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...
openspec-explore
by BqLee-AIOpenSpec 探索模式，用于在创建或实施 change 前后澄清需求、调查代码、比较方案和沉淀决策；如需写入 OpenSpec artifacts，默认生成中文正文并保留英文校验语法。
openspec-apply-change
by BqLee-AI按 QuantAgent OpenSpec change 实施任务。用户要求开始实现、继续实现、推进 tasks、或基于已审核 OpenSpec 写代码时使用；实现前会检查中文 artifacts、工程质量 gate 和 strict validate 状态。
gh-issue-create
by BqLee-AI用于为本仓库创建、拆分、补写或批量整理 GitHub 开发 issue；当讨论、设计差距、OpenSpec seed、PR 评论或用户粗略需求需要先被压成可协作 issue 时使用，输出 why now、范围、非目标、未决点、验收、验证和 OpenSpec 处理。
gh-issue-deliver
by BqLee-AI用于接手并交付本仓库 GitHub 开发 issue；当用户给出 issue 编号、要求实现 issue、处理 issue 关联 change 或从 issue 推进 PR 时使用，先读取 issue/评论，按 rooted OpenSpec 补齐或复用 change，再实现、验证、更新任务并收口。
gh-pr-comments
by BqLee-AI用于处理本仓库 PR review comments、Copilot/AI 审查建议、CI 评论和维护者反馈；当用户要求修 PR 评论、评估 review、回复评论或继续修改 PR 时使用。
openspec-propose
by BqLee-AI为 QuantAgent 创建新的 OpenSpec change，并一次性生成可评审、可校验、可实施的 proposal、design、specs 和 tasks。用户要求提 proposal、创建 change、生成 OpenSpec、规划行为/架构/契约变更时使用。
semiconductor-evidence-research
by BqLee-AIUse when the evidence_research_analyst searches public sources and compresses semiconductor event evidence.
semiconductor-market-analysis
by BqLee-AIUse when a semiconductor MainAgent needs to reason about AI GPU, HBM, foundry, equipment, memory, supply chain, and risk impact from a routed event.
gh-pr-create
by BqLee-AI用于为本仓库准备、撰写和创建 GitHub PR；当实现已完成，或 OpenSpec artifacts 需要先单独提 PR 审核时使用，负责从 issue/OpenSpec change 形成 PR 说明、提交证据链、选择验证结果并推送分支。
ai-code-review
by BqLee-AI用于对本仓库 PR、commit、diff 或代码变更做 AI Code Review；当用户要求 review PR、review commit、review diff、代码审查、CR 规范检查、AI CR 或检查变更是否符合 QuantAgent 模块边界时使用。
openspec-archive-change
by BqLee-AI归档已完成的 QuantAgent OpenSpec change，并在归档前检查 artifacts、tasks、delta spec 同步和中文/英文语法边界。用户要求 finalize、archive、归档 change 或同步 stable spec 时使用。
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.