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技能侦察兵

在创建新技能之前,搜索本地、市场、GitHub 和 Web 上的现有技能源。当用户想要为某个工作流程创建、构建、复刻或查找技能时使用。

技能侦察兵 是什么?

Skill Scout 是一个 AI 代理技能,旨在防止重复发明轮子。在用户表示想要创建新技能时,它会自动触发。该技能首先从本地安装的技能和市场插件中查找,然后扩展至 GitHub 和 Web 搜索。它会根据关键词匹配、描述匹配、来源可靠性(本地 > GitHub > Web)和仓库维护状态对结果进行排名,并将最终结果限制为 10 个。在推荐任何外部技能之前,Skill Scout 会强制进行安全审查:它会读取 SKILL.md 以查找意外的命令或凭据处理。最后,它会向用户提供清晰的选择:按原样使用现有技能、复刻并扩展它,或创建全新的技能。Skill Scout 支持所有基于 Claude Code 的平台(包括 CLI 和 IDE 插件),并与现有的 `search-first`、`skill-stocktake` 和 `agent-sort` 工作流程协同工作。它通过确保每次新技能的创建都是真的必要,帮助团队和组织维护高质量的技能目录。

兼容平台✓Claude Code~Codex CLI~Cursor
npx skills add https://github.com/affaan-m/everything-claude-code/tree/main/skills/skill-scout

在你喜欢的 AI 中提问

打开一个已预加载此 Agent Skill 的新对话。

文档

Skill Scout

在创建新技能之前使用此技能。目标是避免重复现有的社区或市场工作,同时在采用任何外部技能之前进行审查。

来源:来自社区过时的 PR #1232,由 redminwang 提供。

何时使用

  • 用户说“创建一个技能”、“构建一个技能”、“制作一个技能”或“新技能”。
  • 用户问“有没有一个技能可以做 X?”或“是否存在一个做 Y 的技能?”
  • 用户描述了一个工作流程,而您正准备建议创建一个新技能。
  • 用户想要复刻或扩展现有技能。

如果用户明确表示跳过搜索或从头创建,请确认这一点,然后按请求的创建工作流程进行。

工作原理

步骤 1 - 捕获意图

提取:

  • 该技能应执行的任务。
  • 使用它的触发条件。
  • 涉及的领域、工具、框架或数据源。
  • 三到五个搜索关键词以及有用的同义词。

步骤 2 - 搜索本地源

首先搜索已安装的和市场技能名称。本地源是首选,因为它们已经在用户的环境中。

find ~/.claude/skills -maxdepth 2 -name SKILL.md 2>/dev/null | grep -iE "keyword|synonym"
find ~/.claude/plugins/marketplaces -path '*/skills/*/SKILL.md' 2>/dev/null | grep -iE "keyword|synonym"

然后搜索 frontmatter 描述:

grep -RilE "keyword|synonym" ~/.claude/skills ~/.claude/plugins/marketplaces 2>/dev/null

步骤 3 - 搜索远程源

使用可用的 GitHub 和 Web 搜索工具。优先使用简洁的查询:

gh search repos "claude code skill keyword" --limit 10 --sort stars
gh search code "name: keyword" --filename SKILL.md --limit 10

对于 Web 搜索,最多使用三个有针对性的查询,例如:

"claude code skill" keyword
"SKILL.md" keyword
"everything-claude-code" keyword

步骤 4 - 审查外部匹配项

在推荐任何外部技能用于采用或复刻之前:

  • 阅读 SKILL.md 的 frontmatter 和说明。
  • 查找意外的 shell 命令、文件写入、网络调用、凭据处理或软件包安装。
  • 检查仓库是否看起来仍在维护中。
  • 优先将技能复制到新的本地分支并审查差异,而不是直接编辑市场原始版本。

步骤 5 - 对结果进行排名

按以下方式对候选者排名:

  1. 技能名称中的精确关键词匹配。
  2. 描述中的关键词或同义词匹配。
  3. 本地安装或市场来源。
  4. 有近期活动的维护中的 GitHub 来源。
  5. 仅 Web 提及。

将最终列表限制为 10 个结果。

步骤 6 - 呈现决策选项

给用户一个简短的表格:

选项含义
使用现有按原样调用或安装一个匹配的技能。
复刻或扩展复制最接近的技能并进行修改。
创建全新在确认没有接近的匹配后,构建一个新的技能。

只有在用户选择该路径或搜索未找到接近匹配后,才创建新技能。

示例

结果表格

| # | 技能 | 来源 | 匹配原因 | 差距 |
| --- | --- | --- | --- | --- |
| 1 | article-writing | 本地 ECC | 起草文章和指南 | 不专注于发布说明 |
| 2 | content-engine | 本地 ECC | 多格式内容工作流程 | 比需要的更重 |
| 3 | blog-writer | GitHub | 写博客技能,有近期提交 | 需要安全审查 |

面向用户的摘要

我找到了两个接近的本地匹配和一个外部候选。最接近的是 `article-writing`;它涵盖了起草和修订,但不包含您要求的发布说明检查清单。我可以按原样使用它,将其复刻成一个发布说明的变体,或者创建一个全新的技能。

反模式

  • 在搜索结果合理时,不要直接跳到新技能创建。
  • 未经阅读,不要安装外部技能。
  • 不要呈现一长串未排序的弱匹配项。
  • 不要将仅 Web 提及视为可信来源。
  • 不要直接编辑已安装的市场原始版本。

相关

  • search-first - 搜索优先于构建的工作流程。
  • skill-stocktake - 审计已安装技能的健康状况、重复和差距。
  • agent-sort - 对现有代理和技能进行分类和组织。

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

accessibility

Design, implement, and audit inclusive digital products using WCAG 2.2 Level AA. Use when building or auditing UI that must meet WCAG 2.2 Level AA, or when reviewing a change for keyboard, contrast, or screen-reader support.

affaan-m/claude-api

Anthropic Claude API patterns for Python and TypeScript. Covers Messages API, streaming, tool use, vision, extended thinking, batches, prompt caching, and Claude Agent SDK. Use when building applications with the Claude API or Anthropic SDKs.

affaan-m/everything-claude-code

End-to-end marketing campaign planning and execution. Covers audience research, positioning, campaign angle definition, landing page copy, email sequences, social posts, ad copy, short-form video scripts, and content calendars. Use as the orchestration layer for multi-channel product launches. Use when planning or executing a multi-channel product launch, or producing landing page, email, social, or ad copy.

affaan-m/everything-claude-code

Development conventions and patterns for everything-claude-code. JavaScript project with conventional commits.

affaan-m/everything-claude-code-conventions

Development conventions and patterns for everything-claude-code. JavaScript project with conventional commits.

affaan-m/frontend-design

Create distinctive, production-grade frontend interfaces with high design quality. Use when the user asks to build web components, pages, or applications and the visual direction matters as much as the code quality.

affaan-m/gget

gget CLI and Python workflow for quick genomic database queries, sequence lookup, BLAST-style searches, enrichment checks, and reproducible bioinformatics evidence logs.

affaan-m/literature-review

Systematic literature-review workflow for academic, biomedical, technical, and scientific topics, including search planning, source screening, synthesis, citation checks, and evidence logging.

affaan-m/motion-ui

Production-ready UI motion system for React/Next.js. Use when implementing animations, transitions, or motion patterns.

affaan-m/project-guidelines-example

Example project-specific skill template based on a real production application.

affaan-m/pubmed-database

Direct PubMed and NCBI E-utilities search workflows for biomedical literature, MeSH queries, PMID lookup, citation retrieval, and API-backed literature monitoring.

affaan-m/scholar-evaluation

Structured scholarly-work evaluation for papers, proposals, literature reviews, methods sections, evidence quality, citation support, and research-writing feedback.

affaan-m/uspto-database

USPTO patent and trademark data workflow for official record lookup, PatentSearch queries, TSDR checks, assignment data, and reproducible IP research logs.

agent-architecture-audit

Full-stack diagnostic for agent and LLM applications. Audits the 12-layer agent stack for wrapper regression, memory pollution, tool discipline failures, hidden repair loops, and rendering corruption. Produces severity-ranked findings with code-first fixes. Essential for developers building agent applications, autonomous loops, or any LLM-powered feature. Use when an agent or LLM feature misbehaves and the failing layer is unknown, or before shipping an agent stack.

agent-eval

Head-to-head comparison of coding agents (Claude Code, Aider, Codex, etc.) on custom tasks with pass rate, cost, time, and consistency metrics. Use when choosing between coding agents, or when a change to an agent setup needs measured pass rate, cost, and time rather than an impression.

agent-harness-construction

Design and optimize AI agent action spaces, tool definitions, and observation formatting for higher completion rates. Use when defining or revising an agent

agentic-engineering

Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing. Use when planning or executing engineering work that agents will carry out end to end.

agentic-os

Build persistent multi-agent operating systems on Claude Code. Covers kernel architecture, specialist agents, slash commands, file-based memory, scheduled automation, and state management without external databases. Use when building a persistent multi-agent system on Claude Code with its own memory, commands, and scheduling.

agent-introspection-debugging

Structured self-debugging workflow for AI agent failures using capture, diagnosis, contained recovery, and introspection reports. Use when an agent run fails and you need a reproducible diagnosis instead of a retry.

agent-payment-x402

Add x402 payment execution to AI agents with per-task budgets, spending controls, and non-custodial wallets. Supports Base through agentwallet-sdk and X Layer through OKX Payments / OKX Agent Payments Protocol. Use when an agent must pay for something itself and needs per-task budgets, spending controls, and a non-custodial wallet.

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