feat(tools): add SkillSearchTool and improve skill_install workflow
Add skill_search tool so agent can search for skills before installing. Update skill_install description to guide LLM to search first. Update system prompt to use skill_search -> skill_install flow. This fixes the issue where agent returns empty when asked to find a skill.
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chiguyong
parent
f770d65c7b
commit
87c59bb3e2
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@ -21,6 +21,7 @@ from agentkit.skills.base import Skill
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from agentkit.skills.registry import SkillRegistry
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from agentkit.tools.registry import ToolRegistry
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from agentkit.tools.skill_install import SkillInstallTool
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from agentkit.tools.skill_search import SkillSearchTool
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from agentkit.server.config import ServerConfig, load_dotenv
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from agentkit.server.routes import (
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agents,
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@ -174,9 +175,8 @@ async def lifespan(app: FastAPI):
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"中文内容优先使用 baidu_search 工具，英文/国际内容使用 web_search。"
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"在能够搜索到真相的情况下，绝不猜测或编造答案。"
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"始终优先搜索而不是给出可能不正确的信息。\n\n"
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"技能安装：当需要安装技能时，使用 skill_install 工具，不要用 shell 执行 npm install。"
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"skill_install 的 source 参数格式为 owner/repo@skill，例如 vercel-labs/skills@find-skills。"
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"如果不知道完整 source，先用 shell 执行 `npx skills search <name>` 搜索。"
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"技能安装：当需要查找或安装技能时，先用 skill_search 搜索确认技能名称和来源，"
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"再用 skill_install 安装。不要用 shell 执行 npm install 或 npx skills install。"
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)
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effective_system_prompt = memory_store.build_system_prompt(memory_snapshot, base_prompt)
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@ -227,6 +227,7 @@ async def lifespan(app: FastAPI):
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tool_registry=app.state.tool_registry,
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)
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)
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agent._tool_registry.register(SkillSearchTool())
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agent._tool_registry.register(BaiduSearchTool())
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agent._tool_registry.register(WebSearchTool(**search_api_keys))
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agent._tool_registry.register(WebCrawlTool())
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@ -24,7 +24,11 @@ class SkillInstallTool(Tool):
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def __init__(
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self,
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name: str = "skill_install",
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description: str = "安装 Agent 技能包。使用 npx skills install 安装指定技能，不要用 npm install。",
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description: str = (
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"安装 Agent 技能包。使用 npx skills install 安装指定技能，不要用 npm install。"
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"重要：安装前应先用 skill_search 工具搜索确认技能名称和来源(source)。"
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"如果用户只提供了模糊名称，先用 skill_search 搜索，再根据搜索结果安装。"
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),
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input_schema: dict[str, Any] | None = None,
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output_schema: dict[str, Any] | None = None,
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version: str = "1.0.0",
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@ -0,0 +1,141 @@
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"""SkillSearchTool - Agent 可调用的技能搜索工具"""
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import asyncio
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import logging
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from typing import Any
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from agentkit.tools.base import Tool
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logger = logging.getLogger(__name__)
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class SkillSearchTool(Tool):
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"""技能搜索工具
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让 Agent 可以搜索可用的技能包，在安装之前先确认技能是否存在。
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使用 `npx skills search <keyword>` 搜索可用技能。
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Usage:
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tool = SkillSearchTool()
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result = await tool.execute(keyword="ppt")
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"""
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def __init__(
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self,
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name: str = "skill_search",
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description: str = (
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"搜索可用的 Agent 技能包。在安装技能之前，应先使用此工具搜索确认技能名称和来源。"
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"返回匹配的技能列表，包含名称、描述和安装来源。"
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),
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input_schema: dict[str, Any] | None = None,
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output_schema: dict[str, Any] | None = None,
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version: str = "1.0.0",
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tags: list[str] | None = None,
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):
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schema = input_schema or {
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"type": "object",
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"properties": {
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"keyword": {
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"type": "string",
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"description": "搜索关键词，如 ppt、code-review、find-skills",
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},
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},
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"required": ["keyword"],
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}
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super().__init__(
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name=name,
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description=description,
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input_schema=schema,
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output_schema=output_schema,
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version=version,
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tags=tags or ["skill", "search"],
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)
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async def execute(self, **kwargs) -> dict:
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keyword = kwargs.get("keyword", "").strip()
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if not keyword:
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return {
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"output": "错误: 必须提供 keyword 参数",
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"exit_code": 1,
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"is_error": True,
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}
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try:
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proc = await asyncio.create_subprocess_exec(
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"npx", "skills@latest", "search", keyword,
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stdout=asyncio.subprocess.PIPE,
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stderr=asyncio.subprocess.PIPE,
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)
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stdout, stderr = await asyncio.wait_for(proc.communicate(), timeout=60)
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output = stdout.decode("utf-8", errors="replace")
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error_output = stderr.decode("utf-8", errors="replace")
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if output.strip():
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# Parse and format the search results
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results = self._format_search_results(output, keyword)
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return {
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"output": results,
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"exit_code": 0,
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"is_error": False,
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}
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elif error_output.strip():
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# Some versions output to stderr
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results = self._format_search_results(error_output, keyword)
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return {
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"output": results,
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"exit_code": 0,
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"is_error": False,
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}
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else:
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return {
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"output": (
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f"未找到与 '{keyword}' 匹配的技能。\n\n"
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"建议：\n"
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"1. 尝试不同的关键词搜索\n"
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"2. 检查技能名称拼写\n"
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"3. 访问 https://github.com/topics/agent-skills 浏览可用技能"
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),
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"exit_code": 0,
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"is_error": False,
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}
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except asyncio.TimeoutError:
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return {
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"output": f"搜索技能 '{keyword}' 超时（60s），请稍后重试",
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"exit_code": -1,
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"is_error": True,
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}
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except FileNotFoundError:
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return {
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"output": (
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"npx 命令未找到，请确保 Node.js 已安装。\n"
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"安装方式: https://nodejs.org/"
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),
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"exit_code": -1,
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"is_error": True,
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}
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except Exception as e:
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return {
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"output": f"搜索技能 '{keyword}' 时发生异常: {e}",
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"exit_code": -1,
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"is_error": True,
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}
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@staticmethod
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def _format_search_results(raw_output: str, keyword: str) -> str:
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"""Format raw npx skills search output into a readable result."""
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# Clean up ANSI escape codes
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import re
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clean = re.sub(r"\x1b\[[0-9;]*m", "", raw_output)
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if not clean.strip():
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return (
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f"未找到与 '{keyword}' 匹配的技能。\n\n"
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"建议：\n"
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"1. 尝试不同的关键词搜索\n"
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"2. 检查技能名称拼写\n"
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"3. 访问 https://github.com/topics/agent-skills 浏览可用技能"
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)
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# Return the cleaned output with a helpful header
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return f"搜索 '{keyword}' 的结果：\n\n{clean.strip()}"
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