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...
xxe-testing
by Q16G检测 XXE（XML 外部实体注入）风险；当目标存在 XML 文件上传、SOAP 接口、SVG 处理等 XML 解析功能时触发。
xss-testing
by Q16G检测 XSS（反射/存储/DOM）风险；当用户输入回显在页面中、搜索结果含用户输入、错误消息反射用户输入时触发；适用于表单、搜索、评论、个人资料等场景。
jwt-weakness
by Q16GJWT 弱密钥与信息泄露检测 — 检测 JWT 算法配置、密钥强度与敏感 claims 暴露；适用于登录认证、API 网关与单点登录场景。
dataflow-analysis
by Q16G数据流分析与污点追踪 — 对候选漏洞做跨函数 source-to-sink 数据流确认，验证污点传播路径是否真实可达；支持 SSA 引擎分析与手动 fallback 两种模式。
notification-abuse
by Q16G通知滥用/邮箱短信轰炸检测 — 检测短信、邮件、验证码发送接口的轰炸与反滥用缺陷，统一分诊连续发送/冷却时间/多维限流与低成本绕过（XFF、空格、标准化变体）/人机挑战/多目标资源耗尽。
open-redirect-testing
by Q16G检测开放重定向风险；当目标存在含重定向参数的 URL（url=、redirect=、next=、return=、goto=）时触发；适用于登录后跳转、OAuth 回调、链接中转等场景。
path-traversal-lfi
by Q16G检测路径穿越和本地文件包含（LFI）风险；当目标存在文件读取/下载/预览功能且含路径参数时触发；适用于文件下载、日志查看、模板加载等场景。
recon-methodology
by Q16G侦察方法论 — 系统化的攻击面枚举、技术栈指纹识别和信号收集，为后续任务清单的适用性判断提供依据。
registration-abuse
by Q16G注册机制批量注册检测 — 检测注册接口是否缺少反自动化与频率限制，导致可被批量创建账号；适用于开放注册、邀请注册与手机号邮箱注册场景。
sensitive-info-exposure
by Q16G敏感信息未脱敏检测 — 检测接口响应、日志回显、导出内容中是否暴露未脱敏敏感信息；适用于用户信息、认证信息、财务信息与系统配置场景。
sql-injection-comprehensive
by Q16GSQL 注入多策略综合检测 — 注入类型或数据库不明确时统一分诊布尔盲注/时间盲注/报错/UNION；适用于接口审计、黑盒探测与快速定级场景。
ssrf-testing
by Q16G检测 SSRF 风险；当目标存在服务端发起请求的功能（URL 参数、webhook、预览/导入外部资源）时触发；适用于链接预览、文件导入、代理请求等场景。
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.