From 9824bc2d6c35564152b4fe1e56e1a5fd790b93e5 Mon Sep 17 00:00:00 2001
From: "DESKTOP-72TV0V4\\caoxiaozhu" <leocaoxiaozhu@163.com>
Date: Sat, 21 Mar 2026 11:29:57 +0800
Subject: [PATCH] feat: add SkillRegistry for agent integration

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
---
 backend/app/agents/graph.py          | 29 +++++++++++++++---
 backend/app/agents/skill_registry.py | 46 ++++++++++++++++++++++++++++
 2 files changed, 71 insertions(+), 4 deletions(-)
 create mode 100644 backend/app/agents/skill_registry.py

diff --git a/backend/app/agents/graph.py b/backend/app/agents/graph.py
index 8ff9f0e..5bc6e24 100644
--- a/backend/app/agents/graph.py
+++ b/backend/app/agents/graph.py
@@ -13,6 +13,7 @@ from app.agents.prompts import (
     ANALYST_SYSTEM_PROMPT,
 )
 from app.agents.tools import ALL_TOOLS
+from app.agents.skill_registry import build_skill_context
 from app.services.llm_service import get_llm
 
 
@@ -69,8 +70,13 @@ async def planner_node(state: AgentState) -> AgentState:
     user_msgs = _filter_user_messages(state["messages"])
     user_query = user_msgs[-1].content if user_msgs else ""
 
+    system_msgs = [SystemMessage(content=PLANNER_SYSTEM_PROMPT)]
+    skill_ctx = build_skill_context("planner")
+    if skill_ctx:
+        system_msgs.append(SystemMessage(content=skill_ctx))
+
     response = await llm.invoke(
-        [SystemMessage(content=PLANNER_SYSTEM_PROMPT), HumanMessage(content=f"用户请求: {user_query}")]
+        system_msgs + [HumanMessage(content=f"用户请求: {user_query}")]
     )
 
     plan_text = response.content
@@ -92,8 +98,13 @@ async def executor_node(state: AgentState) -> AgentState:
     user_msgs = _filter_user_messages(state["messages"])
     user_query = user_msgs[-1].content if user_msgs else ""
 
+    system_msgs = [SystemMessage(content=EXECUTOR_SYSTEM_PROMPT)]
+    skill_ctx = build_skill_context("executor")
+    if skill_ctx:
+        system_msgs.append(SystemMessage(content=skill_ctx))
+
     response = await llm.bind_tools(ALL_TOOLS).invoke(
-        [SystemMessage(content=EXECUTOR_SYSTEM_PROMPT), HumanMessage(content=f"用户请求: {user_query}")]
+        system_msgs + [HumanMessage(content=f"用户请求: {user_query}")]
     )
 
     tool_calls = getattr(response, "tool_calls", None) or []
@@ -131,8 +142,13 @@ async def librarian_node(state: AgentState) -> AgentState:
     user_msgs = _filter_user_messages(state["messages"])
     user_query = user_msgs[-1].content if user_msgs else ""
 
+    system_msgs = [SystemMessage(content=LIBRARIAN_SYSTEM_PROMPT)]
+    skill_ctx = build_skill_context("librarian")
+    if skill_ctx:
+        system_msgs.append(SystemMessage(content=skill_ctx))
+
     response = await llm.bind_tools(ALL_TOOLS).invoke(
-        [SystemMessage(content=LIBRARIAN_SYSTEM_PROMPT), HumanMessage(content=f"用户请求: {user_query}")]
+        system_msgs + [HumanMessage(content=f"用户请求: {user_query}")]
     )
 
     tool_calls = getattr(response, "tool_calls", None) or []
@@ -171,8 +187,13 @@ async def analyst_node(state: AgentState) -> AgentState:
     user_msgs = _filter_user_messages(state["messages"])
     user_query = user_msgs[-1].content if user_msgs else ""
 
+    system_msgs = [SystemMessage(content=ANALYST_SYSTEM_PROMPT)]
+    skill_ctx = build_skill_context("analyst")
+    if skill_ctx:
+        system_msgs.append(SystemMessage(content=skill_ctx))
+
     response = await llm.bind_tools(ALL_TOOLS).invoke(
-        [SystemMessage(content=ANALYST_SYSTEM_PROMPT), HumanMessage(content=f"用户请求: {user_query}")]
+        system_msgs + [HumanMessage(content=f"用户请求: {user_query}")]
     )
 
     tool_calls = getattr(response, "tool_calls", None) or []
diff --git a/backend/app/agents/skill_registry.py b/backend/app/agents/skill_registry.py
new file mode 100644
index 0000000..c693a37
--- /dev/null
+++ b/backend/app/agents/skill_registry.py
@@ -0,0 +1,46 @@
+"""
+Skill Registry - Agent 运行时加载 Skills
+"""
+from typing import Optional
+from app.database import async_session
+from app.services.skill_service import SkillService
+
+# 缓存：agent_type -> list[Skill]
+_skill_cache: dict[str, list] = {}
+
+async def load_skills_for_agent(agent_type: str, force_reload: bool = False) -> list:
+    """加载指定 Agent 类型的可用 Skills"""
+    if not force_reload and agent_type in _skill_cache:
+        return _skill_cache[agent_type]
+    async with async_session() as db:
+        svc = SkillService(db)
+        skills = await svc.get_by_agent_type(agent_type)
+        _skill_cache[agent_type] = skills
+        return skills
+
+def get_skills_for_agent(agent_type: str) -> list:
+    """同步接口：返回缓存的 Skills（供 Agent 节点调用）"""
+    return _skill_cache.get(agent_type, [])
+
+def build_skill_context(agent_type: str) -> str:
+    """
+    构建 Skill 上下文，供注入到 Agent 系统提示
+    格式：Skill 名称 + 描述 + 工具列表
+    """
+    skills = get_skills_for_agent(agent_type)
+    if not skills:
+        return ""
+    lines = ["\n\n【可用的 Skills】"]
+    for s in skills:
+        tools_str = ", ".join(s.tools) if s.tools else "无"
+        lines.append(f"""
+## {s.name}
+- 描述: {s.description or '无'}
+- 工具: {tools_str}
+- 指令: {s.instructions[:200]}...""" if len(s.instructions) > 200 else f"- 指令: {s.instructions}")
+    return "\n".join(lines)
+
+def clear_cache():
+    """清除缓存（配置变更时调用）"""
+    global _skill_cache
+    _skill_cache = {}
\ No newline at end of file