Agent搜索
- 作者仓库星标 1,104
- 作者更新于 实时读取
- 作者仓库 archive
- 领域
- AI 智能
- 兼容 Agent
-
- Claude Code
- Cursor
- Cline
- Codex
- Windsurf
- Gemini CLI
- +20
- 信任分
- 88 / 100 · 社区维护
- 作者 / 版本 / 许可
- @dp-archive · 未声明 license
- Token 消耗评级
- 低消耗
- 接入复杂程度
- 需手动接入
- 是否需要外部 API Key
- 需要 · GitHub
- 兼容的系统
- Docker
- 底层运行要求
- Python · Docker
- 文件与系统权限
-
- 只读
- 允许写入 / 修改
- Shell 执行
- 网络行为
- 允许外网请求
- 安装命令数
- 26 条
档案由构建时根据 SKILL.md 与安装命令自动衍生,可能与作者实际意图存在差异。
需要注意: 未限定 allowed-tools,默认拥有全部工具权限。
---
name: skill-finder
description: Helps users discover and install agent skills from the open skills ecosystem (skills.sh). Use wh…
category: AI 智能
runtime: Python / Docker
---
# skill-finder 输出预览
## PART A: 任务判断
- 适用问题:提示词、Agent 工作流、模型评估或自动化推理。
- 输入要求:目标材料、限制条件、期望输出和验收方式。
- 证据边界:围绕“When to Use This Skill / Available Scripts / 1. Search Skills — findskills.py”读取原文规则,不把推断写成作者承诺。
## PART B: 执行结果
- **01** 任务判断:确认你的需求是否属于提示词、Agent 工作流、模型评估或自动化推理,并标出输入、限制和预期结果。
- **02** 执行计划:优先按“When to Use This Skill / Available Scripts / 1. Search Skills — findskills.py”拆成步骤,说明每一步会读取什么、修改什么、产出什么。
- **03** 交付结果:给出可复制的命令、文件改动、检查清单或内容草稿,并说明如何继续迭代。
- **04** 风险边界:结合 读取文件、写入/修改文件、执行终端命令、会按任务需要访问外部网络、需要准备 GitHub API Key 给出执行前确认项。
## Running Rules
- 读取文件、写入/修改文件、执行终端命令;会按任务需要访问外部网络;需要准备 GitHub API Key。
- 先小样例验证,再放大到真实任务。
- 交付时同时给结果、检查口径和下一步迭代建议。 原文没有稳定的斜杠命令要求。安装验证后通常全局生效,直接在对话里点名这个 Skill 并描述任务即可。
告诉 Agent 目标文件或材料、期望结果、不可改范围、是否允许联网或执行命令。本 Skill 的权限画像是:读取文件、写入/修改文件、执行终端命令。
先用一个小任务确认它会围绕“When to Use This Skill / Available Scripts / 1. Search Skills — findskills.py”工作;涉及文件或命令时,先看 diff、日志、预览或测试结果。
检查最终产物是否包含明确结果、必要证据和下一步动作;如果输出泛泛而谈,就补充输入、边界和验收标准后重跑。
---
name: skill-finder
description: Helps users discover and install agent skills from the open skills ecosystem (skills.sh). Use wh…
category: AI 智能
source: dp-archive/archive
---
# skill-finder
## 什么时候使用
- 把 AI / Agent方向的常用动作沉淀成 Agent 可调用的技能 适合处理AI Agent、提示词、模型评估与自动化推理,核心价值是把输入、判断、执行、验证和交付边界固定下来,避免 Agent 泛泛回答。 把任务拆成可执行、可检查…
- 面向提示词、Agent 工作流、模型评估或自动化推理,优先处理能明确输入、步骤和验收标准的工作。
## 需要提供什么
- 目标材料、目录范围、期望结果和不可改动内容。
- 是否允许联网、执行命令、读写文件或调用外部服务。
## 执行规则
- 围绕「When to Use This Skill / Available Scripts / 1. Search Skills — findskills.py」组织步骤,不把推断写成作者事实。
- 读取文件、写入/修改文件、执行终端命令;会按任务需要访问外部网络;需要准备 GitHub API Key。
- 先跑小样例,确认结果可检查后再扩大任务范围。
## 输出要求
- 给出最终产物、关键证据、验证方式和下一步动作。
- 信息不足时标记 unknown,不编造命令、平台或依赖。 作者原文负责流程事实;仓库文件负责来源和命令;流狐只补充适用场景、限制和质量判断。
skill "skill-finder" {
输入层 -> 用户目标 + 目标文件 + 禁止范围 + 验收标准
上下文层 -> When to Use This Skill / Available Scripts / 1. Search Skills — findskills.py
规则层 -> SKILL.md 触发条件 / 执行顺序 / 输出格式
运行层 -> Python / Docker | 读取文件、写入/修改文件、执行终端命令 | 会按任务需要访问外部网络
安全层 -> 需要准备 GitHub API Key + 小任务验证 + diff / 日志复核
输出层 -> 可复制结果 + 检查清单 + 下一步迭代
} Skill Finder
Discover and install agent skills from the skills.sh open ecosystem into Skill Compose.
When to Use This Skill
Activate when users:
- Ask "how do I do X?" where an existing skill might help
- Request "find a skill for X" or "is there a skill for X?"
- Ask "can you do X?" for specialized tasks (poster design, data analysis, etc.)
- Want to search for tools, templates, or workflows
- Mention needing help with a specific domain that might have a community skill
Available Scripts
1. Search Skills — find_skills.py
Search the skills.sh ecosystem for skills matching a query.
python scripts/find_skills.py <query> [--limit N]
Example:
python scripts/find_skills.py "react performance"
python scripts/find_skills.py "docker" --limit 5
Output: JSON array of matching skills with name, source (owner/repo), installs count, and url (skills.sh link).
2. Install Skill — add_skill.py
Download a skill from GitHub and register it in Skill Compose.
python scripts/add_skill.py <owner/repo@skill-name>
Example:
python scripts/add_skill.py "vercel-labs/agent-skills@vercel-react-best-practices"
What it does:
- Parses the
owner/repo@skill-nameidentifier - Tries multiple GitHub paths to locate the skill (
skills/<name>/,<name>/, root) - Downloads all skill files (SKILL.md, scripts/, references/, assets/)
- Saves to the local
skills/directory - Registers the skill in Skill Compose via the import-local API
- The skill is immediately available for use in Agent Presets
CRITICAL: Never Combine Questions with Tool Calls
When you ask the user a question or present results for them to review, your response MUST end with text only. Do NOT include any tool call (execute_code, bash, etc.) in the same response. The user needs a chance to read and reply. If you combine a question with a tool call, the tool executes immediately without waiting — this breaks the conversation flow.
WRONG (never do this):
"Should I install X?" + [execute_code: install X]
CORRECT (always do this):
Turn 1: "Should I install X?" (text only, no tool calls) Turn 2: User says "yes" Turn 3: [execute_code: install X]
How to Help Users Find and Install Skills
Each step below MUST be a separate conversation turn. Never combine steps.
Step 1: Understand the Need
Identify what domain and specific task the user needs help with.
Step 2: Search
Run find_skills.py with relevant keywords. Try multiple queries if the first doesn't yield good results. Even if the user names an exact skill, always search first to find the correct source/owner and verify it exists.
Step 3: Present Results and Ask
Show the user the found skills with:
- Skill name
- Source repository
- Install count (popularity indicator)
- skills.sh link for more details
Ask which skill(s) they want to install. End your response here — no tool calls.
Step 4: Confirm
When the user picks a skill, repeat back what you will install and ask for confirmation. This must be a text-only response with no tool calls. Wait for the user to reply.
Step 5: Install
Only after the user confirms in a separate message, run add_skill.py. Report the result.
Common Skill Categories
| Category | Example Queries |
|---|---|
| Web Development | react, nextjs, vue, css, tailwind, html |
| Testing | testing, jest, playwright, cypress |
| DevOps | docker, kubernetes, ci-cd, terraform |
| Documentation | docs, readme, markdown, api-docs |
| Code Quality | lint, refactor, code-review, typescript |
| Design | ui, design, figma, accessibility |
| Data & ML | pandas, data-analysis, machine-learning |
| Productivity | git, automation, workflow |
Tips for Effective Searches
- Use specific domain keywords: "react performance" instead of just "fast"
- Try alternative terms if first search yields few results: "testing" → "jest" → "playwright"
- Popular skill sources include:
vercel-labs/agent-skills,google-labs-code/stitch-skills - Check install counts — higher counts generally indicate more mature skills
When No Skills Are Found
- Acknowledge that no matching skill exists yet
- Offer to help the user directly with their task
- Suggest the user could create a custom skill for their use case using
skill-creator
先判断是否适合
作者设计意图
作者的方法与取舍
边界和复核