Explore AI Agent Skills & Claude Prompts
Discover open-source agent skills for Claude Code, Codex, ChatGPT, and any tool that uses SKILL.md.
Enter through keywords, occupations, creators, and GitHub sources to see what kinds of skills are emerging across domains.
Use the same catalog through the API
Connect 381,784 public skills to your own search, analytics, or agent workflow with the REST API.
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droo-stack
by DROOdotFOODetailed coding patterns for a polyglot stack. TRIGGER when: working in Elixir, TypeScript, Go, Rust, Python, Lua, C, Zig, Shell/Bash, Noir, or chezmoi templates. Provides incorrect/correct examples that complement CLAUDE.md preferences. DO NOT TRIGGER when: working with Claude API or Anthropic SDK (use claude-api skill), Raxol TUI/agent framework patterns (use raxol skill), Solidity smart contracts (use solidity-auditor skill), ZK circuit domain questions (use noir skill -- this skill only covers Noir language syntax), NIF/SIMD domain questions for BEAM integration (use native-code skill -- this skill only covers C and Zig general syntax), or ffmpeg upstream / libav* assembly work (use ffmpeg-asm skill).
ffmpeg-asm
by DROOdotFOOffmpeg upstream contributions (hand-written assembly in libavcodec/libavfilter/libswscale) and custom ffmpeg integration (building, embedding, calling libav* APIs). Covers x86_64 (SSE/AVX/AVX2/AVX-512) and AArch64 (NEON/SVE/SVE2) idioms specific to ffmpeg's x86inc.asm and aarch64/asm.S macro frameworks, the checkasm and FATE test harnesses, and the ffmpeg upstream patch workflow. TRIGGER when: editing .asm files under libav*/x86/, .S files under libav*/aarch64/ or libav*/arm/, working with x86inc.asm, x86util.asm, or aarch64/asm.S macros, writing *_init.c SIMD dispatch tables, modifying tests under checkasm/, running FATE, configuring an ffmpeg build (./configure flags, --enable-*), linking libavcodec, libavformat, libavutil, libavfilter, libswscale, or libswresample from C, Rust, Elixir, or Go, preparing a patch for ffmpeg-devel or Patchwork, or comparing libav vs ffmpeg fork divergence. DO NOT TRIGGER when: general SIMD or intrinsics questions outside ffmpeg's macro framework (use droo-stack for C/Rust synt
native-code
by DROOdotFOONIF (Native Implemented Functions) development and SIMD patterns for Elixir/BEAM. TRIGGER when: writing NIFs in C or Rust (Rustler), using erl_nif.h, Zig SIMD code for BEAM integration, tree-sitter grammar NIFs, or discussing native performance boundaries in Elixir. DO NOT TRIGGER when: general C/Zig/Rust language questions (use droo-stack), general Elixir patterns (use droo-stack), Raxol framework (use raxol skill), or ffmpeg upstream / libav* hand-written assembly (use ffmpeg-asm skill).
nix
by DROOdotFOONix language, flakes, NixOS, Home Manager, and agent-skills packaging. TRIGGER when: working with .nix files, flake.nix, flake.lock, Nargo.toml (Nix packaging context), NixOS configuration, Home Manager modules, nix-agent MCP tools, agent-skills-nix deployment, or rigup.nix riglets. DO NOT TRIGGER when: only using nix PATH (chezmoi handles that), or working on ZK circuits (use noir skill), or Nix language is incidental to another domain.
noir
by DROOdotFOOZero-knowledge circuit design with Noir (Aztec's ZK DSL). TRIGGER when: working with .nr files, Nargo.toml, ZK circuits/proofs, Aztec contracts, zoir extension, or discussing zero-knowledge proof design. Covers circuit architecture, constraint optimization, ZK-specific security, and Aztec integration. DO NOT TRIGGER when: only Noir language syntax is needed (droo-stack handles that), working with Solidity only (use solidity-auditor skill), or hybrid Solidity + Noir projects with both foundry.toml and Nargo.toml (use zk-x-ray skill).
zk-x-ray
by DROOdotFOOPre-audit report generator for ZK + EVM hybrid protocols (Noir circuits + Solidity verifier / oracle layers). Produces an x-ray report, a classified entry-points map, an invariant catalog with a Circuit↔Solidity Consistency section, a per-circuit map, and an EIP-readiness verdict. TRIGGER when: project has both `foundry.toml` (or hardhat config) AND a `Nargo.toml` workspace; user asks for "zk-x-ray", "audit zk", "audit zkp", "zk readiness", "pre-eip review", "circuit-solidity audit", or "zk pre-audit"; EIP/ERC draft is being prepared for submission and a hybrid Solidity + Noir codebase needs a structural readiness check. DO NOT TRIGGER when: protocol is Solidity-only (use solidity-auditor skill or pashov's x-ray); deep circuit-design questions without Solidity integration (use noir skill); general Ethereum tooling questions (use ethskills); when a full external audit is the goal rather than a pre-audit briefing.
blockscout
by DROOdotFOOBlockscout MCP tool reference for on-chain data queries. Covers all 16 tools: address info, transactions, token transfers, NFTs, contract ABI/source, read-only calls, ENS resolution, and block data across 8+ chains. TRIGGER when: user asks about on-chain data, contract state, token balances, transaction history, ENS lookup, NFT holdings, or uses blockscout MCP tools. DO NOT TRIGGER when: user asks about crypto market prices or trading volume (use coingecko skill), or writing Solidity code (use solidity-auditor skill).
digest
by DROOdotFOOGenerate a multi-platform activity digest for a topic. Fetches and ranks items from HN, GitHub, Reddit, YouTube, ethresear.ch, Snapshot, Polymarket, package registries, CoinGecko, Blockscout, and Shodan. TRIGGER when: user invokes "/digest" or asks for a "digest", "what's happening with X", "activity summary for X", "news about X". DO NOT TRIGGER when: user asks about digest agent code/implementation.
sentinel
by DROOdotFOOMonitor on-chain contracts for anomalous transactions. Checks for large transfers, ownership changes, unusual methods, and selfdestruct calls via Blockscout API v2. TRIGGER when: user asks about contract monitoring, "check this contract", on-chain alerts, "any suspicious transactions", or invokes "/sentinel". DO NOT TRIGGER when: user is working on sentinel agent code itself.
coingecko
by DROOdotFOOCoinGecko and GeckoTerminal API reference for crypto market data, token prices, DEX pools, and on-chain analytics. TRIGGER when: user asks about token/coin prices, market caps, trading volume, DEX pools, trending tokens, price history, crypto market data, or CoinGecko API usage. DO NOT TRIGGER when: user asks about on-chain contract state or transactions (use blockscout skill), or Solidity/smart contract code (use solidity-auditor skill).
Browse Agent Skills by Occupation
23 major groups · 867 SOC occupations
Browse by Category
Explore agent skills organized by their primary use case
Explore the agent skills ecosystem by occupation and creator
SkillMD is not just a keyword search box. It is an open map that organizes public skills by occupation, creator, and repository, helping you see which workflows, judgment criteria, and domain habits people are writing for AI agents.
Then follow creators and GitHub repositories back to the source: compare the skills a team maintains, whether the repo is active, and how the README frames the work before you open, install, or reuse anything.
Use it three ways: learn an unfamiliar field by occupation, study how creators organize skills, then use source context to decide what is worth opening or reusing.
01 Map a field
Browse 23 occupation groups and 867 SOC roles to learn what skills exist in adjacent domains and how they break down real work.
02 Follow creators
Use creator and repository pages to inspect maintained skill collections, recent updates, and source context before trusting a result.
03 Search with sources
Search 1.7M+ collected skills, then use occupation tags, creators, and GitHub source context to decide what is worth opening.
Start with the occupation map, then follow creators and repositories back to real code. SkillMD helps explain why a skill is worth opening, not only what it is named.
Standardizing Agent Capabilities with SKILL.md and Model Context Protocol (MCP)
In the rapidly evolving landscape of artificial intelligence, LLM agents (Large Language Model agents) have transitioned from simple text predictors to autonomous problem solvers. To orchestrate complex, multi-step agentic workflows, developers require a standardized format to specify agent capabilities, prompt instructions, system rules, and database bindings. This is where SKILL.md and the Model Context Protocol (MCP) have emerged as standard developer paradigms. SkillMD serves as the central directory for indexing, exploring, and sharing these critical agent configurations.
Our open-source registry currently tracks over 1.7 million collected SKILL.md configurations and system prompts. By compiling agent configurations from active developers on GitHub, we bridge the gap between prompt engineering research and production execution. Whether you are building agents with Anthropic's Claude Code, OpenAI's GPT-4, Google's Gemini, or local models using Ollama and LlamaIndex, standardized skill definitions ensure your agents behave predictably across different runtime environments.
What is the Model Context Protocol (MCP)?
The Model Context Protocol (MCP) is an open-source standard designed to connect LLMs to data sources, developer tools, and external environments. MCP establishes a bidirectional communication channel between client applications (like Cursor, Claude Desktop, or custom agent systems) and servers hosting data or capabilities. Standardizing instructions via SKILL.md enables LLMs to query databases, read local files, execute terminal commands, and integrate third-party APIs. SkillMD allows you to find ready-to-run MCP servers and prompt instructions for various occupations and technical tasks.
The Structure of a Professional SKILL.md File
A valid SKILL.md configuration is designed to be easily read by humans and parsed by LLMs. It contains precise system instructions, trigger conditions, required parameters, and execution examples. Below is the typical architectural blueprint of a professional agent skill:
- Metadata & Core Scope: Declares the name of the skill, author details, target models, and a description of the capability.
- Triggers & Intent Detection: Details semantic triggers that help the agent decide when to invoke this skill.
- System Prompts: Explicit system-level instructions that direct the agent's behavior, personality, safety guardrails, and formatting preferences.
- Capabilities & Tools: Lists the files, databases, or APIs the agent must access to complete the tasks.
- Few-Shot Examples: Demonstrates real inputs and outputs, helping the model generalize behavior through in-context learning.
Optimizing Agent Workflows for Modern LLMs
Writing effective agent skills requires deep knowledge of prompt engineering. With the release of advanced reasoning models like Claude 3.5 Sonnet, ChatGPT o1, and DeepSeek-V3, prompt templates must focus on structured thinking. Developers are encouraged to use XML tags (e.g., <thought>, <context>, and <rules>) to isolate execution boundaries. Standardized prompts prevent agents from suffering from context drift, ensuring that long-running tasks remain aligned with the initial system parameters.
Exploring by SOC Occupations and Creator Profiles
What makes SkillMD unique is its taxonomy. Instead of simple text search, we parse and organize files according to the Standard Occupational Classification (SOC) system. This means you can discover skills written for Computer and Mathematical roles, Business and Financial operations, Legal, Design, and and Educational Instruction fields. By tracking creator profiles, developers can study how different teams organize their custom instructions, compare version updates, and fork public configs for specialized enterprise use cases.
SkillMD operates as a high-performance index running on a fast Go backend and a highly responsive Astro SSR frontend. All search queries execute in milliseconds, featuring smart debouncing to prevent multiple API requests while keeping user data secure. Join our community of developers to standardize your AI agent instructions and optimize your LLM prompting workflows today.
Frequently Asked Questions
A practical guide to agent skills: what they are, how to inspect them, and how SkillMD helps you explore the ecosystem.