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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mapbox-integration-patterns
by MikeCheng1208Official integration patterns for Mapbox GL JS across popular web frameworks. Covers setup, lifecycle management, token handling, search integration, and common pitfalls. Based on Mapbox's create-web-app scaffolding tool.
copilotkit
by MikeCheng1208Build AI copilots, chatbots, and agentic UIs in React and Next.js using CopilotKit. Use this skill when the user wants to add an AI assistant, copilot, chat interface, AI-powered textarea, or agentic UI to their app. Covers setup, hooks (useCopilotAction, useCopilotReadable, useCoAgent, useAgent), chat components (CopilotPopup, CopilotSidebar, CopilotChat), generative UI, human-in-the-loop, CoAgents with LangGraph, AG-UI protocol, MCP Apps, and Python SDK integration. Triggers on CopilotKit, copilotkit, useCopilotAction, useCopilotReadable, useCoAgent, useAgent, CopilotRuntime, CopilotChat, CopilotSidebar, CopilotPopup, CopilotTextarea, AG-UI, agentic frontend, in-app AI copilot, AI assistant React, chatbot React, useFrontendTool, useRenderToolCall, useDefaultTool, useCoAgentStateRender, useLangGraphInterrupt, useCopilotChat, useCopilotAdditionalInstructions, useCopilotChatSuggestions, useHumanInTheLoop, CopilotTask, copilot runtime, LangGraphAgent, BasicAgent, BuiltInAgent, CopilotKitRemoteEndpoint, A2UI, MC
copilotkit-nextjs-integration
by MikeCheng1208Integrate CopilotKit AI components into Next.js frontend for building agentic UIs. Enables context-aware AI agents that can read app state and trigger tools/actions. Supports custom adapters for self-hosted LLMs and multiple provider integrations.
chakra-ui
by MikeCheng1208Builds accessible React applications with Chakra UI v3 components, tokens, and recipes. Use when creating styled component systems, theming, or accessible form controls.
dyad-swarm-pr-review
by MikeCheng1208Team-based PR review using Claude Code swarm. Spawns three specialized teammates (correctness expert, code health expert, UX wizard) who review the PR diff, discuss findings with each other, and reach consensus on real issues. Posts a summary with merge verdict and inline comments for HIGH/MEDIUM issues.
polaris-local-forge
by MikeCheng1208**[REQUIRED]** Use for **ALL** requests involving local Apache Polaris: setup, API queries, catalog operations, cleanup, teardown. **AUTO-ACTIVATE:** If `.snow-utils/snow-utils-manifest.md` contains `polaris-local-forge:` this skill MUST handle ALL operations including cleanup. **DO NOT** use `polaris` CLI (does not exist), curl to Polaris endpoints (needs OAuth), or docker ps checks - invoke this skill first. Triggers: polaris local, local iceberg catalog, local polaris setup, rustfs setup, create polaris cluster, try polaris locally, get started with polaris, apache polaris quickstart, polaris dev environment, local data lakehouse, replay from manifest, reset polaris catalog, teardown polaris, clean up, cleanup, delete cluster, remove resources, polaris status, list catalogs, show namespaces, list tables, show catalog, describe table, list principals, show principal roles, list views, polaris namespaces, polaris catalogs, query data, query table, query iceberg, query catalog data, show my data, show table d
vueuse-library-rule
by MikeCheng1208Encourages leveraging VueUse functions throughout the project to enhance reactivity and performance.
agno
by MikeCheng1208Agno AI agent framework. Use for building multi-agent systems, AgentOS runtime, MCP server integration, and agentic AI development.
biome
by MikeCheng1208Configure BiomeJS for projects - linting, formatting, and code style setup. Use when the user asks to set up biome, configure linting or formatting, migrate from eslint or prettier, enforce code style, or add biome to a project.
question-refiner
by MikeCheng1208将原始研究问题细化为结构化的深度研究任务。通过提问澄清需求，生成符合 OpenAI/Google Deep Research 标准的结构化提示词，完全替代 ChatGPT 的问题细化功能。当用户提出研究问题、需要帮助定义研究范围、或想要生成结构化研究提示词时使用此技能。
ant-design
by MikeCheng1208Builds enterprise React applications with Ant Design's comprehensive component library. Use when creating admin dashboards, data tables, complex forms, or enterprise UIs with consistent design language.
ant-design-knowledge-base
by MikeCheng1208Provides comprehensive answers about Ant Design components, documentation, and semantic descriptions using local knowledge base files. Use when asked about Ant Design, React UI components, design system, component semantics, or specific component usage.
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.