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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agi-framework-chollet
by jonaProvides François Chollet's framework for understanding intelligence, AGI development paths, and the limitations of current AI approaches. Use this skill when users ask about- (1) What intelligence really means and how to define AGI, (2) Why scaling pre-training alone won't achieve AGI, (3) The difference between memorized skills and fluid intelligence, (4) Test-time adaptation and its role in AGI, (5) The ARC benchmark and what it measures, (6) Type 1 vs Type 2 abstraction in AI systems, (7) Program synthesis approaches to intelligence, (8) Evaluating claims about AGI progress, or (9) Understanding the conceptual foundations needed for building generally intelligent systems.
claude-code-best-practices
by jonaBest practices for using Claude Code effectively based on insights from its creator Boris Cherny. Trigger this skill when users ask about optimizing Claude Code usage, configuring CLAUDE.md files, using plan mode, working with sub-agents, understanding Claude Code philosophy, improving coding productivity with Claude Code, or building AI coding tools. Also trigger when users mention blatant demand, scaffolding in AI products, building for future model capabilities, or ask about Anthropic's approach to AI coding assistants.
yc-startup-fundamentals
by jonaY Combinator startup methodology covering team formation, MVP development, growth strategies, fundraising, PR, operations, and hiring. Trigger when users ask about starting a startup, forming a founding team, building an MVP, achieving product-market fit, raising venture capital, startup fundraising strategy, doing PR for startups, startup hiring decisions, startup operations, or when they need guidance on early-stage company building. Also trigger when users mention YC, Y Combinator, startup acceleration, or reference startup fundamentals like runway, burn rate, or co-founder dynamics.
b2b-ai-startup-levie
by jonaStrategic framework for evaluating and building B2B AI startups based on Aaron Levie's insights from building Box through the cloud transformation. Use when founders or advisors need to - (1) Evaluate AI startup ideas for defensibility and market timing, (2) Design pricing models for AI products (consumption vs seat-based), (3) Analyze competitive positioning against incumbents, (4) Identify high-value AI opportunities in enterprise unstructured data, (5) Assess whether to target "core" vs "context" business functions, (6) Understand the 2024-2027 AI startup window dynamics, or (7) Apply Innovator's Dilemma and Crossing the Chasm frameworks to AI market entry.
ai-accelerated-building-ng
by jonaApply Andrew Ng's startup building principles and AI-accelerated development strategies from AI Fund's experience launching ~1 startup per month. Use when users ask about startup execution speed, AI coding tools for faster prototyping, agentic AI workflows, evaluating AI startup opportunities, or building AI applications. Triggers include questions about how to build startups faster, AI technology stack layers, where AI opportunities exist, implementing agentic workflows, or applying lessons from successful AI venture studios.
enterprise-ai-strategy-nadella
by jonaStrategic AI thinking frameworks and mental models from Satya Nadella's perspective on platform shifts, AI deployment, and building successful AI products. Use when evaluating AI strategy decisions, assessing platform opportunities, thinking through AI product positioning, considering enterprise AI deployment challenges, evaluating talent and team capabilities, or needing frameworks for justifying AI investments in terms of economic surplus. Triggers on questions about AI platform strategy, change management for AI adoption, building AI scaffolding layers, evaluating AI opportunities, or thinking through AI's societal implications.
spatial-intelligence-li
by jonaKnowledge base on spatial intelligence as the next frontier in AI, based on Fei-Fei Li's insights from her Y Combinator AI Startup School talk. Use this skill when users ask about spatial intelligence concepts, 3D world modeling, the evolution of computer vision from ImageNet to World Labs, AI research strategy and problem selection, or when seeking advice on AI entrepreneurship and founding AI companies. Also trigger when discussing the relationship between vision/spatial understanding and AGI, differentiating generative vs discriminative models in 3D contexts, or exploring the data-algorithm-compute trinity for AI breakthroughs.
ai-search-strategy-srinivas
by jonaKnowledge base containing insights from Aravind Srinivas (Perplexity CEO) on building AI-powered search products, competitive strategy against well-funded incumbents, and the future of agentic browsers. Use this skill when users ask about Perplexity's strategy, AI search product development, competing with Google/OpenAI/Anthropic, building answer engines, agentic browser concepts, startup competitive moats, or when analyzing the AI search market landscape. Also use when discussing how to position AI products against incumbents or when exploring the "cognitive operating system" concept for browsers.
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.