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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mckinsey-consultant
by ian-lawrence423McKinsey-level structured consulting methodology for strategy, analysis, and problem-solving. This is the analytical OS — load it for any strategy work, investment evaluation, structured diagnosis, framework design, or McKinsey-style document. It owns all analytical methodology: 7-step MBB problem solving, MECE issue trees, 7 strategy dimensions, Pyramid Principle, Six Screening Questions for investments, and all analytical modules (Porter's, SWOT, market sizing, positioning maps, value chain). It does NOT govern evidence gathering or source validation — that is market-research's job. For any task that requires original data collection (market sizing, competitive intelligence, customer research), invoke market-research on top of this skill. For financial modeling, use financial-model-builder. For PPTX/docx output, use pattern-investment-pptx or pattern-docx.
mckinsey-consultant
by ian-lawrence423McKinsey-level structured consulting methodology for strategy, analysis, and problem-solving. This is the analytical OS — load it for any strategy work, investment evaluation, structured diagnosis, framework design, or McKinsey-style document. It owns all analytical methodology: 7-step MBB problem solving, MECE issue trees, 7 strategy dimensions, Pyramid Principle, Six Screening Questions for investments, and all analytical modules (Porter's, SWOT, market sizing, positioning maps, value chain). It does NOT govern evidence gathering or source validation — that is market-research's job. For any task that requires original data collection (market sizing, competitive intelligence, customer research), invoke market-research on top of this skill. For financial modeling, use financial-model-builder. For PPTX/docx output, use pattern-investment-pptx or pattern-docx.
competitive-landscape-deliverable
by ian-lawrence423Triggers when converting a competitive landscape, market mapping, or M&A target spreadsheet into a board-ready executive deliverable. Preserves the raw research sheet and adds a second sheet that compresses each cell while preserving the full rating + key evidence — fidelity over brevity. Verdict-led layout, Pattern brand styling, and consulting-grade formatting. Mines the rating-and-rationale source format produced by the Pattern competitive landscape pipeline.
competitive-landscape-deliverable
by ian-lawrence423Triggers when converting a competitive landscape, market mapping, or M&A target spreadsheet into a board-ready executive deliverable. Preserves the raw research sheet and adds a second sheet that compresses each cell while preserving the full rating + key evidence — fidelity over brevity. Verdict-led layout, Pattern brand styling, and consulting-grade formatting. Mines the rating-and-rationale source format produced by the Pattern competitive landscape pipeline.
competitive-landscape-deliverable
by ian-lawrence423Triggers when converting a competitive landscape, market mapping, or M&A target spreadsheet into a board-ready executive deliverable. Preserves the raw research sheet and adds a second sheet that compresses each cell while preserving the full rating + key evidence — fidelity over brevity. Verdict-led layout, Pattern brand styling, and consulting-grade formatting. Mines the rating-and-rationale source format produced by the Pattern competitive landscape pipeline.
ntb-diligence
by ian-lawrence423Standalone Need-to-Believe (NTB) diligence skill. Produces a comprehensive NTB registry, a prioritised diligence plan keyed to each gap, and adversarial stress tests of every NTB assumption. Use this skill whenever Ian asks to "run NTB diligence", "run NTB analysis", "build the NTB registry", "what do I need to believe about [company]", "stress test the thesis", "map out the NTBs", "work out the need-to-believes", "figure out the diligence plan for [deal]", "surface the assumptions", "what's the NTB on this", "map the base case assumptions", or "early-stage diligence triage". Runs standalone for early-stage diligence when no IC memo exists yet; its output plugs directly into ic-memo when the full memo is later produced. Structured workflow with intake protocol and four sequential phases — not a one-pass tool. Default output is evidence-tagged markdown; .docx output on explicit request via pattern-docx. Complementary to ic-memo (runs before it), claim-scrutinizer (tests a completed thesis whereas this skill bu
writing-style
by ian-lawrence423Run final prose and claim-discipline review for formal outputs, including claim tags, attribution integrity, epistemic language, and clarity.
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.