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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xgjk-skill
by evan-zhang玄关 Skill — 三位一体的 Skill 全生命周期工具：发现平台已有 Skill、按 XGJK 协议创建新 Skill、发布/更新/下架 Skill
xgjk-skill-auditor
by evan-zhangAudit and score Agent Factory skills across 5 dimensions before publishing. Use when reviewing a skill before ClawHub release, checking skill quality, running security scan, generating PASS/REVISE verdict, or auditing cms-cwork / cms-sop / tpr-framework and other factory skills.
pharma-search-cn-policy
by evan-zhang城市院外检索国内段：按合同串行维度取证，将 minmax_web_search_mcp 结果规范追加到 evidence.jsonl。
07-hukou-settlement-collector
by evan-zhang使用内置 references 模块文档逐渠道采集中国城市“落户”政策信息。适用于用户提供目标城市和年份/时间范围后，围绕本科学历在职员工通过人才引进或积分落户方式申请落户的可申请性、社保要求、社保年限、断缴补缴规则和积分落户社保加分规则等指标使用 搜索工具检索官方与辅助来源、保留完整来源声明，并写入固定目录下 3 个 Markdown 文件且不做最终政策裁决的场景。
05-vehicle-plate-lottery-collector
by evan-zhang使用内置 references 模块文档逐渠道采集中国城市“车牌摇号”政策信息。适用于用户提供目标城市和年份/时间范围后，围绕新能源车指标、燃油车指标、摇号资格、上牌资格与社保年限要求等指标使用 搜索工具检索官方与辅助来源、保留完整来源声明，并写入固定目录下 3 个 Markdown 文件且不做最终政策裁决的场景。
01-out-of-town-medical-collector
by evan-zhang使用内置 references 模块文档逐渠道采集中国城市“异地就医”政策信息。适用于用户提供目标城市和年份/时间范围后，围绕 11 项异地就医指标使用 搜索工具检索官方与辅助来源、保留完整来源声明，并写入固定目录下 3 个 Markdown 文件且不做最终政策裁决的场景。
cms-bd-evaluation-system
by evan-zhang康哲药业（CMS）医药BD投前评估体系——覆盖D/A/B/E/C五大群组共20个独立技能、1个非独立治理工具包、Gate 0~5 + Final门控路径、财务硬门槛、麦肯锡风格HTML报告输出。 当用户提到以下任意场景时必须触发此技能： - 医药BD评估、投前评估、标的评估、引进评估、尽职调查、due diligence、产品引进分析 - 仿制药/生物类似药/创新药/医美/OTC/国际市场的引进评估 - 多标的筛选、横向比选、BD优先级排序 - 市场全景分析、竞争格局分析、准入策略 - 国内合作、商业化授权、代理权/推广权接管、院内流向院外、消费健康品种评估 - 国内Biotech股权投资+商业化授权双引擎 - 康联达（Rxilient）国际市场BD - Gate评估、One-pager、投委会决策包 - 提到A-0/A-1/A-2/A-3/A-4/A-5/A-6/A-7/A-8/B-1/B-2/B-3/C-1/C-2/C-3/D-0/D-1/D-2/D-3/E-1等技能编号 - 用户说"帮我评估这个产品"、"写BD报告"、"做个引进分析"、"多少钱值得引进" 即使用户表述模糊，只要涉及CMS业务主体（深康/德镁/维盛/院外业务/天津康哲/康联达）下的产品引进或BD工作，都应立即触发本技能。 单一入口触发词（v0.9.2+）：新商机入池 / 跑一个品种 / 走完整评估 / 全链路评估 / 从品种名+公司名启动 / 自动跑全链路 / 商机驱动。
06-children-school-admission-collector
by evan-zhang使用内置 references 模块文档逐渠道采集中国城市“子女上学”政策信息。适用于用户提供目标城市和年份/时间范围后，围绕非本地户籍或跨区域家庭子女在指定城市接受义务教育及参加中考、高考的社保要求、居住证要求、区级差异、房产与租房积分差异和时间节点要求等指标使用 搜索工具检索官方与辅助来源、保留完整来源声明，并写入固定目录下 3 个 Markdown 文件且不做最终政策裁决的场景。
vbp-market-revitalization-report
by evan-zhang生成VBP（带量采购）集采品种市场衰退深度诊断与重振实操方案的麦肯锡风格HTML报告。 当用户提到以下任意情形时必须触发此技能： VBP品种分析、集采品种诊断、集采市场重振、带量采购产品策略、原研药市场衰退分析、 不在院医院分析、在院率分析、VBP执行力诊断、集采后原研药保护策略、 "帮我分析VBP品种"、"集采品种为什么在跌"、"怎么做集采品种市场重建"、 "我们VBP产品的在院率下滑了"、"集采产品销售衰退怎么办"、 提供医院流向数据/代表调研表并要求分析销售现状的场景。 典型触发场景（可直接代入生成报告）： - 以某省市（如上海）为样本分析集采产品生意衰退原因 - 提及区域报告揭示的共性问题（人员心态放弃、政策导向只关注总量、占位不作为） - 要求构建销售经营管理数据字典、落地日常监控协调赋能机制 - 要求从大区经理视角给出实操方案（思想动员+市场调查+人员调整+激励政策+日常管理工具） - 要求分角色释义（集团总部/大区/省区）和跨部门协同（MNC经验借鉴） 也适用于需要对医药销售区域执行力进行品种×分组×个人三层穿透分析的场合。 输出一份完整的HTML格式诊断与实操重建报告，覆盖：现状三层穿透、问题穷尽、独立洞见、 实操方案（人员决策·摸排工具·激励政策·日常管理·三层角色·跨部门协同·MNC对标）。
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.