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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wm-atlas-plays
by monkeypantsGenerate one focused map per strategic play, showing only the components, dependencies, evolve arrows, and annotations relevant to that play's argument. Deepens play analysis from evolution assessments, supply chain, and decisions log. Produces atlas/play-{slug}/ for each play.
wm-atlas-evolution-mismatch
by monkeypantsIdentify components where the execution strategy (build/buy/outsource) does not match the evolution stage. Custom-building commodities? Outsourcing genesis work? Also identifies components with no execution strategy decorator. Produces a mismatch-annotated map with notes explaining each discrepancy and its cost.
wm-atlas-sourcing
by monkeypantsGenerate a sourcing-focused map showing all components decorated with build/buy/outsource strategies. Groups components by execution strategy and annotates vendor options and market alternatives from research. Produces atlas/sourcing/ with map and analysis.
wm-strategy
by monkeypantsAdd strategic annotations to a positioned Wardley Map. Identifies evolution opportunities, build/buy/outsource decisions, inertia barriers, pipeline opportunities, and competitive dynamics. Produces an annotated OWM map with evolve arrows, execution strategies, pipelines, and annotations. Use after evolution map is agreed (map.agreed.owm exists in evolve/).
wm-atlas-flows
by monkeypantsExtract flow links and their connected components from the comprehensive strategy map. Strips structural dependencies to show only dynamic relationships (data, signal, money flows). Identifies feedback loops as cycles in the flow graph. Adds minimal structural context.
wm-atlas-movement
by monkeypantsGenerate a movement-focused map showing only components with evolve arrows plus their immediate dependencies. Produces a change-programme view highlighting what moves, why, and what that movement requires. Produces atlas/movement/ with map and analysis.
wm-atlas-overview
by monkeypantsGenerate a simplified landscape map from the comprehensive strategy map. Collapses dense component clusters into submap elements to produce a ~10-12 component orientation artifact. The overview map is the first thing a new reader should see.
wm-atlas-pipelines
by monkeypantsIdentify components that should be represented as pipelines — multiple variants at different evolution stages. Finds explicit pipelines in the strategy map and discovers implicit pipeline candidates from research and evolution assessments. Analyses migration paths and dual-running costs.
wm-needs
by monkeypantsIdentify user needs for Wardley Mapping from prior research. Reads shared research and the project brief, proposes users and their needs, then facilitates negotiation with the client. Produces a markdown needs document that must be explicitly agreed before proceeding. Use after wm-research is complete and brief.agreed.md exists.
wm-research
by monkeypantsKick off a Wardley Mapping project. Reads shared organisation research, agrees project scope with the client, creates the project directory, and produces a coarse landscape sketch in OWM format. Use when starting a new Wardley Mapping project for a client that has already been researched.
wm-tour-investor
by monkeypantsCurate atlas content into a narrative presentation for investors evaluating structural defensibility and growth potential. Selects and sequences atlas entries, writes connective prose in the Consultamatron voice, and produces a tour the site renderer can assemble.
wm-tour-operations
by monkeypantsCurate atlas content into a narrative presentation for operations and delivery teams who need to execute. Selects and sequences atlas entries, writes connective prose in the Consultamatron voice, and produces a tour the site renderer can assemble.
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.