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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obsidian-vault
by pkarpovichCreate or extend notes in Pavel's personal Obsidian vault at `/Users/pavel.karpovich/Obsidian/PK Workspace/`, following his adapted kepano-style vault grammar (plural categories, YYYY-MM-DD dates, heavy internal wikilinking, root-first organization, 1-10 rating). Use this skill WHENEVER Pavel wants to capture something into his vault - even when he does not say "Obsidian" or "vault" explicitly. Trigger phrases include "save a note about <X>", "log this", "add to vault", "I watched <movie>", "I read <book>", "I finished <game>", "I bought <jacket/jeans/sneakers>", "I met <person>", "new idea", "evergreen about", "meeting note for", "project note on", "weekly note", "add <X> to references", and any bare mention of a movie/book/game/show/person/place/product that implies cataloguing. Knows the 19 active note types - Journal, Meeting, Project Note, Task, Idea, Evergreen, Quote, Job Interview, Rent Payment, Weekly, Movie, TV Show, Game, Book, Person, Product, Company, Place, Recipe, Podcast - and where each one li
rust-style
by pkarpovichRust coding style guide. Apply automatically when writing or modifying Rust code. Enforces for-loops over iterators, let-else for early returns, variable shadowing, newtypes, explicit matching, and minimal comments.
lego-cubes-prep
by pkarpovichEnd-to-end preparation of a new Lego Cubes podcast episode (Pavel's Russian-language podcast about content + dev projects). Walks through 7 stages with check-ins — collect Content bullets from Tuclaw/Podcast files, fill the episode note, suggest an episode title in the established poetic style, find IMDB/Steam/Goodreads links, produce a LEGO-style cover image prompt for Nano Banana Pro, after the recording publish the .mp4 + .jpg + .nfo bundle to the NAS for Plex/Kodi, and finally archive the per-content impression files into References/. Trigger on any mention of preparing, assembling, filling, publishing, or archiving a Lego Cubes episode — including phrases like "prepare episode 30", "Lego Cubes 30", "the next episode", "fill out the episode note", "I recorded the podcast", "put it on the NAS", "make the nfo", "faststart the recording", "move impressions to References", "archive the episode", or a bare episode number with podcast context. Pavel typically writes these requests in Russian — same triggers app
python
by pkarpovichModern Python 3.13+ development standards and best practices. Use when writing, reviewing, or refactoring Python code. Triggers on Python file creation/editing, code reviews, architecture decisions, async patterns, type hints, testing strategies, or when user asks about Python best practices.
langfuse-trace
by pkarpovichAnalyze Langfuse trace JSON exports using jq queries. Use when user provides a Langfuse trace file (.json) for analysis, debugging agent behavior, checking token usage, or investigating tool calls. Triggers on phrases like "analyze trace", "check this langfuse", "debug agent run", or when user shares a trace JSON file path.
ralphex-farm
by pkarpovichWork with ralphex-farm: an autonomous executor that polls Linear, picks up Todo issues whose description carries a `<!-- ralphex-farm -->` YAML block (repo + plan + branch, optional mode), runs ralphex against the named plan inside Docker, and opens a PR. Invoke whenever the user wants to: create a Linear ticket for a plan ("create a ralphex farm task", "add task for plan", "queue plan to the farm"), re-run or recover a failed task ("re-run", "recover", "review only", "move back to Todo"), add a new repository to the farm, trigger an immediate sync, debug why a ticket was not picked up, or answer questions about how the farm works. Also trigger on any mention of the `<!-- ralphex-farm -->` metadata block, repos.yaml in the farm context, `/var/ralphex/...` host paths, the farm's `/api/sync` or `/api/repos` endpoints, or Claude plugin provisioning in the farm.
defuddle
by pkarpovichConvert any web article, blog post, documentation page, or release-notes URL into clean markdown with a YAML metadata block (title, author, site, published date, source URL, word count) via the public defuddle.md HTTP API — just `curl https://defuddle.md/<url-without-scheme>`. Use this INSTEAD of WebFetch whenever the user shares a URL that points at readable prose, even if they did not explicitly say "extract", "clean up", or "summarize" — e.g. "open <url>", "what does this article say", "read this blog post", a bare URL pasted into chat, or "what does <author> say about X" with a link. WebFetch summarizes the page through an LLM and drops the metadata, so quotes drift and the publication date is lost; defuddle returns the article text verbatim, which matters when the user wants to discuss, cite, or archive what was written. SKIP this skill (use WebFetch or another tool instead) when the URL ends in `.md` / `.txt` (already plain), `.pdf` / `.png` / other binary, is a JS-rendered SPA or dashboard (defuddle ca
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.