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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publish-skill
by alexanderopCreates a new GitHub repository for a Claude Code skill with proper README and directory structure. Use when you want to package and publish a skill so others can install it. Triggers on "publish skill", "publish this skill", "create skill repo", "package skill", "share this skill".
walkthrough
by alexanderopGenerates a self-contained HTML file with an interactive, clickable Mermaid diagram (flowchart or ER diagram) that explains how a codebase feature, flow, architecture, or database schema works. Designed for fast onboarding — each walkthrough is a visual mental model readable in under 2 minutes. TRIGGER this skill when ANY of these match: - The prompt starts with or contains "$walkthrough" (explicit trigger — always activate, even if no topic follows) - The user asks to "walk me through", "walkthrough", "trace the code path", "explain this flow", "show how X works", "how does X work step by step", "explain the architecture", "visualize the data model", "show the data structures", "show the relationships" - The user wants a visual/interactive explanation of code, flows, pipelines, or schemas When triggered, ALWAYS generate a walkthrough HTML file — never respond with just text. If "$walkthrough" is used with no topic, generate an overview walkthrough of the entire project.
vueuse-functions
by alexanderopApply VueUse composables where appropriate to build concise, maintainable Vue.js / Nuxt features.
screenshot-test
by alexanderopAdd a screenshot (visual regression) browser test for a canvas feature. Use when asked to "add a screenshot test for X", "visual test for X", "screenshot test X", "add a browser test with screenshots", or when the user wants to verify how something renders on the canvas.
nuxt-ui
by alexanderopBuild UIs with @nuxt/ui v4 — 125+ accessible Vue components with Tailwind CSS theming. Use when creating interfaces, customizing themes to match a brand, building forms, or composing layouts like dashboards, docs sites, and chat interfaces.
walkthrough
by alexanderopGenerate interactive HTML walkthroughs with clickable Mermaid diagrams (flowcharts and ER diagrams) that explain codebase features, flows, architecture, and database schemas. Use when asked to "walkthrough", "explain this flow", "how does X work", "trace the code path", "annotated diagram", "code walkthrough", "explain the architecture of", "walk me through", "database schema", "explain the tables", "data model".
vue-development
by alexanderopUse when planning or implementing Vue 3 projects - helps architect component structure, plan feature implementation, and enforce TypeScript-first patterns with Composition API, defineModel for bindings, Testing Library for user-behavior tests, and MSW for API mocking. Especially useful in planning phase to guide proper patterns before writing code.
debugging
by alexanderopDebug issues in the Second Brain Nuxt 4 + @nuxt/content v3 project. Use for any bug, test failure, or unexpected behavior.
demo-2-1-features
by alexanderopDemo skill showing all Claude Code 2.1 features. Use when asked to "demo 2.1" or "show new skill features".
enhancing-notes
by alexanderopEnhance book notes with Blinkist-style summaries. Use when asked to "enhance notes", "improve book notes", "add key insights", "expand notes", or "make notes better". Adds Core Message, Key Insights, Notable Quotes, and Who Should Read sections via parallel web research.
enhancing-talks
by alexanderopEnhance talk notes with Blinkist-style summaries and timestamps. Use when asked to "enhance talk", "improve talk notes", "add timestamps", "blinkist-style talk summary", or "make talk notes better". Adds Core Message, Key Insights with timestamps, Talk Structure, Notable Quotes, Who Should Watch, and Action Items via transcript analysis.
exploring-graph
by alexanderopAnalyze the knowledge graph for insights. Use when asked to "analyze connections", "graph report", "show hubs", "find orphans", or "knowledge map".
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.