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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nix
by wimpysworldLoad when working with Nix, NixOS, Home Manager, nix-darwin, nixpkgs, flakes, derivations, overlays, modules, options, or registries; with .nix files such as configuration.nix, home.nix, default.nix, shell.nix, or flake.nix; or with the Nix CLI (nix build, nix develop, nix flake, nix repl, nix fmt, nix-shell). Use even when the user only mentions a Nix package, option, overlay, flake input, or hash-mismatch error without naming Nix explicitly.
semgrep
by wimpysworldRun Semgrep static analysis scans and create custom detection rules. Use when asked to scan code with Semgrep, find security vulnerabilities, write custom YAML rules, or detect specific bug patterns. IMPORTANT: Also use this skill when users ask to 'scan for bugs', 'check code quality', 'find vulnerabilities', 'static analysis', 'lint for security', 'audit this code', or want to enforce coding standards — even if they don't mention Semgrep by name. Semgrep is the right tool for pattern-based code scanning across 30+ languages.
grill-me
by wimpysworldInterview the user relentlessly about a plan or design until reaching shared understanding, resolving each branch of the decision tree. Use when user wants to stress-test a plan, get grilled on their design, or mentions "grill me".
code-security
by wimpysworldSecurity guidelines for writing secure code. Use when writing code, reviewing code for vulnerabilities, or asking about secure coding practices like 'check for SQL injection' or 'review security'. IMPORTANT: Always consult this skill when writing or reviewing any code that handles user input, authentication, file operations, database queries, network requests, cryptography, or infrastructure configuration (Terraform, Kubernetes, Docker, GitHub Actions) — even if the user doesn't explicitly mention security. Also use when users ask to 'review my code', 'check this for bugs', or 'is this safe'.
gh
by wimpysworldUse when the user mentions `gh`, `gh api`, the GitHub CLI, the GitHub API, or wants to view, query, search, or change GitHub. Covers querying the GitHub API, raw API calls, PRs/pull requests, issues, workflows/Actions/CI, releases, repos, notifications, status, and any task that views or queries GitHub data.
llm-security
by wimpysworldSecurity guidelines for LLM applications based on OWASP Top 10 for LLM 2025. Use when building LLM apps, reviewing AI security, implementing RAG systems, or asking about LLM vulnerabilities like 'prompt injection' or 'check LLM security'. IMPORTANT: Always consult this skill when building chatbots, AI agents, RAG pipelines, tool-using LLMs, agentic systems, or any application that calls an LLM API (OpenAI, Anthropic, Gemini, etc.) — even if the user doesn't explicitly mention security. Also use when users import 'openai', 'anthropic', 'langchain', 'llamaindex', or similar LLM libraries.
love
by wimpysworldLoad when working with LÖVE 2D, the LÖVE engine, love2d, .love archives, Lua 5.1/LuaJIT 2.1 game development, or LÖVE callbacks, modules, conf.lua, and packaging.
prose-style-reference
by wimpysworldExtended writing reference for documentation and content creation. Load for blog posts, READMEs, technical guides, and long-form writing.
write-agents-md
by wimpysworldUse when creating, updating, consolidating, or reviewing an AGENTS.md (or CLAUDE.md, CLAUDE.local.md, .claude/rules/*.md, .cursorrules, .cursor/rules/*.mdc, AGENTS.override.md, or .github/instructions/*.instructions.md) project instruction file. Covers the open agents.md spec, Codex precedence rules, Claude Code memory loading, and migration from legacy formats. Use even if the user only says "instructions", "rules", "project memory", or names a single legacy filename.
write-assistant
by wimpysworldUse when creating, updating, refactoring, or reviewing an AI assistant or sub-agent system prompt - persona, role, capabilities, tools, output format, examples, and constraints. Covers Claude Code agents, OpenAI Codex / Responses-API agents, Pi assistants, and OpenCode agents. Use even if the user only says "agent prompt", "assistant", "subagent", "persona", or names the artefact by file path.
write-command
by wimpysworldUse when creating, updating, or reviewing a slash command - shims that delegate to a skill or agent, standalone commands with an inline output format, and the `description` / `argument-hint` / `model` headers per provider. Covers Claude Code custom commands and the merged skill-as-command format, OpenCode commands, Pi prompt templates, and the legacy Codex `/prompts:` route. Use even if the user only says "slash command", "prompt template", "command shim", "create-command", or names the artefact by path.
write-skill
by wimpysworldUse when creating, updating, or reviewing an Agent Skill - authoring or revising a `SKILL.md`, its frontmatter, layout, references, and progressive disclosure. Use when the user mentions writing, editing, splitting, renaming, or auditing a skill, even if they do not say "skill" explicitly. Covers cross-platform portability across Claude Code, Codex, OpenCode, and Pi.
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.