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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translator
by 0gsdOffline translation across ~419 languages via MADLAD-400-3B-MT (CTranslate2 + SentencePiece). No cloud. No account. The model downloads once (~3 GB) and runs locally on CPU or Apple Silicon thereafter. Use whenever the user wants to translate text TO or FROM a non-English language — short phrases, paragraphs, whole documents, idioms, low-resource languages, indigenous languages, dead languages MADLAD has seen, polite forms, casual registers. Also trigger on "translate", "say this in <lang>", "what does <foreign text> mean", "render this in <language>", "<lang> for <phrase>", "how do you say X in Y", "localize", or any request that involves moving text between human languages. DO NOT use for code translation between programming languages. DO NOT use for transliteration (script conversion) without a target meaning — MADLAD translates meaning, not glyphs.
ibis-girraphiti
by 0gsdIBIS discipline for girraphs — structured mapping of contested, fuzzy, or wicked problems as issues (❓), positions (💡), and arguments (➕/➖) in a .girraph file, BEFORE any solving happens. Engage when the user wants to map a problem, untangle a decision with no clean answer, see the shape of a disagreement, or says 'girraph this', 'map this out', 'help me think through (not solve) X', 'what are the positions on Y', 'IBIS', 'argument map'. Also engage when a planning conversation keeps circling — competing considerations that won't linearize are a map smell. Do NOT engage for problems with a known procedure (use a request file), linear plans (use text-planning), or simple either/or questions the user just wants answered.
memoir-dialectic
by 0gsdMulti-session memoir planning and (optionally) drafting via patient, iterative dialogue. Use whenever the user wants to plan, build, or write a memoir — full life, partial life, single milestone, professional thread, creative thread, or thematic slice. The agent interviews the user across many sessions, accumulates plan documents in a project folder, and optionally synthesizes a draft. Trigger on memoir-dialectic, "help me plan my memoir," "write my memoir with me," "memoir interview," "life story project," "autobiography help," or any long-horizon autobiographical writing collaboration.
analyzer
by 0gsdThree-mode analytical toolkit for documents, books, web pages, and decisions. SUMMARIZE produces an even-handed one-page digest of any text (author motivation, intended audience, tone, key quotes). PROOFREAD makes light spelling/typo corrections on full-length documents (including entire books, chapter by chapter), driven by the Harper grammar checker (local, offline, rule-named findings), and produces a separate proof report with suggestions and repeated-phrase findings. DECIDE picks three archetypal personas from a built-in roster and runs a transcripted debate over a question, complex decision, or 'what should I do next' — returning a recommendation plus the full debate. Use for 'analyze this', 'summarize this', 'one-pager', 'tl;dr but smart', 'read this for me', 'proofread this', 'copy-edit this book', 'fix typos in', 'spot repetition', 'help me decide', 'pros and cons', 'what should I do about', 'three perspectives on', 'debate this'. Works with .txt, .md, .docx, .pdf, .epub, .html, pasted text, web page
skill-scanner
by 0gsdPre-installation explainer and security/epistemic auditor for skill packages (.skill zips or skill directories). For any skill the user is considering installing, produces a single report that LEADS with a plain-English 2–3 paragraph description of what the skill does and why someone might want it, then follows with the safety verdict — prompt-injection vectors, privilege escalation patterns, epistemic pathologies, and executable payload risk. Use whenever a user wants to understand, vet, audit, inspect, or safety-check a skill before installing it, when downloading skills from untrusted sources, when onboarding a skill into an existing ecosystem, or on any request like "what does this skill do," "explain this skill," "scan this skill," "is this skill safe," "audit this zip," "vet this skill," "check this skill for injection," "skill-scanner," "skill security check," "is this skill malicious," or "should I install this." Also trigger when a user mentions downloading skills from the internet, skill marketplace
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.