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...
aio-browser-cookie
by aioceanExtract cookies from local browsers and export them for use in authenticated requests. Use when extracting Chrome cookies, Firefox cookies, or Safari cookies from the local browser store; reusing a browser session to replay an authenticated request; exporting a Netscape cookie jar for curl, wget, or yt-dlp cookies; making an authenticated curl request with your live browser session; or using rookiepy to read browser cookies programmatically. Also covers session replay — sending HTTP requests with the browser's own cookies injected automatically.
aio-mental-models
by aioceanDecision and reasoning advisor — picks 2-3 relevant mental models, walks through applying each, and synthesizes a recommendation. Use proactively when facing a hard trade-off, an ambiguous decision, or any "should we do X or Y" question where structured reasoning beats gut feeling.
aio-bun-fullstack-setup
by aioceanBootstrap or scaffold a Bun fullstack project in one shot — single-port server, Vite dev proxy, monorepo layout, and Docker config. Use when starting a new fullstack project with Bun, setting up a bun server with vite proxy, creating a monorepo bun layout, configuring docker bun deployment, or bootstrapping a bun project from scratch. Skips files that already exist so it never overwrites your work.
aio-catch-me-up
by aioceanTurn Claude into a wise, effective teacher whose only goal is to make sure YOU deeply understand the work an AI agent just did — the problem it solved, why that problem existed, the solution and its design decisions, the edge cases it handled, and the broader impact. Incremental and mastery-gated: it has you restate your understanding first, fills the gaps at the depth you ask for (eli5 / eli14 / like-an-intern), shows you the real code, and quizzes you with AskUserQuestion — and it does not conclude until you have demonstrably understood. Use when the AI moved faster than you could follow and you want to catch up before merging. Trigger on "teach me", "help me understand what you did", "make sure I understand this change", "walk me through this", "explain the session", "I don't get it", "why did you do it this way", "quiz me", "eli5 / eli14", "don't let me fall behind", "I want to actually understand this, not just merge it", "catch me up", "bring me up to speed", "I fell behind", "the AI moved too fast".
aio-dream
by aioceanMemory consolidation — review, merge, prune and re-index memory files so future sessions orient quickly.
aio-feedback
by aioceanSubmit bug reports, feature requests, and plugin requests to aiocean/claude-plugins via GitHub Issues.
aio-patch-control
by aioceanScaffold a reference HTTP control-channel gateway (sample shell clients + bun TUI) into <project>/control/, OR print the control-channel protocol docs. Reference example based on the dirty-claude project (Phase 1 patched binary exposes HTTP+SSE server on 127.0.0.1:$DC_PORT inside the running Claude). NOT a generic abstraction — adapt to your own transport (WebSocket / ZMQ / named pipes) if needed.
aio-patch-extract
by aioceanExtract Claude Code's cli.js + native .node modules from an installed claude binary (or a binary you pass as positional arg). Output lands in dist/<arch>/. Required before aio-patch-compile. Default arch detected from host; ARCH env var overrides for cross-arch extraction.
aio-patch-run
by aioceanExec the compiled patched Claude binary at dist/<arch>/claude. Pass-through args + env. Requires aio-patch-compile to have been run first (sentinel: dist/<host-arch>/claude exists).
aio-code-review
by aioceanCode-review playbook for both sides — reviewer (what to look for, when to approve, severity-labeled comments, handling pushback) and PR/CL author (writing CL descriptions, splitting changelists, responding to comments). Use when reviewing or authoring a PR/MR/CL/diff, deciding LGTM or LGTM threshold, authoring a merge request, assessing code health, handling a hotfix review, or replying to review feedback.
aio-discover
by aioceanFind code, locate implementations, and understand how features work via parallel Explore agents. Step 1 of the codeflow discover → map → plan trio used before implementation.
aio-doc-writer
by aioceanGenerate comprehensive architecture documentation — the Codebase Oracle that writes docs, module docs, and dependency maps — powered by the GitNexus knowledge graph and LSP analysis.
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.