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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iom-pentest
by chainreactorsAutonomous penetration testing via IoM C2 MCP tools. Adaptively executes based on user intent: situational awareness, reconnaissance, privilege escalation, credential harvesting, lateral movement, persistence, and more. Presents an execution plan and waits for user confirmation before sensitive operations. Trigger conditions: user mentions penetration testing, red team, post-exploitation, privilege escalation, lateral movement, credentials, persistence, situational awareness, or any scenario involving security assessment of a target through IoM.
iom
by chainreactorsComplete user guide for the IoM (Implant-over-Middleware) C2 framework. Covers architecture concepts, basic operations, startup parameters, authentication configuration, troubleshooting, documentation resources, and community feedback. Trigger conditions: use this skill when the user asks about how IoM works, command usage, architecture concepts, configuration methods, how to troubleshoot issues, where to find documentation, or how to file an issue. Should also trigger for questions like "how do I connect", "what is a session", "I got an error", or "is there documentation".
mal-develop
by chainreactorsMAL (Malice Scripting Language) plugin development guide. Helps users write Lua plugins for IoM, covering plugin structure, command registration, BOF invocation, resource management, event callbacks, testing, debugging, and publishing workflows. Trigger conditions: when users want to write MAL plugins, extend IoM commands, write Lua scripts, integrate BOFs, develop custom modules, or ask questions like "how to write a mal plugin", "how to add a new command to IoM", or "what Lua APIs are available".
iom-pentest
by chainreactorsAutonomous penetration testing through IoM C2 MCP tools. Covers the full engagement lifecycle: reconnaissance, privilege escalation, credential harvesting, lateral movement, and persistence. Operates in an OODA loop — each phase analyzes results before deciding the next action. Use this skill whenever the user wants to run automated pentest, red team assessment, privilege escalation analysis, post-exploitation, or any offensive operation through IoM — even if they just mention "pentest", "提权", "横向", "信息收集", "凭据", "持久化", or "自动化测试".
iom-opsec
by chainreactorsIoM Operational Security (OPSEC) advisor. Provides OPSEC methodology guidance, helps users understand operational risks, build secure operating habits, and accumulate experience through a case library. Does not execute commands directly; serves as decision support. Concrete technical specifications and OPSEC scoring are maintained in the iom-pentest skill. Trigger conditions: use when the user asks "is this safe?", "will this be detected?", "how should I think about OPSEC?", "risk assessment", "operational security advice", or "help me analyze the detection surface".
ctf-ops
by chainreactorsCTF 运维指南 — 回连验证、nuclei 模板使用、共享状态协议。 自动注入所有 CTF worker 的 system prompt。
okr-creator
by chainreactors为任何项目生成定制化 OKR skill。分析项目内容、文档、结构、历史，在目标项目 .claude/skills/okr/ 下生成可加载的 SKILL.md 并部署每日追踪 Action。
okr
by chainreactors本项目的 OKR 目标和关键结果。每次开始新任务前参考此 OKR 确保工作方向一致。生成时间：2026-03-17。基于项目诊断 + 用户意图共同生成。
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.