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...
factor-backtest-skill
by GRD-Chang安全、高效地批量回测 8 个 Alpha 表达式，内置验证、熔断和异常处理机制。
alpha-research-recorder
by GRD-ChangAlpha 研究日志记录器 - 作为研究流程的数据持久化层。 核心功能： 1. 根据记录类型（session_meta/round/final_summary）引用对应的模板文件 2. 接收结构化数据并验证必填字段 3. 创建/更新 YAML 或 Markdown 日志文件 使用方式： - 调用时指定 record_type 和 session_id - 查看模板文件了解完整字段结构：templates/{record_type}.template - 传入数据合并到模板，生成最终文件
html-portal
by GRD-ChangUse when the user needs to preview a single HTML file generated on a remote machine, especially from Codex, SSH, iOS, or Windows app workflows. Creates a temporary token-protected preview URL with HTML Portal, verifies it, reports expiry and cleanup commands, and avoids exposing directories or sensitive HTML.
cat-villa-designer-en
by GRD-ChangDesign custom cat villas through Socratic questioning, staged constraint mapping, first-pass layout design, and ASCII visualization. Use when Codex needs to help a user design or refine a cat villa, cat cabinet, or cat house, especially when the user has no design background, only vague lifestyle needs or reference images, cannot describe structural ideas clearly, has conflicting zone requirements, needs separated litter circulation, or wants a concrete first-pass design instead of stopping at a requirement summary.
cat-villa-designer
by GRD-Chang通过苏格拉底式提问、分阶段约束梳理、初版布局设计和 ASCII 示意图生成，帮助用户设计定制猫别墅。适用于帮助用户设计或完善猫别墅、猫柜、cat villa、cat house，尤其是在用户没有设计经验、只有模糊生活需求或参考图、说不清结构想法、功能区冲突、猫砂动线复杂，或希望快速得到可理解的具体初版方案而不是停留在需求总结时使用。
html-portal-zh
by GRD-Chang当用户需要在远程机器上预览单个 HTML 文件时使用，尤其适用于 Codex、SSH、iOS 手机、Windows Codex App 工作流。使用 HTML Portal 创建临时 token 预览 URL，验证可访问性，报告过期时间和清理命令，并避免暴露目录或敏感 HTML。
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.