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...
justhireme-disabled
by vasu-devsDeprecated placeholder. Do not use as project guidance.
graph
by vasu-devsQuery forge's live, always-fresh code map — a dependency graph of the repo with god-nodes, modules, and neighbors/impact (blast-radius) queries. Use before editing to know what depends on a file, to find the most-depended-on hotspots, to map an unfamiliar codebase's architecture, or whenever you need current structural context instead of guessing.
review
by vasu-devsReview code changes for correctness and quality before merging. Use after implementing a meaningful chunk or feature, between plan tasks, or before opening a PR — to catch issues while they're cheap.
ship
by vasu-devsFinish a completed piece of work and integrate it cleanly — verify, then decide how to merge, PR, or set it aside. Use when implementation is done and tests pass, and you need to land the work and clean up.
tdd
by vasu-devsImplement features and fix bugs test-first with a strict red-green-refactor loop. Use whenever you are about to write implementation code or fix a bug — before the production code exists.
understand
by vasu-devsBuild an accurate mental map of unfamiliar code before changing it. Use when starting work in a codebase or area you don't know well, before editing code whose ripple effects you can't predict, or when the user asks how something works or where something lives.
verify
by vasu-devsProve that work is actually complete, fixed, or passing before saying so. Use right before claiming success, marking a task done, committing, or opening a PR — any moment you're about to assert that something works.
architect
by vasu-devsDesign the shape of a non-trivial module, API, or system before implementing it. Use when a design decision has more than one reasonable answer, when introducing a new abstraction or interface, or when getting the shape wrong would be expensive to undo.
brainstorm
by vasu-devsTurn a vague idea into an approved, written spec before any code is written. Use before building a feature, adding functionality, or changing behavior — whenever the work is more than a trivial mechanical edit and the requirements aren't already pinned down.
debug
by vasu-devsFind the root cause of a bug, test failure, or performance regression before proposing any fix. Use the moment something is broken, throwing, failing, flaky, or slower than expected — and resist the urge to patch first.
design-ui
by vasu-devsBuild or redesign frontend UI that doesn't look templated or AI-generated. Use when creating components, pages, landing pages, or restyling existing UI — before writing CSS/markup, and before shipping any visual work.
eval
by vasu-devsMeasure non-deterministic behavior — LLM features, agents, prompts, or a skill itself — with repeatable evals instead of one-shot checks. Use when building or tuning AI/LLM functionality (ranking, extraction, generation, agent loops), when a feature could pass once by luck, or when validating that a prompt or skill actually changes behavior.
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.