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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ddd-architecture
by 7SpadeComprehensive DDD (Domain-Driven Design) skill for Xuanwu. Covers all four layers (Presentation, Application, Domain, Infrastructure), implementation patterns, scaffolding, and audit workflows. Use when designing, implementing, or auditing DDD-structured code in this repository.
ddd-progressive-layering
by 7SpadeWorkflow for progressively migrating existing Xuanwu code to Layered Architecture in Domain-Driven Design. Use when refactoring mixed-responsibility slices, extracting domain invariants, introducing port interfaces, or reducing direct infrastructure coupling without a big-bang rewrite.
shadcn
by 7SpadeProject-aware shadcn/ui skill for Xuanwu. Knows the project's component aliases, registry configuration, installed components, Tailwind setup, and correct import paths. Use when adding, customizing, or troubleshooting shadcn/ui components in this project. Triggers: "shadcn", "ui component", "add component", "button", "dialog", "dropdown", "form", "table", "card", "shadcn/ui", "radix", "component registry".
x-framework-guardian
by 7SpadeXuanwu 架構守護者三位一體掃描工作流。Use this skill when you need to run an architecture compliance audit, check for cross-module boundary violations, generate migration git mv commands, bootstrap a new Domain Module, or validate a module's logic chain against the L0→L5 canonical flow. Trigger keywords: architecture audit, drift report, boundary audit, migration audit, new module, logic chain, compliance status, 架構審計, 邊界巡邏, 清理舊債, 建立模組, 邏輯鏈驗證.
xuanwu-diagram-standards
by 7SpadeDiagram standards, Mermaid guidelines, architecture layer color system, layout principles, refactoring rules, and quality targets for all Xuanwu architecture diagrams. Use when designing, refining, or reviewing architecture diagrams in this repository. Triggers: "architecture diagram", "Mermaid", "diagram refactor", "diagram review", "diagram standard", "layer color", "diagram quality".
xuanwu-docs-index
by 7SpadeIndex and navigate Xuanwu architecture and management docs. Use this skill when you need architecture SSOT lookup, governance rule tracing, open-vs-archived management item routing, or documentation triage keywords like architecture, management, issue, debt, security audit, semantic conflict, and performance bottleneck.
xuanwu-intent-pipeline
by 7SpadeXuanwu 六步驟智能理解管道（Six-Step Intent Pipeline）。 在執行任何代理任務之前，透過結構化步驟完整理解用戶意圖。 Use this skill when: decomposing an ambiguous request, routing to the correct agent, or verifying you have captured all parameters before making code or documentation changes. Triggers: "understand intent", "analyze request", "pipeline", "六步驟", "意圖分析", "任務拆解", "before dispatch", "clarify scope".
xuanwu-skill
by 7SpadeReference codebase for Xuanwu Platform. Use this skill when you need to understand the structure, implementation patterns, or code details of the Xuanwu Platform project.
xuanwu-test-expert
by 7SpadeNext.js local preflight and diagnostic skill for Xuanwu. Starts localhost:9002, performs next-devtools project structure and realtime runtime/metadata analysis, and applies minimal automated fixes when safe.
developing-genkit-js
by 7SpadeDevelop AI-powered applications using Genkit in Node.js/TypeScript. Use when the user asks about Genkit, AI agents, flows, or tools in JavaScript/TypeScript, or when encountering Genkit errors, validation issues, type errors, or API problems.
angular-material-v20
by 7SpadeSkills for working with Angular Material v20 (@angular/material: "~20.0.0") UI component library in Angular applications.
agent-governance
by 7SpadePatterns and techniques for adding governance, safety, and trust controls to AI agent systems. Use this skill when: - Building AI agents that call external tools (APIs, databases, file systems) - Implementing policy-based access controls for agent tool usage - Adding semantic intent classification to detect dangerous prompts - Creating trust scoring systems for multi-agent workflows - Building audit trails for agent actions and decisions - Enforcing rate limits, content filters, or tool restrictions on agents - Working with any agent framework (PydanticAI, CrewAI, OpenAI Agents, LangChain, AutoGen)
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.