Summary

This repository curates "Agent Skills," a standardized format for AI coding agents to acquire new capabilities dynamically. Designed for AI assistants like Claude, Copilot, and VS Code, Agent Skills enable on-demand learning through simple text files (SKILL.md), making AI assistants more efficient, lighter, and easier to extend. The primary benefit is a modular approach to AI functionality, allowing users and developers to share and integrate specific task instructions across various compatible platforms without modifying the core AI model.

How It Works

Agent Skills operate on a three-stage loading process: Browse (AI sees skill names and descriptions), Load (AI reads the full SKILL.md instructions when a skill is required), and Use (AI executes the instructions). This on-demand mechanism ensures the AI remains performant by only loading necessary capabilities. Skills are fundamentally human-readable instructions, not executable code, which simplifies creation, sharing, and broad compatibility across different AI agent frameworks.

Quick Start & Requirements

Integration varies by agent:

• Claude: Place skills in ~/.claude/skills/. Official tutorials available here and here.
• Codex: Use the $skill-creator skill or manually create SKILL.md files. Install skills via $skill-installer. Documentation: developers.openai.com.
• VS Code / Copilot CLI: Store skills in .github/skills/ or .claude/skills/ within the workspace. Documentation: code.visualstudio.com.
• MCP Servers: Can be set up using npx (e.g., @modelcontextprotocol/server-filesystem). Documentation: modelcontextprotocol.com.

Highlighted Details

• Official Skills: Includes Anthropic's document processing skills (docx, xlsx, pptx, pdf) and HuggingFace's tools for dataset creation, model evaluation, and LLM training.
• Broad Compatibility: Supports Claude (Claude.ai, Claude Code), GitHub Copilot, VS Code, and other agents adopting the Model Context Protocol (MCP).
• Diverse Community Skills: Features collections for AWS development, D3.js visualizations, Playwright automation, Git operations, security threat hunting, and more.
• Skill Structure: Skills are organized in directories containing a SKILL.md file, optionally supplemented by scripts/, templates/, and resources/.

Maintenance & Community

The repository follows an open development model, welcoming contributions from the ecosystem. Community resources include links to LangChain tools, Google Search wrappers, Wikipedia fetchers, and Python REPL execution.

Licensing & Compatibility

The project is licensed under the MIT License, permitting broad use and modification. Compatibility is high across supported AI agents, with skills designed to be platform-agnostic instruction sets.

Limitations & Caveats

Agent Skills themselves are instructional and do not execute code; actual task execution often requires integration with companion tools or MCP servers. Community-contributed skills should be verified for reliability and security before adoption. The README advises keeping SKILL.md files under 500 lines for optimal performance.