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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fixed-wing-overall-sizing-runbook
by BaiSongt固定翼总体设计唯一入口：执行 Class I 闭环并在收敛后进入阶段 2–7 扩展分析，落盘输出报告/数据/外形资产，并可选 PySide6 实时可视化。
fixed-wing-overall-sizing-spec
by BaiSongt生成固定翼总体设计方案（需求→约束→设计点→初步尺寸→重量/性能闭合）。当需要给出固定翼总体方案、关键参数范围与决策依据时调用。
fixed-wing-constraints-runbook
by BaiSongt执行固定翼约束校核并给出设计点调整建议。当用户关心起降/失速/巡航/爬升约束是否满足或要定位卡点时调用。
fixed-wing-constraints-spec
by BaiSongt固定翼约束分析方案：失速/巡航/爬升/起降距离等约束线与设计点选择。当需要用约束分析确定 W/S 与 T/W 设计点时调用。
fixed-wing-weights-spec
by BaiSongt固定翼重量方案（Class I）：空重统计模型 + 航程燃油估算 + MTOW 迭代闭合。当需要建立重量闭合与敏感性分析时调用。
fixed-wing-aero-buildup-runbook
by BaiSongt执行固定翼气动阻力分解计算。当需要从几何参数计算详细阻力清单时调用。
fixed-wing-aero-buildup-spec
by BaiSongt固定翼气动阻力分解方案：基于几何外形的部件级阻力叠加（CD0 Buildup）。当需要用物理模型替代用户猜测的 CD0 时调用。
fixed-wing-aero-runbook
by BaiSongt执行固定翼气动一级模型（CD0/e/k、巡航L/D）。当总体闭环需要气动输入或要诊断航程/爬升受阻时调用。
fixed-wing-aero-spec
by BaiSongt固定翼气动方案设计：阻力分解、极曲线、升阻比与高升力装置取值范围。当需要建立可用于性能/重量闭合的气动模型时调用。
fixed-wing-design-loop-runbook
by BaiSongt指导固定翼总体设计如何迭代收敛与做敏感性检查。当总体入口已跑完但结果不满足/不收敛，需要系统化调整变量时调用。
fixed-wing-performance-runbook
by BaiSongt执行固定翼一级性能校核（巡航所需推力、爬升率/余度）。当总体闭环需要性能判据或要定位性能不满足原因时调用。
fixed-wing-performance-spec
by BaiSongt固定翼性能快算方案：巡航配平、所需推力、爬升率等一级性能校核。当需要对总体方案做快速性能余度检查时调用。
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.