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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story-splitting
by citypaulTurn broad requirements, large stories, epics, features, initiatives, or backlog items into small end-to-end child stories without turning them into technical component tasks. Use when refining a backlog, decomposing epics, planning an MVP or walking skeleton, looking for vertical slices, reducing story size, applying SPIDR/Hamburger/capability slicing, avoiding scatter-gather/component stories, or deciding the first valuable story before implementation planning.
teach-me
by citypaulStructured learning and tutoring for any topic. Use when the user wants to learn a concept, be quizzed, create a learning plan, generate a structured course, or produce reviewable HTML lessons. Invoked via /teach-me [topic].
api-design
by citypaulStable API and interface design patterns. Use when designing REST endpoints, module boundaries, component prop interfaces, or any public contract between systems. Covers contract-first development, error semantics (RFC 9457), REST conventions, pagination, idempotency, rate limiting, and backward compatibility. For TypeScript type patterns (branded types, discriminated unions, schemas), see typescript-strict. For validation at trust boundaries, see typescript-strict.
characterisation-tests
by citypaulUse when modifying existing code that lacks tests and you need to document its actual current behavior before making changes -- the legacy code dilemma where you need tests to refactor safely but the code was not written for testability. Specifically for understanding and pinning down what code currently does, not what it should do. Do NOT use for test-driving new behavior (see tdd), general test writing patterns (see testing), verifying test effectiveness (see mutation-testing), or making untestable code testable (see finding-seams).
ci-debugging
by citypaulSystematic CI/CD failure diagnosis using hypothesis-first investigation, local reproduction, and environment delta analysis. Use when a CI pipeline, GitHub Actions workflow, or build job fails; when tests pass locally but fail in CI; when diagnosing flaky tests, timeouts, or red pipelines; or when the user says "CI is failing", "the build is broken", or "works on my machine".
cli-design
by citypaulUnix-composable CLI design patterns. Use when building CLI tools, designing command trees, implementing output layers, or testing CLI behavior. Covers stream separation (stdout/stderr), format flags (--json/--plain), exit codes, TTY detection, composability, and error design. Language-agnostic principles; TypeScript implementation patterns in resources/. For API design (REST, HTTP), see api-design.
diagrams
by citypaulCreate diagrams and visualizations in Markdown using Mermaid, Graphviz, Vega-Lite, PlantUML, infographics, JSON Canvas, architecture diagrams, and info cards. Use when asked to create any diagram, chart, visualization, or visual documentation.
domain-driven-design
by citypaulDomain-Driven Design patterns for TypeScript. Use when implementing ubiquitous language, value objects, entities, aggregates, domain events, domain services, or bounded contexts. Only applies to projects that explicitly use DDD. Do NOT use for simple CRUD or projects without domain modeling.
expectations
by citypaulCapture learnings, gotchas, and architectural decisions into the right project documentation while context is fresh. Use when capturing learnings, documenting gotchas, recording architectural decisions, or deciding where a piece of knowledge should live. Triggers on "document this", "remember this pattern", "what should I know about", or after completing significant features.
find-gaps
by citypaulAdversarially review an existing written artifact — stories, plans, acceptance criteria, specs, or design mocks — to surface missing states, unhandled edge cases, unstated assumptions, unverifiable criteria, and slices still too broad or horizontal. Works interactively, one question at a time, writing each answer back into the artifact as a new acceptance criterion, plan update, or mock-state spec. Use when an artifact needs tightening before planning or coding ("what's missing?", "poke holes in this", "tighten this up"). Requires an artifact to inspect — for resolving a fuzzy decision tree with no artifact yet, see grill-me; for splitting oversized work, see story-splitting.
find-skills
by citypaulHelps users discover and install agent skills when they ask questions like "how do I do X", "find a skill for X", "is there a skill that can...", or express interest in extending capabilities. This skill should be used when the user is looking for functionality that might exist as an installable skill.
finding-seams
by citypaulUse when existing code has untestable dependencies that prevent writing tests -- direct construction of collaborators, static or global function calls, tight coupling to external systems, or singleton access patterns. Specifically for identifying substitution points (seams) that make legacy or tightly-coupled code testable without editing at the call site. Do NOT use for greenfield TDD (see tdd), general test writing patterns (see testing), or refactoring already-tested code (see refactoring).
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.