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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context7
by larsboesSearch documentation using Context7's vector embeddings. Provides semantic search over code documentation (libraries, frameworks, APIs) via the c7 CLI.
art
by larsboesGenerate illustrations, technical diagrams, mermaid flowcharts, infographics, header images, thumbnails, comics, and PAI pack icons using multiple rendering backends. USE WHEN art, header images, visualizations, mermaid, flowchart, technical diagram, infographic, PAI icon, pack icon, YouTube thumbnails, ad hoc thumbnails, annotated screenshots, aphorisms, comics, comparisons, D3 dashboards, embossed logo wallpaper, essay illustration, frameworks, maps, recipe cards, remove background, stats, taxonomies, timelines, brand wallpaper, visualize, generate image, Midjourney, compose thumbnail, generate prompt.
fabric
by larsboes240+ specialized prompt patterns for content analysis, extraction, and transformation — run patterns, sync from repo, create threat models, summarize. USE WHEN fabric, fabric pattern, run fabric, update patterns, sync fabric, summarize with fabric, create threat model, analyze with fabric.
tripplanning
by larsboesTrip and event planning — research destinations, hotels, transport, create itineraries, manage bookings, conference prep, and travel notes in Obsidian. USE WHEN plan trip, travel planning, book hotel, find flights, conference prep, plan event, create itinerary, travel research, packing list, trip budget, plan vacation, plan conference attendance, travel schedule.
council
by larsboesMulti-agent collaborative debate producing round-by-round transcripts with genuine friction — custom agents composed per topic for real disagreement. Two workflows: DEBATE (3 rounds + synthesis) and QUICK (1 round). USE WHEN council, debate, multiple perspectives, weigh options, deliberate, get different views, multi-agent discussion, what would experts say, is there consensus, pros and cons from multiple angles. NOT FOR parallel task execution (use Delegation). NOT FOR pure adversarial attack (use RedTeam).
bazel
by larsboesBazel build system — MODULE.bazel (bzlmod), custom rules, test targets, Starlark scripting, CI integration, and remote caching. USE WHEN bazel, bazel build, MODULE.bazel, bzlmod, bazel rule, starlark, BUILD file, bazel test, bazel CI, bazel remote cache, bazel query, bazel run, bazel error, custom rule, bazel workspace, bazel dependency.
brightdata
by larsboes4-tier progressive URL scraping and multi-page crawling — WebFetch, then Chrome-header curl, then Playwright browser, then Bright Data proxy. Auto-escalates when lower tiers fail. USE WHEN Bright Data, scrape URL, web scraping, site blocking me, can't access, bot detection, crawl site, crawl pages, spider, CAPTCHA, four tier scrape, progressive scraping, Chrome headers.
focus-alignment
by larsboesMeta skill for life focus areas. Check alignment, surface neglected areas, connect work to priorities.
tmux
by larsboesRemote control tmux sessions — send keystrokes to interactive CLIs (python REPL, gdb, node, psql), scrape pane output, session presets, save/restore sessions, and JSON capture. USE WHEN tmux, tmux session, tmux pane, send keys to terminal, control tmux, interactive REPL, python session, gdb session, node repl, postgres psql, scrape terminal output, tmux preset, tmux capture.
worldthreatmodelharness
by larsboesStress-test ideas, strategies, and investments across 11 time horizons (6mo-50yr). Update and view world models. USE WHEN threat model, world model, test idea, test strategy, future analysis, test investment, test against future, stress test idea, time horizon analysis, update models, view models, refresh models, model status.
migrate
by larsboesIntakes existing content from external sources, classifies each chunk against the PAI destination taxonomy, and commits approved chunks with provenance. Sources: .md/.markdown/.txt, stdin, PAI TELOS/MEMORY/KNOWLEDGE dirs, CLAUDE.md/.cursorrules/OpenAI Custom Instructions, Obsidian/Notion/Apple Notes exports, journal dumps. MigrateScan.ts chunks and classifies, producing a routing table with per-target counts and confidence %. MigrateApprove.ts approval loop: --approve-all, --approve-target, --review, --dry-run. UNCLEAR never bulk-approved. Phase 6 delivers summary and /interview recommendation for sparse areas. Confidence: ≥70% auto-approve; 40-70% confirm; <40% walk-through. Destinations: TELOS (MISSION/GOALS/PROBLEMS/STRATEGIES/CHALLENGES/BELIEFS/WISDOM/MODELS/FRAMES/NARRATIVES/SPARKS), IDEAL_STATE (per-dimension explicit call), preferences (BOOKS/AUTHORS/MOVIES/BANDS/RESTAURANTS/FOOD_PREFERENCES/LEARNING/MEETUPS/CIVIC), Identity (PRINCIPAL_IDENTITY.md — always prompts), Knowledge (MEMORY/KNOWLEDGE/{Ideas,P
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.