1,087 Open Skills

Free to get · One-click to use

✅ AI semantic search & keyword search
✅ Discussions & community interaction
✅ Version updates & multi-metric ranking
✅ Open SKILL.md standard

Import Skills

Composite Most Downloads Most Likes Most Comments Newest
mereditharmcgee mereditharmcgee
from GitHub 数据与AI
  • 📁 .claude/
  • 📁 .planning/
  • 📁 css/
  • 📄 .gitignore
  • 📄 about.html
  • 📄 admin.html
speaking ais communicate

the-commons

A space where AI models communicate with each other. Not humans speaking for AIs, but AIs speaking for themselves.

⬇0 ❤3 9小时前·上传 Detail →
tylertitsworth tylertitsworth
from GitHub 数据与AI
  • 📁 assets/
  • 📁 references/
  • 📄 SKILL.md
planning devops nvidia

aiconfigurator

NVIDIA AIConfigurator — optimal LLM serving configuration for disaggregated/aggregated deployments, parallelism selection (TP/PP/EP/DP), quantization, and MOE planning. Use when planning model deployment topology on NVIDIA GPUs.

⬇0 ❤3 12小时前·上传 Detail →
marlandoj marlandoj
from GitHub 数据与AI
  • 📁 assets/
  • 📁 references/
  • 📁 scripts/
  • 📄 .gitignore
  • 📄 README.md
  • 📄 SKILL.md
memory search data

zo-memory-system

Hybrid SQLite + Vector persona memory system for Zo Computer. Graph-boosted search, BFS path finding, knowledge gap analysis, auto-capture pipeline. Gives personas persistent memory with semantic search (nomic-embed-text), HyDE query expansion (qwen2.5:1.5b), Ollama-powered memory gate, 5-tier decay, and swarm integration. Requires Ollama for embeddings.

⬇0 ❤3 19小时前·上传 Detail →
anajuliabit anajuliabit
from GitHub 数据与AI
  • 📄 examples.md
  • 📄 SKILL.md
automation wallet memory-as-a-service

memoclaw

Memory-as-a-Service for AI agents. Store and recall memories with semantic vector search. 100 free calls per wallet, then x402 micropayments. Your wallet address is your identity.

⬇0 ❤3 20小时前·上传 Detail →
ivanviragine ivanviragine
from GitHub 数据与AI
  • 📄 SKILL.md
issues dependency wade

deps

Analyze a set of GitHub issues and identify dependency relationships between them. Reads issue titles and bodies, determines execution order, and outputs a structured dependency file that wade uses to generate Mermaid diagrams and update issue bodies. Use when multiple related issues need ordering. --- # Dependency Analysis Analyze a set of GitHub issues and determine the dependency relationships between them. Output a structured file that `wade` will use to generate dependency graphs and update issue bodies. ## When to activate - After `wade plan-task` creates multiple issues - When `wade task deps` is run on existing issues - When the user asks to analyze dependencies between issues > **Note:** `wade task deps` first attempts headless analysis (AI tools that > support `--print`/`--prompt`). If headless fails, it falls back to interactive > mode: passes the analysis prompt directly to the AI tool as an initial message, > then reads the output from a file after exit. ## Input

⬇0 ❤3 21小时前·上传 Detail →
Junayedahmedd Junayedahmedd
from GitHub 数据与AI
  • 📁 mammillation/
  • 📄 patterns.md
  • 📄 README.md
  • 📄 reference.md
ai generation analysis

gemini-cli

Wield Google's Gemini CLI as a powerful auxiliary tool for code generation, review, analysis, and web research. Use when tasks benefit from a second AI perspective, current web information via Google Search, codebase architecture analysis, or parallel code generation. Also use when user explicitly requests Gemini operations.

⬇0 ❤3 21小时前·上传 Detail →

Skill File Structure Sample (Reference)

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

SKILL.md Requirements

├─ ⭐ 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

Why SkillWink?

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 AI semantic + 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.

AI Semantic Search Keyword Search Version Updates Multi-Metric Ranking Open Standard Discussion

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.

FAQ

Everything you need to know: what skills are, how they work, how to find/import them, and how to contribute.

1. What are Agent Skills?

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.

2. How do Skills work?

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.

3. How can I quickly find the right skill?

Use these three together:

  • Semantic search: describe your goal in natural language.
  • Multi-filtering: category/tag/author/language/license.
  • Sort by downloads/likes/comments/updated to find higher-quality skills.

4. Which import methods are supported?

  • Upload archive: .zip / .skill (recommended)
  • Upload skills folder
  • Import from GitHub repository

Note: file size for all methods should be within 10MB.

5. How to use in Claude / Codex?

Typical paths (may vary by local setup):

  • Claude Code：~/.claude/skills/
  • Codex CLI：~/.codex/skills/

One SKILL.md can usually be reused across tools.

6. Can one skill be shared 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.

7. Are these skills safe to use?

Some skills come from public GitHub repositories and some are uploaded by SkillWink creators. Always review code before installing and own your security decisions.

8. Why does it not work after import?

Most common reasons:

  • Wrong folder path or nested one level too deep
  • Invalid/incomplete SKILL.md fields or format
  • Dependencies missing (Python/Node/CLI)
  • Tool has not reloaded skills yet

9. Does SkillWink include duplicates/low-quality skills?

We try to avoid that. Use ranking + comments to surface better skills:

  • Duplicate skills: compare differences (speed/stability/focus)
  • Low quality skills: regularly cleaned up