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...
markdown-agents
by M4n5terCreate, inspect, migrate, and maintain OpenCode markdown agents under .opencode/agents or ~/.config/opencode/agents, including frontmatter, permissions, provider options, examples, and restart workflow.
simplify-code
by M4n5ter使用并行 subagent 从复用性、代码质量和效率角度审查变更代码，并直接修复具体清理问题。
tunnel-doctor
by M4n5terDiagnoses and fixes conflicts between Tailscale and proxy/VPN tools (Shadowrocket, Clash, Surge) on macOS. Covers five conflict layers - (1) route hijacking, (2) HTTP proxy env var interception, (3) system proxy bypass, (4) SSH ProxyCommand double tunneling, and (5) VM/container runtime proxy propagation (OrbStack/Docker). Includes SOP for remote development via SSH tunnels with proxy-safe Makefile patterns. Use when Tailscale ping works but SSH/HTTP times out, when browser returns 503 but curl works, when git push fails with "failed to begin relaying via HTTP", when Docker pull times out behind TUN/VPN, when setting up Tailscale SSH to WSL instances, or when bootstrapping remote dev environments over Tailscale.
thermo-nuclear-review
by M4n5ter对当前分支改动做全面的安全性与正确性审查。用于 thermo nuclear、thermonuclear、深度 review、分支或 PR diff 审计，重点检查 bug、破坏性变更、安全漏洞、开发体验回退、feature gate 泄漏、测试可靠性和虚假测试信心。
gejv
by M4n5ter当用户要求“打开格局”、think bigger、挑战保守方案、避免过度兼容、跳出局部细节、输出更大胆的高位方案判断，或讨论架构/产品方向时不要被重构困难吓住时使用。
ubuntu-gnome-wayland-rdp
by M4n5terSet up, verify, and troubleshoot Ubuntu GNOME Wayland Remote Login over RDP with GNOME Remote Desktop system mode and GDM. Use when the user wants lock-screen or logged-out RDP access to Ubuntu GNOME, insists on Wayland instead of xrdp/Xorg, connects from macOS Windows App or Microsoft Remote Desktop, sees server redirection failures, black screens, login loops, blurry or offset resolution, stale handover sessions, or wants cleanup after remote desktop experiments.
thermos
by M4n5ter并行运行两个热核级审查流程，然后综合它们的发现。用于用户明确要求 thermos、double thermo review、两个 thermo reviewer、并行 review agent，或同时覆盖 bug、安全问题、测试可靠性与代码质量的分支审计。
teach
by M4n5terTeach the user a new skill or concept, within this workspace.
thermo-nuclear-code-quality-review
by M4n5ter运行极其严格的可维护性审查，重点检查抽象质量、超大文件、意大利面式条件增长、重复逻辑、低价值测试和有意义的清理机会。用于 thermo-nuclear code quality review、thermonuclear maintainability review、深度代码质量审计、针对变更代码或测试代码的 cleanup/fix 请求、复用检查、垃圾测试清理，或特别严格的可维护性审查。
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.