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...
collect-basic-info
by TashanGKD采集科研数字分身的基础信息（研究阶段、学科领域、方法范式、技术能力、科研流程能力）。当用户开始建立科研数字分身、或基础信息尚未填写时使用。
remediation-planner
by TashanGKD整改计划制定 Skill。将差距报告、审核发现、或不兼容分析转化为有优先级、有验收标准的可执行整改计划。每个步骤配对验证方法，防止「计划没有验证」的情况。触发词：「基于这个分析制定整改计划」「怎么修复这些问题」「制定改进方案」「这些差距怎么补」「把发现转成行动计划」。
role
by TashanGKD技术架构师角色。关键词：技术架构/系统设计/数据模型/接口规范/技术选型/API设计/数据库设计/模块划分。激活后读PRD，先输出草稿给PM确认，再下发给开发。
cognitive-consistency-check
by TashanGKD对认知结构运行完整的C1-C10一致性验证，输出验证报告并修复发现的不一致。触发词：「一致性检查」「自洽检查」「验证认知结构」「检查有没有问题」「跑一遍验证」。批量操作完成后自动触发。
cognitive-detect-contradiction
by TashanGKD检测L1文档之间或文档内部的矛盾，基于L1.5原则提出消解方案。触发词：「矛盾」「不一致」「这里有问题」「检查一致性」「[文档A]和[文档B]是否一致」「有没有冲突」「逻辑漏洞」。
cognitive-self-reflect
by TashanGKD引导用户进行自我认知反思，结构化写入自我反思记录，并与历史模式做跨时间比对。触发词：「反思」「我发现我有个习惯」「我注意到」「我又犯了」「这个模式」「我有个问题」「感觉自己」。
article-proofreading
by TashanGKD按郑总的审稿标准审阅中文文章草稿，检查AI腔、标题写法（4种错误类型）、绝对表达、结构层次污染、结语完整性，逐条给出修改方案。当用户说"审稿""帮我检查文章""review一下"时使用，也可在完成文章草稿后主动执行。
article-review-tracker
by TashanGKD追踪文章审稿意见，将编辑反馈结构化记录并逐条落实。触发词：审稿意见/修改文章/这里写得不好/审稿反馈/帮我追踪这个意见。
research-output
by TashanGKD对任何主题进行系统调研，自动产出图文并茂的 Markdown 文档（含 Mermaid 结构图 + qwen-image 可视化），保存到认知结构对应维度的知识库，并自动注册到文档分类清单和知识图谱。当用户说「帮我调研/调研一下/系统调研/研究一下/帮我系统了解/深度研究 [主题]」时触发。
cognitive-ask
by TashanGKD基于用户自己的认知文档回答问题，严格引用来源，标注置信度，暴露矛盾，识别知识空白。触发词：「基于我的文档」「根据我的想法」「我之前怎么想的」「我说过什么关于X」「我对X的看法是什么」「帮我回忆」「我的认知里怎么说」「我的框架里有没有」「从我的角度」「我如何看待」。
cognitive-reorganize
by TashanGKD从整个工作区出发，系统性地重组认知结构并整理全局文档归属。适用于首次建立认知结构、或积累了大量散落文档需要批量归档时。基于三大闭环框架（Loop1 Skill体系 / Loop2 思维体系 / Loop3 场景投射）做全工作区分类路由。触发词：「系统整理」「重组认知结构」「从文档构建认知结构」「整合文档」「全量梳理」「认知结构乱了」「全工作区整理」「文档归属梳理」「如何加入认知结构」「怎么加入认知结构」「按规范加入」「未更新的文档怎么处理」「这些文档要怎么整理」「有没有关于X的已有文档」。注意：若用户仅询问流程而非执行重组，执行 Step 0.5（检查分类清单现状）后，先向用户展示已有文档状态和分类建议，再确认是否执行完整重组。
cognitive-update-knowledge
by TashanGKD受控更新L1系统性文档：含影响范围预分析、历史版本备份、矛盾检测、级联写入。触发词：「更新[文档名]」「完善[文档名]」「修改[文档名]」「我要改[内容]」「在[文档]里加上」「[文档]需要更新」。
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.