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...
update-screen-design
by alvisUpdate Notion design docs to latest template
commit
by alvis'Save any code change cleanly — jj-first, git-compatible. The skill auto-routes among save / split / absorb / edit / parallel-workspace based on working-copy state. Explicit operations via flags: --retrospective (blame-trace fixups), --reorder (re-linearise history), --create-pr (materialise stacked PRs). Triggers when: "commit my changes", "save this", "split this branch", "edit a prior commit", "absorb fixes", "create a stack", "restack", "reorder history".'
document
by alvisCreate or update a package's README.md and optionally an ARCHITECTURE.md from the actual code implementation. Use when writing project documentation, drafting a new README, refreshing stale docs after code changes, documenting monorepo packages, or producing an ARCHITECTURE.md reflecting current source structure. Follows existing README.template.md or sibling package READMEs when present; otherwise falls back to bundled templates in references/ and only enters plan mode when neither exists.
draft-code
by alvisDraft TypeScript-compliant code skeleton with TODO placeholders. Use when starting new implementations, creating code scaffolds, or preparing test structure for TDD.
fix
by alvis'Fix failing code, tests, lint, or type errors with auto area detection. Triggers when: "fix the bug", "fix this error", "fix failing tests", "fix the lint errors", "fix type errors". Also use when: resolving TypeScript complaints, addressing PR review feedback, repairing broken CI. Examples: "fix the bug in parseDate", "the tests are failing, fix them", "fix these eslint warnings".'
modernize
by alvisAnalyze project configuration and upgrade code to use the latest supported syntax, APIs, and patterns. Use when adopting new language features, upgrading runtime versions, or ensuring code uses modern idioms the project supports.
review-code
by alvis'MUST RUN after implementing any code. Spawns review subagents (test, security, code-quality, docs, style) to audit against the plan, siblings, redundancy, and correctness. Triggers when: "review this code", "review my PR", "audit this file", "check the code quality", "review for security". Also use when: finishing an implementation, validating test coverage, pre-merge checks. Examples: "review src/auth", "review the pull request", "audit this module for security issues".'
setup-project
by alvisEnsure project structure exists before development, creating barebone scaffolding only if needed. Use when initializing new projects, validating project setup, or ensuring monorepo component structure.
write-code
by alvisWrite production-ready code end-to-end via a full TDD lifecycle (design, skeleton, implement, test, refactor). Triggers when: "write a function", "implement this feature", "build a new module", "add a feature". Also use when: starting a new component from scratch, turning a spec or ticket into working code, creating a CLI or API endpoint with tests. Examples: "write a function that parses dates", "implement user authentication", "build a rate limiter module".
write-pr
by alvisAuthor conventional-commit PR titles and unified PR bodies from a `jj`/`git` change ref, emit to stdout for the caller to pipe into `gh pr create`. Triggers when: "write a PR description", "draft a pull request", "open a PR for this", "generate PR body". Also use when: `/coding:commit --create-pr` needs a PR body per stacked change, or any caller needs a conventional-title + unified-template body from a commit. Examples: "write a PR for this implementation", "draft a PR body for change @-", "open a draft PR for the current jj change".
deep-research
by alvisConduct comprehensive multi-source research with AI-powered analysis. Use when investigating complex topics, gathering information from multiple sources, or synthesizing research findings.
think
by alvisStructured pre-implementation thinking for ambiguous problems. Use when there is no crystal-clear instruction or solution on how to solve a problem — forces deliberate reasoning before any modification or creation begins.
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.