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.
Querying local SQLite index...
codex-mcp-config-format-matrix
by davidruzickaUse when generating or reviewing MCP configuration snippets for Codex (CLI/TOML) to enforce Codex-native auth and format rules.
create-crud-mcp-profile
by davidruzickaCreate a new CRUD MCP profile from an OpenAPI spec in this repository. Use when a user asks for a profile equivalent to existing CRUD-style profiles (for example YouTrack, Grafana, GitLab, GitHub Security), including action mapping, parameter modeling, profile tests, docs updates, and validation.
focus-on-fix-validity-over-pr-process
by davidruzickaPrioritize fix validity (threat model, exploitability, coverage, residual risk) over PR process status when reviewing security changes.
gemini-cli-mcp-config-format-matrix
by davidruzickaUse when generating or reviewing Gemini CLI MCP configuration snippets in this repository (profile index/docs). Enforce Gemini-native settings.json mcpServers format and gemini mcp add/list/remove command syntax for stdio and streamable HTTP servers.
javascript-es2022
by davidruzickaUse when implementing or refactoring JavaScript files (`.js`, `.mjs`, `.cjs`) in this repository. Applies Node.js 20+ ES2022 style, testing, and interaction rules from former `.github/instructions/javascript-es2022.instructions.md`.
prepare-release-publishing
by davidruzickaPrepare patch/minor/major release publishing in this repository by inserting a dated version header under `## [Unreleased]` in `CHANGELOG.md`, bumping `package.json` with `npm version`, and verifying both files are consistent. Use when asked with prompts like "prepare patch publishing", "prepare minor release", or "prepare major publish".
prevent-generated-code-duplication
by davidruzickaDetect and refactor duplicate logic introduced by generated code, including overlaps between generated artifacts and existing project code.
review-plan-before-implementation
by davidruzickaRun an interactive pre-implementation plan review with explicit tradeoffs, opinionated recommendations, and user checkpoints before any code changes. Use when asked to review a plan, challenge architecture, assess quality/testing/performance risks, or decide implementation direction.
typescript-es2022
by davidruzickaUse when implementing or refactoring TypeScript files (`.ts`) in this repository. Applies TypeScript 5.x and ES2022 development rules from former `.github/instructions/typescript-5-es2022.instructions.md`.
use-correct-github-review-comment-enums
by davidruzickaEnforce schema-correct enum values when posting GitHub PR review comments via MCP tools. Use when creating inline review comments or handling tool validation errors for review-thread parameters.
verify-local-config-before-claims
by davidruzickaVerify repository configuration locally before making conclusions about MCP/server setup. Use when asked to diagnose configuration, profile routing, auth behavior, endpoints, env vars, or profile IDs/aliases in this workspace.
auto-update-skills
by davidruzickaAfter correction/feedback: propose new skill or update existing to capture reusable pattern. Covers knowledge, tools, policies, preferred style. Trigger immediately for critical issues, on repetition for trivial patterns.
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.