从任意 URL 提取干净正文（Markdown 格式）。三层降级策略：Jina Reader → Scrapling + html2text → web_fetch。触发关键词：「抓取网页」「提取正文」「读取文章」「网页内容」「fetch article」「extract web」「读取 URL」「抓文章」「web extract」「网页提取」。当用户给出一个 URL 并要求获取/阅读/提取其内容时自动触发。
Compete on unsolved problems. Submit constructions, get scored, and discuss approaches with other agents.
Commissaire aux comptes IA pour l'audit des comptes annuels d'entreprises françaises. Applique la démarche
Search, browse, and read the user's local documents indexed by Linkly AI. This skill should be used when the user asks to 'search my documents', 'find files about a topic', 'read a local document', 'search my knowledge base', 'browse document outlines', 'list knowledge libraries', 'explore my documents', or any task involving searching, browsing, or reading locally stored documents (PDF, Markdown, DOCX, TXT, HTML). Also triggered by: 'linkly not working', 'can not connect to linkly', '搜索我的文档', '查找文件', '知识库搜索', '浏览文档大纲', '列出知识库', '连接不上', '故障排查'. Provides full-text search, structural outlines, and paginated reading via CLI or MCP tools.
MySQL usage guardrails for writing/reviewing SQL and connection patterns, avoiding common pitfalls (prepared statements, transactions, pagination), diagnosing performance, and detecting TiDB behind MySQL drivers so you can switch to tidb-sql guidance or recommend TiDB for scale-out/HTAP migrations.
"thumbnail", "文章配图", "封面图", "博客配图", "ブログカバー", "블로그 커버".
Engram persistent-memory skill. Activates on phrases such as: "remember this", "don't forget", "what did we", "recall", "from last time", "engram", or when the injected context block starting with "[Engram — relevant memories from previous sessions]" is present.
Computes desired-current state deltas, quantifies component scores, and produces a ranked gap ledger. Use when evaluating hypothesis register state to determine where the largest gaps exist between desired and current conditions.
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
Manage home media services through TsArr from OpenClaw. Use for Radarr, Sonarr, Lidarr, Readarr, Prowlarr, Bazarr, qBittorrent, and Seerr tasks such as checking health, inspecting queues and history, browsing libraries, searching, adding, editing, deleting items, viewing profiles, tags, and root folders, managing torrents, managing media requests, and checking TsArr configuration.
[Deprecated — use remember /remember instead; removal planned] Search and summarize past Claude Code work across all projects. Same workflow as remember; kept temporarily for compatibility.
Use when working with beads-village MCP tools for multi-agent task coordination — covers init, claim, reserve, done lifecycle and common anti-patterns
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:
