- 📄 SKILL.md
task-orchestrator
Guide the full lifecycle of a feature-implementation tagged MCP item (the feature container) — from queue through review
Guide the full lifecycle of a feature-implementation tagged MCP item (the feature container) — from queue through review
Add proper Tracey spec annotations to code, find requirements, and check coverage. Use when working with projects that have Tracey configuration (.config/tracey/config.styx), when adding spec references to code, or when checking requirement coverage.
Use when working with crit CLI commands, review files, addressing review comments, leaving inline code review comments, sharing reviews via crit share/unpublish, pushing reviews to GitHub PRs, or pulling PR comments locally. Covers crit comment, crit share, crit unpublish, crit pull, crit push, review file format, and resolution workflow.
Node.js / Python 接口自动化与签名还原工程技能:对自有平台或已授权平台的 Web API 进行签名分析与接口对接, 通过 Camoufox 反检测浏览器动态调试与静态源码分析,定位并还原前端加密/签名逻辑, 使用 Node.js 或 Python 实现算法复现与自动化接口调用。 深度集成 camoufox-reverse MCP v0.8.0(C++ 引擎级指纹伪装,78 个逆向分析工具,域级 Session 档案 + 断言系统)。 擅长 JSVMP 虚拟机保护的双路径攻克:路径 A 算法追踪(Hook / 插桩 / 日志分析 / 源码级插桩四板斧, 通用对RS 5/6、Akamai sensor_data、webmssdk、obfuscator.io)、 路径 B 环境伪装(jsdom/vm 沙箱 + 浏览器环境采集对比 + 全量补丁)。 v2.6.0 新增反爬类型三分法(签名型/行为型/纯混淆)作为顶层决策框架,明确 pre_inject_hooks 与 hook_jsvmp_interpreter(mode="proxy") 对签名型反爬不可用, 引入 mode="transparent" 签名安全备选与 MCP 侧 AST 源码插桩(消除 CDN 依赖)。 v2.9.0 新增域级 Session 档案(跨任务复用反爬判定/指纹基准/Cookie 归因)与断言驱动交付体系, Phase 5 升级为断言驱动结构化交付,新增降级梯度原则防止 AI 过早放弃。
Semantic code search and AI-powered codebase Q&A across indexed repositories. Use when understanding code beyond local files, exploring dependencies, discovering cross-project patterns, planning features, debugging, or onboarding. Queries like "How does X work?", "Show me Y patterns", "How is library Z used?". The default path is semantic search plus grep search; chat-with-codebase is slower, more expensive, and usually secondary.
微信自动化操作 skill,用于帮助用户快速完成微信消息群发、文件发送、群管理、聊天记录获取等自动化任务。当用户需要批量发送微信消息、管理微信群、获取聊天记录、或进行其他微信自动化操作时使用此 skill。
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Example Claude skill
Build production-ready Tavily integrations with best practices baked in. Reference documentation for developers using coding assistants (Claude Code, Cursor, etc.) to implement web search, content extraction, crawling, and research in agentic workflows, RAG systems, or autonomous agents.
直接调用后端 API,直接控制项目配置、文件管理、知识库、MCP 等核心功能
This fixture contains malformed JSON on purpose.
Query previous pi sessions to retrieve context, decisions, code changes, or other information. Use when you need to look up what happened in a parent session or any other session file.
skill-sample/ ├─ SKILL.md ⭐ Required: skill entry doc (purpose / usage / examples / deps) ├─ manifest.sample.json ⭐ Recommended: machine-readable metadata (index / validation / autofill) ├─ LICENSE.sample ⭐ Recommended: license & scope (open source / restriction / commercial) ├─ scripts/ │ └─ example-run.py ✅ Runnable example script for quick verification ├─ assets/ │ ├─ example-formatting-guide.md 🧩 Output conventions: layout / structure / style │ └─ example-template.tex 🧩 Templates: quickly generate standardized output └─ references/ 🧩 Knowledge base: methods / guides / best practices ├─ example-ref-structure.md 🧩 Structure reference ├─ example-ref-analysis.md 🧩 Analysis reference └─ example-ref-visuals.md 🧩 Visual reference
More Agent Skills specs Anthropic docs: https://agentskills.io/home
├─ ⭐ Required: YAML Frontmatter (must be at top) │ ├─ ⭐ name : unique skill name, follow naming convention │ └─ ⭐ description : include trigger keywords for matching │ ├─ ✅ Optional: Frontmatter extension fields │ ├─ ✅ license : license identifier │ ├─ ✅ compatibility : runtime constraints when needed │ ├─ ✅ metadata : key-value fields (author/version/source_url...) │ └─ 🧩 allowed-tools : tool whitelist (experimental) │ └─ ✅ Recommended: Markdown body (progressive disclosure) ├─ ✅ Overview / Purpose ├─ ✅ When to use ├─ ✅ Step-by-step ├─ ✅ Inputs / Outputs ├─ ✅ Examples ├─ 🧩 Files & References ├─ 🧩 Edge cases ├─ 🧩 Troubleshooting └─ 🧩 Safety notes
Skill files are scattered across GitHub and communities, difficult to search, and hard to evaluate. SkillWink organizes open-source skills into a searchable, filterable library you can directly download and use.
We provide keyword search, version updates, multi-metric ranking (downloads / likes / comments / updates), and open SKILL.md standards. You can also discuss usage and improvements on skill detail pages.
Quick Start:
Import/download skills (.zip/.skill), then place locally:
~/.claude/skills/ (Claude Code)
~/.codex/skills/ (Codex CLI)
One SKILL.md can be reused across tools.
Everything you need to know: what skills are, how they work, how to find/import them, and how to contribute.
A skill is a reusable capability package, usually including SKILL.md (purpose/IO/how-to) and optional scripts/templates/examples.
Think of it as a plugin playbook + resource bundle for AI assistants/toolchains.
Skills use progressive disclosure: load brief metadata first, load full docs only when needed, then execute by guidance.
This keeps agents lightweight while preserving enough context for complex tasks.
Use these three together:
Note: file size for all methods should be within 10MB.
Typical paths (may vary by local setup):
One SKILL.md can usually be reused across tools.
Yes. Most skills are standardized docs + assets, so they can be reused where format is supported.
Example: retrieval + writing + automation scripts as one workflow.
Some skills come from public GitHub repositories and some are uploaded by SkillWink creators. Always review code before installing and own your security decisions.
Most common reasons:
We try to avoid that. Use ranking + comments to surface better skills: