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.
Querying local SQLite index...
video-craft
by sendaifunFrame-level visual composition and product demo presentation for Remotion videos. Use when the user says "video looks generic", "make video frames look better", "video frame design", "device frame", "product demo video craft", "video CTA", "end card", "video composition", "video craft", "screenshot in video", "frame quality", or when reviewing Remotion compositions for visual quality. Sits on top of marketing-video — adds the visual design layer for each frame. Does NOT claim "create a video" or "marketing video" — those route to marketing-video.
product-review
by sendaifunProduct quality review — UX flows, onboarding, feature completeness, and user value. Use when a user says "product review", "review my product", "UX review", "is my product good", "product quality", "user experience review", "onboarding review", or "feature audit". Different from code review (review-and-iterate) and product roast (roast-my-product) — this is structured, balanced evaluation.
review-and-iterate
by sendaifunReview Solana project code for quality, security, and production readiness. Use when a user says "review my code", "is this production ready", "audit my program", "what should I fix", "code review", or "check for security issues".
roast-my-product
by sendaifunHarsh, honest product critique — find every weakness before users do. Use when a user says "roast my product", "harsh feedback", "be brutal", "what sucks", "find weaknesses", "product critique", "tear it apart", or "what would kill this". Deliberately harsh but constructive — scores each dimension and explains exactly what to fix.
scaffold-project
by sendaifunSet up a complete Solana project workspace from a validated idea. Use when a user says "scaffold my project", "set up my workspace", "what stack should I use", "create the project structure", or "initialize my project". Reads idea-context.md from a prior idea phase if available. Leverages solana-new's catalogs of 106 repos, 77 skills, and 36 MCPs.
verify-humanity-poh
by sendaifunIntegrate Proof of Human (POH) API to distinguish real users from bots in any Solana app. Use when a user says "filter bots", "verify humans", "sybil protection", "airdrop eligibility", "bot detection", "proof of human", "POH", "verify wallet is human", "gate my airdrop", "DAO sybil resistance", "check if wallet is real", "human verification", or "anti-bot". Works for airdrops, DAO voting, NFT allowlists, and API gating.
virtual-solana-incubator
by sendaifunDeep technical Solana bootcamp — SVM architecture, Rust patterns, program development. Use when a user says "Solana incubator", "teach me Rust for Solana", "SVM deep dive", "Solana bootcamp", "learn Solana development", "deep dive Solana", "PDA tutorial", "CPI tutorial", or "Anchor tutorial". Structured curriculum that assesses level and assigns exercises.
colosseum-copilot
by sendaifunSearch and analyze 5,400+ Solana hackathon projects using Colosseum Copilot. Find similar projects, discover winner patterns, identify gaps, and explore ML clusters. Use when a user says "colosseum copilot", "hackathon projects", "winner patterns", "gap analysis hackathon", "similar Solana projects", or "colosseum landscape". Requires a Colosseum Copilot token.
competitive-landscape
by sendaifunMap the competitive landscape for a crypto product idea. Use when a user says "who are my competitors", "map the competitive landscape", "what exists in this space", "show me similar projects", or "competitive analysis". Leverages solana-new's catalogs of 106 repos, 78 skills, and 36 MCPs.
defillama-research
by sendaifunResearch DeFi protocols and market opportunities using DefiLlama data. Use when a user says "show me TVL data", "which protocols are growing", "DeFi market research", "what should I build in DeFi", "find DeFi opportunities", "analyze protocol TVL", or "which chains are trending". Uses TVL as a trust metric to suggest protocols worth building on or integrating with.
find-next-crypto-idea
by sendaifunInterview users sharply to discover, rank, or validate what they should build in crypto. Use when a user asks what to build in crypto, wants startup ideas in a crypto niche such as DeFi or AI x crypto, wants blunt feedback on an existing crypto idea, or wants a concrete artifact comparing the best next ideas. Treat the bundled idea datasets as inspiration, not constraints, and always combine them with fresh market research.
learn
by sendaifunManage project learnings across sessions. Review, search, prune, and export what superstack has learned. Use when asked to "what have we learned", "show learnings", "prune stale learnings", "export learnings", or "remember this". Proactively suggest when the user asks about past patterns or wonders "didn't we fix this before?"
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.