         try:
             with llm_operation("general_skill.review", attempt=attempt):
                 raw = LLMClient(model_config).generate_json(
-                    unified_system_prompt(), payload
+                    unified_system_prompt(skill.skill_markdown), payload
                 )
             review = GeneralSkillExecutionReview.model_validate(raw).model_dump(mode="json")
         except Exception as exc:
@@ -822,11 +819,27 @@ def _skill_files(skill: GeneralSkill) -> list[dict[str, Any]]:
 
 
 def _skill_package_payload(skill: GeneralSkill, preview_limit: int = 12000) -> dict[str, Any]:
+    # SKILL.md's content is already sent in full via the sibling "markdown"
+    # field on every call site that includes a package payload; echoing it
+    # again here as a content_preview doubles the token cost of every plan/
+    # repair/review call for no benefit, since the model already has it.
+    markdown = str(getattr(skill, "skill_markdown", "") or "")
     files = _skill_files(skill)
     previews: list[dict[str, Any]] = []
     remaining = preview_limit
     for file in files:
         content = str(file.get("content") or "")
+        if file.get("path") == "SKILL.md" and content == markdown:
+            previews.append(
+                {
+                    "path": file["path"],
+                    "size": file.get("size"),
+                    "mime_type": file.get("mime_type"),
+                    "content_preview": "（与上方 skill.markdown 字段内容相同，不重复展开）",
+                    "truncated": False,
+                }
+            )
+            continue
         preview = content[: max(0, min(len(content), remaining))]
         remaining -= len(preview)
         previews.append(

backend/app/llm/stage_protocol.py

@@ -90,8 +90,23 @@
 }
 
 
-def unified_system_prompt() -> str:
-    return UNIFIED_PROMPT_PATH.read_text(encoding="utf-8").strip()
+def unified_system_prompt(skill_markdown: str | None = None) -> str:
+    base = UNIFIED_PROMPT_PATH.read_text(encoding="utf-8").strip()
+    markdown = (skill_markdown or "").strip()
+    if not markdown:
+        return base
+    # Appending the active general skill's SKILL.md here (rather than in the
+    # per-turn stage payload) puts it in the one message position that stays
+    # byte-identical across every call for this skill, so provider prefix
+    # caching (DeepSeek/Gemini) actually reuses it instead of re-billing the
+    # full text on every turn. See render_stage_user_message for the
+    # matching comment on the per-turn side.
+    return (
+        base
+        + "\n\n---\n\n"
+        + "以下是当前技能的 SKILL.md 全文，是本次调用的固定背景资料：\n\n"
+        + markdown
+    )
 
 
 def stage_payload(
@@ -152,28 +167,36 @@ def render_stage_user_message(
         output_contract = json.dumps(
             output_contract or {}, ensure_ascii=False, separators=(",", ":")
         )
-    sections = []
+    # Static/highly-reused content goes first, volatile content last. Providers
+    # (DeepSeek, OpenAI-compatible proxies, etc.) cache the longest matching
+    # prefix of a request; a per-call timestamp or the user's raw message
+    # placed ahead of the large, stable stage instructions (e.g. a skill's
+    # full SKILL.md) breaks the prefix at the first byte and defeats caching
+    # for everything that follows, even though that content is identical
+    # across calls. Keeping the actual user question last also matches
+    # standard prompting practice (closest to where generation begins).
+    sections = [
+        f"当前阶段：\n{stage.get('phase') or '未指定'}",
+        (
+            "思考要求：\n保留完成当前阶段所需的简短思考；不要复述上下文、逐字段展开检查、"
+            "罗列无关备选方案或反复验证已明确的信息。得到可靠结论后立即按输出约束作答。"
+        ),
+        f"阶段规则：\n{str(stage.get('instructions') or '').strip()}",
+        f"输出约束：\n{output_contract}",
+    ]
+    if include_turn_header:
+        sections.append(f"用户记忆：\n{stage.get('memory') or '无'}")
+    sections.append(
+        "当前阶段独有内容：\n"
+        + json.dumps(projected, ensure_ascii=False, separators=(",", ":"))
+    )
     if include_turn_header:
         sections.extend(
             [
-                f"用户记忆：\n{stage.get('memory') or '无'}",
                 f"本轮时间：\n{stage.get('turn_time') or '未提供'}",
                 f"本轮用户输入：\n{user_message or '（空）'}",
             ]
         )
-    sections.extend(
-        [
-            f"当前阶段：\n{stage.get('phase') or '未指定'}",
-            (
-                "思考要求：\n保留完成当前阶段所需的简短思考；不要复述上下文、逐字段展开检查、"
-                "罗列无关备选方案或反复验证已明确的信息。得到可靠结论后立即按输出约束作答。"
-            ),
-            f"阶段规则：\n{str(stage.get('instructions') or '').strip()}",
-            "当前阶段独有内容：\n"
-            + json.dumps(projected, ensure_ascii=False, separators=(",", ":")),
-            f"输出约束：\n{output_contract}",
-        ]
-    )
     return "\n\n".join(sections)
 
 

backend/tests/test_llm_client.py

@@ -458,7 +458,7 @@ def test_generate_text_projects_conversation_context_messages():
     assert '"pending_tasks"' not in current_input["content"]
 
 
-def test_stage_input_uses_stable_history_and_puts_memory_time_and_question_first():
+def test_stage_input_uses_stable_history_and_puts_instructions_before_volatile_content():
     client = object.__new__(LLMClient)
     client.client = _FakeOpenAIClient()
     client.model = "demo-model"
@@ -492,13 +492,17 @@ def test_stage_input_uses_stable_history_and_puts_memory_time_and_question_first
         {"role": "assistant", "content": "请说明本次需求"},
     ]
     current = messages[-1]["content"]
-    assert current.startswith("用户记忆：\n- 用户偏好简洁回复\n\n本轮时间：")
-    assert "本轮时间：\n2026-07-13T20:30:00+08:00" in current
-    assert "本轮用户输入：\n我想申请报销" in current
-    assert "当前阶段：\nRouter" in current
+    # Static/stable content (phase, thinking requirement, stage instructions) must
+    # come first so provider-side prompt-prefix caching isn't broken by a per-call
+    # timestamp or the user's raw message; volatile content stays last.
+    assert current.startswith("当前阶段：\nRouter")
     assert "思考要求：" in current
     assert "保留完成当前阶段所需的简短思考" in current
-    assert "available_skills" in current
+    assert current.index("只根据技能摘要路由。") < current.index("本轮时间：")
+    assert current.index("available_skills") < current.index("本轮用户输入：")
+    assert "本轮时间：\n2026-07-13T20:30:00+08:00" in current
+    assert "本轮用户输入：\n我想申请报销" in current
+    assert current.rstrip().endswith("本轮用户输入：\n我想申请报销")
     assert "memory_internal" not in current
     assert sum("我想申请报销" in str(message["content"]) for message in messages) == 1
 

backend/tests/test_unified_agent_prompt.py

@@ -105,7 +105,7 @@ def fake_generate_text(self, system_prompt, payload):  # noqa: ANN001
         assert payload["_agent_stage"]["output_contract"]
 
 
-def test_general_skill_substages_use_the_same_unified_system_prompt(monkeypatch) -> None:
+def test_general_skill_substages_share_base_prompt_with_skill_caching_suffix(monkeypatch) -> None:
     systems: dict[str, str] = {}
 
     def fake_init(self, model_config):  # noqa: ANN001
@@ -199,5 +199,18 @@ def fake_execute_plan(  # noqa: ANN001
         "Reflection / General Skill Review",
         "Response Generator / General Skill Reply",
     }
-    assert len(set(systems.values())) == 1
-    assert "统一执行引擎" in next(iter(systems.values()))
+    base_prompt = systems["Router / General Skill Selector"]
+    assert "统一执行引擎" in base_prompt
+    # Selector and Reply never see the full SKILL.md, so their system
+    # message stays on the pure base prompt regardless of which skill is
+    # active — that keeps it maximally cache-stable across skills.
+    assert systems["Response Generator / General Skill Reply"] == base_prompt
+    # Plan and Review embed the active skill's SKILL.md in the system
+    # prompt (not the per-turn payload) so repeat calls for the same skill
+    # hit the provider's prefix cache instead of re-billing the full text
+    # on every turn.
+    skill_aware_prompt = systems["Step Agent / General Skill Plan"]
+    assert systems["Reflection / General Skill Review"] == skill_aware_prompt
+    assert skill_aware_prompt.startswith(base_prompt)
+    assert skill.skill_markdown in skill_aware_prompt
+    assert skill_aware_prompt != base_prompt