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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llm-wiki-interview
by ProgrammerAnthony面试向 LLM Wiki 全流程:Raw 层在 raw/ 沉淀 _research.md、basic/、blog/(见 references/raw-layer.md);Wiki 层只读 raw/、编译维护 wiki/(实体/概念、index、log,见 references/wiki-layer.md)。触发:建资料包、收录博客、从 raw 导入 wiki、查询、lint、面试备考知识库。关键词:LLM Wiki、raw、wiki、ingest、面试、Obsidian、知识库、用户供稿。
writing-plans
by ProgrammerAnthonyUse when 已有经批准的设计/规格说明、多步骤实施任务,在动代码之前需要可执行任务清单时。触发场景:写实施计划、拆解开发任务、implementation plan、执行计划文档、任务拆分、按 TDD 步骤写计划。
ai-agent-security
by ProgrammerAnthonyAI Agent 安全开发与防护最佳实践,包含prompt注入防护、代码执行安全、敏感信息保护、合规审计全流程规范。
architecture-advisor
by ProgrammerAnthonyUse when 用户需要设计新系统架构、评审或优化已有系统架构、选择技术方案时。触发场景:架构分析、架构设计、系统设计、architecture、架构优化、系统架构、架构评审、架构咨询、技术方案、技术设计、如何组织代码结构、模块划分、服务拆分、数据库选型、微服务设计。
brainstorming
by ProgrammerAnthonyUse when 用户要创建新功能、构建组件、添加功能或修改行为等任何创作性工作之前。必须在实施任何方案前先触发本技能。触发场景:头脑风暴、方案设计、需求分析、功能规划、设计方案、系统设计、我想做、帮我想想、如何实现、方案评估、设计评审。
code-review-expert
by ProgrammerAnthonyUse when 用户要求审查代码、评估代码质量、提交 PR 前检查、发现代码有潜在问题时。触发场景:代码审查、code review、审查代码、review、检查代码、代码检查、代码质量、代码评审、这段代码有问题吗、帮我看看代码、合并前检查。
code-security-audit
by ProgrammerAnthonyUse when 用户需要对代码进行安全审计、发现安全漏洞、上线前安全评估、检查代码是否存在安全风险时。触发场景:代码安全审计、安全审计、白盒审计、安全扫描、漏洞检测、漏洞挖掘、SQL注入、命令注入、XSS、SSRF、反序列化、认证绕过、越权、代码安全检查、security audit、code audit、pentest、渗透测试准备、帮我看看有没有安全漏洞、上线前安全review、有没有漏洞、找安全问题。
debug-expert
by ProgrammerAnthonyUse when 程序出现错误、异常、崩溃,或行为与预期不符,或测试失败,或无法定位问题根因时。触发场景:调试、debug、报错、错误、异常、bug、问题排查、故障排查、不工作、崩溃、无法运行、出错了、为什么不生效、运行报错、跑不起来、程序挂了。
docs-lookup
by ProgrammerAnthony通过 Context7 MCP 获取库和框架的实时最新文档,而非依赖训练数据,防止 API 幻觉。适用于查询任何库或框架的用法、配置、示例代码。触发词:怎么用、怎么配置、API参考、文档、示例代码、用法、接口、库文档、框架文档、documentation、docs、how to use、API reference、setup、configure、React怎么用、Next.js配置、Prisma查询、Vue用法、Express路由、Tailwind类名、Supabase认证、TypeScript类型、Zod验证、shadcn组件、Drizzle ORM、tRPC、Fastify、NestJS、Astro、SvelteKit、Nuxt、Vite、Vitest、Playwright。
frontend-code-review
by ProgrammerAnthonyUse when 用户需要审查前端代码(React/Vue/Next.js/TypeScript/Tailwind等)、检查代码质量、性能问题、可维护性、安全漏洞、最佳实践落地时。触发场景:前端代码评审、前端代码优化、React/Vue代码检查、TypeScript代码审查、前端性能优化、前端安全审计、前端代码规范检查。
frontend-performance-optimization
by ProgrammerAnthonyUse when 用户需要优化前端性能、提升页面加载速度、减少白屏时间、优化交互流畅度、进行性能排查时。触发场景:前端性能优化、页面加载慢、白屏时间长、卡顿、LCP/FID/CLS指标优化、前端性能分析、打包体积优化。
planning-with-files
by ProgrammerAnthonyManus 风格的“文件化规划”工作流。用 task_plan.md / findings.md / progress.md 作为持久化工作记忆,配合 Cursor hooks 实现:每次工具前回读计划、写文件后提醒更新、未完成阶段时 stop 自动继续。
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.