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...
dangling-markup-injection
by ok-helloworldDangling markup injection playbook. Use when HTML injection is possible but JavaScript execution is blocked (CSP, sanitizer strips event handlers, WAF blocks script tags) — exfiltrate CSRF tokens, session data, and page content by injecting unclosed HTML tags that capture subsequent page content.
jndi-injection
by ok-helloworldJNDI injection playbook. Use when Java applications perform JNDI lookups with attacker-controlled names, especially via Log4j2, Spring, or any code path reaching InitialContext.lookup().
jwt-oauth-token-attacks
by ok-helloworldJWT and OAuth token attack playbook. Use when validating token trust, signing algorithms, key handling, claim abuse, bearer flows, and OAuth account-binding weaknesses.
vibe-pentest
by ok-helloworldAI 渗透测试：多 Agent 并行架构的 Web 应用渗透测试技能。 流程: 指纹识别 → 后台入口扫描 → API 预扫描 → 浏览器登录提取凭证（可选） → Katana 爬虫 → 过滤数据 → 攻击面映射 → 多 Agent 并行渗透测试 → 攻击链分析 → 漏洞证据复查 → 导出 JSON 报告 → 主机漏洞扫描 (可选)。 纯黑盒测试，不依赖源码。平台无关设计，可在任意 Agent 平台创建和使用。
ssti-server-side-template-injection
by ok-helloworldSSTI playbook. Use when template expressions, server-side rendering, preview features, or templating engines may evaluate attacker-controlled content.
ssrf-server-side-request-forgery
by ok-helloworldSSRF playbook. Use when the server fetches URLs, resolves hostnames, imports remote content, or can be driven toward internal networks, cloud metadata, or secondary protocols.
dns-rebinding-attacks
by ok-helloworldDNS rebinding attack playbook. Use when testing applications that trust DNS resolution for origin checks, interact with internal services from browser context, or when SSRF is not possible server-side but the target has client-side fetch/XHR to attacker-controlled domains.
xxe-xml-external-entity
by ok-helloworldXXE playbook. Use when XML, SVG, OOXML, SOAP, or parser-driven imports may resolve external entities, files, or internal network resources.
xss-cross-site-scripting
by ok-helloworldXSS playbook. Use when user-controlled content reaches HTML, attributes, JavaScript, DOM sinks, uploads, or multi-context rendering paths.
file-agent
by ok-helloworld负责文件类漏洞检测：路径穿越/LFI/RCE升级、任意文件上传、文件包含、任意文件下载、 Zip Slip、SVG/CSV 注入、PHP Wrapper 利用、PEARCMD RCE、编辑器路径利用
deserialization-insecure
by ok-helloworldInsecure deserialization playbook. Use when Java, PHP, or Python applications deserialize untrusted data via ObjectInputStream, unserialize, pickle, or similar mechanisms that may lead to RCE, file access, or privilege escalation.
email-header-injection
by ok-helloworldEmail header injection and spoofing playbook. Use when testing contact forms, email APIs, password reset flows, or any feature that constructs SMTP messages with user-controlled fields. Covers CRLF injection in headers, SPF/DKIM/DMARC bypass, and phishing amplification.
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.