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...
development
by channingwaltonImplement features using strict test-driven development. Use for the DEVELOP phase of XP workflow.
planning
by channingwaltonBreak down features into tasks through collaborative discussion. Use for the PLAN phase of XP workflow.
refactor
by channingwaltonImprove code design without changing behaviour. Use for the REFACTOR phase of XP workflow. All tests must pass before and after.
xp
by channingwaltonExtreme Programming workflow orchestrator. Use when implementing features. Coordinates planning, TDD, refactoring, and commits.
code-query
by channingwaltonSearch the codebase and Obsidian vault for relevant implementation and project context. Use when the user asks how something works, where it is implemented, or wants related notes alongside code results.
push
by channingwaltonCommit, push, open a PR
unison-update
by channingwaltonRepair Unison definitions after `update-definitions` reports affected definitions that no longer typecheck. Use when an update response includes `sourceCodeUpdates` for broken dependents.
glossary
by channingwaltonAdd domain terms to project glossary in the Obsidian vault. Use when defining new terms, clarifying jargon, or when unfamiliar terminology appears in requirements.
investigation
by channingwaltonCreate and maintain local evidence dossiers for messy investigations, data corrections, incident follow-ups, or reconciliation work where evidence spans local artefacts, a vault task note, tickets, Slack threads, code branches, and repeated runs. Use when the user asks to organise an investigation folder, keep run evidence up to date, trace where numbers came from, build a manifest or event log, or keep evidence out of a large task note.
obsidian-topic-maintainer
by channingwaltonKeep an Obsidian vault project's Topics folder up to date from its Tasks (and Events). Use when the user wants to refresh or maintain a project's topics, find missing or new topics, add task-to-topic backlinks, audit topic interlinking, fix orphan topics, resolve duplicate/alias collisions, or convert a dated retrospective into a Task. Assumes each project under the vault has Topics/ and Tasks/ sub-folders (and usually Events/).
scala-developer
by channingwaltonSenior Scala developer using functional programming and Typelevel ecosystem. Use when writing Scala code, implementing Scala features, or working with sbt/bloop projects. Used as a part of the XP skill.
skill-review
by channingwaltonAudit Channing's authored skills for whether they earn their place — usefulness, overlap with other skills or Claude's built-in abilities, redundancy, gaps, and triggering quality. Use when asked to review, audit, critique, or prune skills; when wondering whether a skill is still worth keeping; or after writing or editing a skill. Reviews authored skills only: real dirs in ~/.claude/skills and ~/.claude/commands (symlinks skipped) plus the dev repo in ~/dev/skills/skills.
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.