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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log-analysis-document
by xiaoshudian555将日志表格生成为 Markdown 文档，含概述、涉及仓库组件、关键配置、日志表格、Mermaid 流程图、关键说明。触发词：生成Markdown、生成文档、日志文档。
log-analysis-fault-mode
by xiaoshudian555故障模式提炼器的适配器层。从日志表格中提炼故障模式，生成标准化故障模式库 CSV。触发词：提炼故障模式、生成故障模式库、故障模式库。
log-analysis-search
by xiaoshudian555从代码仓库中搜索日志语句，提取原始日志及其上下文（函数名、类名、文件位置）。输入仓库名+功能描述，输出原始日志列表供后续结构化使用。触发词：搜索日志、提取日志、日志搜索、grep日志、整理日志语句。
log-analysis-structurize
by xiaoshudian555将原始日志语句结构化为 9 列 CSV 表格。输入搜索阶段输出的原始日志列表，输出标准化日志表格（编号/出现场景/组件/日志级别/日志内容/出现阶段/含义/下一步走向/代码位置）。触发词：结构化日志、日志表格、整理日志表格。
log-diagnosis-pd-link-establishment
by xiaoshudian555PD实例建链失败的日志诊断。当P节点与D节点之间的KVCache链路建立失败、卡住或反复重试时，通过日志定位根因。触发词：建链失败、建链卡住、link failed、建链超时、Link exception、内存注册失败、PD链路异常。
log-quality-scan
by xiaoshudian555扫描日志文件或代码，逐一检查每条日志是否符合日志标准（分级/描述/组件/防刷屏/链路追踪/隐私），输出通过/不通过清单和改进建议。触发词：扫描日志、判断日志好坏、日志质量检查、日志扫描。
log-analysis-dispatcher
by xiaoshudian555日志分析调度器。从代码仓库中提取日志，生成日志表格、Markdown 文档、故障模式库、诊断 Skill 全链路产出。触发词：整理日志、整理日志全流程、日志分析完整流程、日志全链路分析。
log-diagnosis-framework
by xiaoshudian555日志问题定位技能组框架。定义了一组基于日志进行问题定位的 skill 的组织方式、通用结构和命名规范。每个具体问题（如缩P保D、建链失败）有独立的诊断 skill。触发词：日志定位、日志诊断、log diagnosis、问题定位、故障定位。
log-diagnosis-controller-recovery-terminate
by xiaoshudian555Controller 精度告警触发的实例「自杀」(terminate) 失败日志诊断。当 precision auto-recovery 应杀 P/D 却未杀、terminate_instance_for_recovery 失败、或 Recovery/NodeManager stop 报错时， 按 Coordinator→Controller→Recovery→NodeManager 链路定位根因。触发词：controller自杀、自杀失败、 terminate失败、precision-auto-recover、精度告警恢复、terminate_instance、Recovery:、实例终止失败、 auto-recovery failed、add_alarm、0xFC001107。
log-diagnosis-large-ep-startup
by xiaoshudian555大 EP 场景启动失败/拉不起来的日志诊断。当 Controller 或 Coordinator 无法到达 MindIE-MS coordinator is ready，或长期 not ready 时，按 cmotor 启动五阶段定位；PD 分离卡在就绪前则下钻 mindie-llm 建链。触发词：大EP拉不起来、大EP启动失败、大EP启动卡住、coordinator not ready、coordinator is ready、MindIE-MS coordinator is not ready、大EP起不来。
log-diagnosis-shrink-p-reserve-d
by xiaoshudian555缩P保D流程异常的日志诊断。当PD分离架构中D实例故障后触发缩P保D，流程卡住或失败时，通过日志定位根因。触发词：缩P保D失败、缩P保D卡住、缩P保D异常、shrink P reserve D、缩容失败、D实例故障恢复失败。
log-diagnosis-mindie
by xiaoshudian555MindIE/cmotor 日志诊断调度器。接收 MindIE/PyMotor/cmotor 相关日志，自动判断问题方向并调度对应子 skill 诊断。触发词：日志诊断、日志分析、日志定位、故障定位、MindIE日志、PyMotor日志、大EP拉不起来、coordinator not ready。
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.