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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agents-md-creator
by 0sernaGenerate or update the AGENTS.md of a JavaScript or TypeScript repository following the https://agents.md/ standard. Use when asked to create, generate, update, or refresh AGENTS.md.
commit
by 0sernaGenerate and run a concise conventional commit for staged changes when the user explicitly asks to commit, create a git commit, or finish a staged change as a commit. Do not use for requests that only ask to draft, suggest, or review a commit message.
explore
by 0sernaEnter exploration mode as a thinking partner for ideas, problems, requirements, architecture, tradeoffs, or codebase investigation. Use when the user wants to explore, brainstorm, reason through uncertainty, or clarify a subject without implementation.
fallow
by 0sernaCodebase intelligence for JavaScript and TypeScript. Free static layer reports quality, changed-code risk, cleanup opportunities (unused files, exports, types, dependencies), code duplication, circular dependencies, complexity hotspots, architecture boundary violations, feature flag patterns, and opt-in security candidates. Runtime coverage merges production execution data into the same health report for hot-path review, cold-path deletion confidence, and stale-flag evidence, with a single local capture available by default and continuous/cloud runtime monitoring available as an optional mode. 118 framework plugins, zero configuration, sub-second static analysis. Use when asked to analyze code health, audit PR risk, find cleanup opportunities or unused code, detect duplicates, check circular dependencies, audit complexity, check architecture boundaries, detect feature flags, surface security candidates, clean up the codebase, auto-fix issues, merge runtime coverage, or run fallow.
grill-me
by 0sernaInterview the user about a plan, design, idea, architecture choice, or decision space until important branches are resolved and shared understanding is explicit. Use when the user asks to be grilled, challenged, interviewed, or walked through decisions without implementation.
openspec-archive-change
by 0sernaArchive a completed change in the experimental workflow. Use when the user wants to finalize and archive a change after implementation is complete.
openspec-propose
by 0sernaPropose a new change with all artifacts generated in one step. Use when the user wants to quickly describe what they want to build and get a complete proposal with design, specs, and tasks ready for implementation.
skill-creator
by 0sernaCreate, review, validate, and improve Agent Skills (SKILL.md) following the agentskills.io specification, best practices, and description optimization guide. Use when the user wants to create a new skill, audit or improve an existing one, ensure spec compliance, optimize triggering, or check reference integrity. Covers the full lifecycle: scoping, drafting, structural validation, semantic quality, description optimization, progressive disclosure, and maintenance.
update-agent-tools
by 0sernaUpdate curated global/local agent tooling used by these dotfiles. Use when asked to refresh agent CLIs or binaries.
update-openspec-config
by 0sernaUse only to update shared generated OpenSpec skills. Do not use for normal OpenSpec proposal, exploration, application, or archive workflows.
agent-quality-gate
by 0sernaSet up or update the quality gate in package.json. Generates scripts/check.sh with agent-parseable output, configures format, auto-fix via lint-staged, pre-commit hooks, and test — while preserving existing tools and asking before modification.
openspec-apply-change
by 0sernaImplement tasks from an OpenSpec change. Use when the user wants to start implementing, continue implementation, or work through tasks.
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.