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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agent-ruler-openmcp
by LSTM-KirigayaGuidance for installing, upgrading, verifying, and using the npm package @agent-ruler/openmcp and its openmcp CLI. Use when a user asks how to install OpenMCP from npm, start or diagnose the Gateway, use OpenMCP CLI commands, configure skills through SKILL_PATH, troubleshoot global installs, or publish/test the CLI package.
skill-test
by LSTM-KirigayaTest skill for SDK skill integration. Use read_skill_file to read reference.md for more details.
onebot-ops
by LSTM-KirigayaOneBot (QQ/Lagrange) 渠道运维与使用规范。消息收发通过 Channel outbound 与 deliver 完成，不依赖 Agent 工具。
lagrange-qq-bot
by LSTM-KirigayaQQ 机器人核心驱动索引。定义了白面鸮人格、SOP 流程及工具调用入口。
memory
by LSTM-Kirigaya基于向量的长期记忆系统。用于存储用户偏好、历史纠正。
util
by LSTM-Kirigaya外部扩展能力，包括网络搜索、网页内容转换。
execute-task
by LSTM-Kirigaya机器人唯一的动作出口。通过 TS/JS 代码调用 OneBot API。
history-message
by LSTM-Kirigaya历史消息搜索功能。支持关键词搜索群聊和好友的历史消息记录。
browser-use
by LSTM-KirigayaBrowser automation tool for AI agents. Use when needing to automate browser tasks, scrape web pages, interact with websites, take screenshots, fill forms, or perform any web automation. Supports both CLI commands and Python Agent API.
tencent-ses-service
by LSTM-Kirigaya腾讯云官方的邮件推送 SES 服务使用 skill。 涵盖：SMTP 发送邮件所需的全部环境变量、API 接口设计规范、验证码邮件的完整发送流程（含人机验证、频率控制、Redis 存储）。 本 skill 以网络协议 / HTTP API 接口级别描述，不绑定具体编程语言。
bishe-guider
by LSTM-Kirigaya中文学术毕业论文（本科/硕士/博士）全流程写作指导与质量检查 Skill。 用于辅助毕业论文的撰写、修改、审查与定稿，覆盖写作风格、文献检索、 论文结构规范、排版语法、实验规范、盲审合规性、图表一致性等维度。 使用场景： (1) 撰写或修改论文章节内容时，确保符合学术写作规范 (2) 润色文本，去除 AI 写作痕迹，使行文自然如人类研究者亲笔 (3) 检索、筛选和管理学术参考文献 (4) 检查论文结构、逻辑一致性、盲审匿名性、图表风格一致性 (5) 定稿前的全面复盘检查
dust-tarui-layout
by LSTM-Kirigaya通用 Tauri 桌面端 UI 布局模板，复现 DiskRookie 风格的无边框窗口外壳、粘性顶栏、主内容滚动区与专家模式分栏结构。适用于从 0 到 1 快速构建类似布局的客户端软件（如笔记管理、即时通讯等）。基于 Tauri 2 + React + MUI + Tailwind。
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.