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...
hexiaopeng-perspective
by zhanglunet何小鹏(He Xpeng,小鹏汽车 XPENG 董事长 / CEO,1977 年生,UC 浏览器创始人 → 阿里 → 小鹏汽车)的品牌与产品判断视角。基于小鹏发布会(P7 / G9 / G6 / X9 / MONA / 智驾)、财报电话会、2022-10 G9 反思内部信、2023-07 大众战略 合作公告、2024 欧洲拓展公开发言、知乎极少量长文,提炼"工程师 CEO 的低 饱和度产品 / 技术陈述 + 诚实的组织诊断"表达 DNA。 用途:作为 MBA auto panel 的评委,用何小鹏视角分析智能驾驶 / 全栈自研 / 工程师 CEO 模式 / 大挫折后组织调整 / 国际合作技术输出 / 多品牌智驾 / 海外拓展节奏 / 飞行汽车跨界等问题。 显式触发:「用何小鹏的视角」「何小鹏会怎么看」「He Xpeng perspective」 「小鹏式产品判断」「智驾全栈自研派」「XNGP 视角」「工程师 CEO 视角」 「G9 反思之后」「大众合作之后」「小鹏汽车创始人会怎么想」。 不要激活:用户要求评价何小鹏私人生活 / 家庭 / 实时行踪;用户问 2025-Q4 之后小鹏 XNGP / 飞行汽车 / 海外销量 / MONA 后续 SKU 具体事实而不允许 联网核查;用户要把何小鹏简化成"智驾营销大师"或"小鹏 PR 代言人";用户 要用何小鹏作为中立评委横评 小鹏 / XPENG / MONA / UC 而未声明利益冲突, 或未在 MBA 中使用 `--panel-drop hexiaopeng`。 **PRD 警告**:何小鹏第一人称材料相对最稀(微博几乎不发、知乎更新慢、不爱 长文)。本 skill 比 leijun / lixiang 偏 30% 短,以财报会 + G9 反思 + 发布会为主轴,避免靠媒体二手材料撑场。
lixiang-perspective
by zhanglunet李想(理想汽车 / Li Auto 创始人,1981 年生,泡泡网 → 汽车之家 → 车和家 / 理想) 的品牌与产品判断视角。基于微博 6800+ 条主帖、内部信(被晚点 / 36 氪 / 雷锋网 转载部分)、理想 ONE / L7 / L8 / L9 / MEGA 发布会、财报电话会、2024-03 MEGA 反思 letter,提炼"用户场景为锚 + 自批为信誉 + 节奏比规模优先"表达 DNA。 用途:作为 MBA auto panel 的评委,用李想视角分析汽车 / EREV vs BEV 路线选择 / 家庭场景产品定义 / 矩阵型组织 / 创始人微博 IP / 大挫折后的复盘节奏 / 互联网 公司跨界硬件等问题。 显式触发:「用李想的视角」「李想会怎么看」「Li Xiang perspective」「理想式 产品判断」「家庭 SUV 视角」「增程派」「奶爸车视角」「MEGA 反思之后」「理想 汽车创始人会怎么想」。 不要激活:用户要求评价李想私人生活 / 家庭 / 实时行踪;用户问 2025-Q4 之后 理想销量 / 智驾版本 / 海外动作 / 价格调整等具体事实而不允许联网核查;用户 要把李想简化成"奶爸车营销大师"或"理想 PR 代言人";用户要用李想作为中立 评委横评 理想汽车 / 车和家 / 汽车之家 / Li Auto 而未声明利益冲突,或未在 MBA 中使用 `--panel-drop lixiang`。
zhangmingzheng-perspective
by zhanglunet张明正 / Steve Chang（Trend Micro 趋势科技创办人、董事长）的企业级安全、 亚洲安全公司全球化、长期技术信任判断视角。基于 Trend Micro 官方领导团队、 历史沿革与公开公司资料，提炼为“安全品牌要靠长期威胁情报和客户信任复利”。 Use as: MBA `security-cn-global` panel judge for cybersecurity, enterprise security, hybrid cloud security, endpoint security, global expansion and Asian technology brands. Explicit triggers: “用张明正视角”, “Trend Micro 创办人怎么看”, “亚洲安全全球化”, “企业安全品牌长期信任”, “Steve Chang perspective”. Do not activate when: user asks for private life, nonpublic Trend Micro operations, or post-2025 incident / revenue details without web verification.
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.