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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hypothesis-tree
by sruthir28Build a Day-1 hypothesis tree — your best-guess answer to the governing question, broken into 2–3 supporting sub-hypotheses, with the test that would prove or kill each one. Different from an issue tree (which decomposes the problem space). This commits to an answer before you do the work, so the work targets what would actually change your mind. Use at the start of any analysis where you'd otherwise "boil the ocean."
mckinsey-charts
by sruthir28Generate McKinsey-style consulting charts as native python-pptx objects (editable inside PowerPoint). Three workhorse types — bar+callout for TAM/single-number stories, stacked column over time for revenue/usage mix, and waterfall for drivers/bridge analysis. Use when you need a chart that looks like it came from an EM-reviewed deck, not from Excel defaults.
mckinsey-critic
by sruthir28Reviews decks, documents, and strategies like a McKinsey engagement manager. Grades each section, flags structural problems, and gives the top 3 fixes. Use after building a deck, writing a strategy doc, or outlining a recommendation — before it goes to stakeholders.
meeting-prep-kit
by sruthir28Personal meeting prep tool. Given a meeting (attendees, topic, desired outcome), generates a tight prep packet — 3-bullet pre-read, time-boxed agenda, your top 3 talking points, and the top 3 objections you'll face with rebuttals. Use before any meeting where you're driving an outcome — exec reviews, stakeholder pitches, scope negotiations, manager 1:1s with an ask, vendor calls.
issue-tree-builder
by sruthir28McKinsey-style issue tree framework for breaking down complex problems into MECE (Mutually Exclusive, Collectively Exhaustive) components. Use when users need to decompose strategic questions, structure analysis, create work plans, or prepare for case interviews. Apply hypothesis-driven approach to problem-solving.
storyline-builder
by sruthir28McKinsey-style storyline framework for building presentation decks. Use when users need to structure presentations, pitch decks, or strategic communications. Creates logical flow where each storyline becomes a slide title, progressing from problem to solution.
ai-use-case-scorer
by sruthir28Score and prioritize AI use cases for your own job. Given a list of candidate use cases (or a description of your workflow), evaluates each on Value × Feasibility × Safety and tiers them — Do Now / Do This Quarter / Park / Avoid. Use when you've heard "we should use AI more" and need to figure out what to actually build first. Built for the individual IC, not org-wide rollout.
decision-memo-builder
by sruthir28Builds a 1-page decision memo (context → options → recommendation → risks → ask) enforcing McKinsey memo DNA — brutal brevity, SCP storyline clarity, decision-forcing output, evidence density. Use when drafting a memo for a leadership decision, a scope cut, a buy/build/partner call, a roadmap slip, or any "should we X?" moment that needs a yes/no from a specific person by a specific date.
scpr-framework
by sruthir28SCPR (Situation-Complication-Problem-Recommendation) framework for structured problem solving and executive communication. Use when users need to structure strategic arguments, analyze business situations, create executive summaries, or develop clear problem statements using McKinsey-style communication. Apply when structuring recommendations, writing memos, or organizing strategic thinking.
stakeholder-map
by sruthir28Map the people who can make or break a decision onto a Power/Interest grid, with each named stakeholder's stance (champion / skeptic / blocker / unknown), what they care about, and the single influence move you'll make for them. Use before any cross-functional decision, big launch, reorg, or executive ask — anywhere the politics will decide the outcome more than the merits.
synthesis
by sruthir28Force the "so what" out of a messy pile of raw inputs — interview notes, customer transcripts, survey results, research dumps, meeting recordings — into 3 insights with evidence and implication. Use when you have more data than you have meaning, and someone is about to ask "ok, so what does this mean?" Different from summarization; output must change how the reader acts.
top-down-memo
by sruthir28Answer-first writing using the Minto Pyramid Principle. Lead with the conclusion, then 2–4 MECE supporting arguments, then evidence under each. Use whenever you have a recommendation, finding, or assessment to communicate in writing — memos, emails to execs, Slack updates that need to land, briefing docs. Different from the Decision Memo Builder (which is a specific 1-page decision artifact); this is the general writing method.
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.