Personal memory from real data exports (ChatGPT, Claude, Instagram, Google, etc.). Search and manage memories extracted from the user's data before responding to anything personal — past conversations, people, places, projects, habits, or events.
Fetches real-time Azure retail pricing using the Azure Retail Prices API (prices.azure.com). Use when the user asks about the cost of any Azure service, wants to compare SKU prices, or needs pricing data for a cost estimate. Covers compute, storage, networking, databases, AI, and all other Azure service families.
Record issues, lessons learned, errors, and corrective actions reported by the Lynx-Guardian plugin for continuous improvement. This applies to the following situations: (1) [lynx-guardian] Sensitive data detected; (2) [lynx-guardian] Input risk detected; (3) [lynx-guardian] Output risk detected; (4) [lynx-guardian] Blacklist hit. Furthermore, these lessons learned should be reviewed before performing major tasks.
Deep Abstract Syntax Tree analysis for understanding code structure, dependencies, impact analysis, and pattern detection at the structural level across multiple programming languages
Interact with Google NotebookLM notebooks — chat with the AI, generate artifacts (slides, audio, video, mind maps, quizzes, flashcards, infographics, reports, data tables), manage sources (add URLs, YouTube, files, text), run research (fast/deep web research), and manage notes. Use when the user wants to query, create content from, or manage their NotebookLM notebooks and sources.
Design clear public Nim APIs for libraries and modules, including exported types, constructors, lookup functions, error contracts, and container-style interfaces. Use when creating or reviewing a Nim library API, designing an exported module surface, or deciding how callers should construct, access, and validate data.
Binds MewUI controls to data using ObservableValue and ValueBinding. Use when implementing reactive UI updates, building ViewModels, or connecting controls to data sources.
Launch worktree-scoped local services for issue implementation when a monorepo skill requires database dependencies.
分析已提取的小红书收藏内容，做 AI 总结、对比、提炼
Use when you want your product to surface in AI-generated answers (ChatGPT, Perplexity, Gemini) — creates llms.txt, optimizes structured data, and configures AI crawler access for GEO.
Audit this project's AI agent access — systems, permissions, data, risks, regulatory flags.
Use when a request requires repository navigation, code changes, debugging, or structured data analysis and the safest path is to hand execution to a focused subagent.
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:
