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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text-to-excel
by yipng05-max将结构化文本内容生成为Excel表格(.xlsx)的工具。支持解析用户提供的各种结构化文本（表格、列表、键值对、CSV、Markdown表格、JSON等），自动识别表头和数据行，生成格式规整的Excel文件。支持多sheet、合并单元格、样式设置、冻结窗格、自动筛选、条件格式、数据验证、图表生成等全部功能。当用户提到以下场景时触发：(1) 需要将文本/数据/内容转换为Excel/表格/xlsx文件 (2) 需要生成Excel表格 (3) 需要创建电子表格 (4) 提供了结构化数据并要求输出为Excel (5) 要求整理数据到表格中 (6) 需要把信息做成表格导出。关键词：Excel、表格、xlsx、电子表格、导出表格、生成表格、做成表格、转成表格、整理成表格。
analytic-memo
by yipng05-max分析备忘录（Analytical Memo）生成工具。研究者在编码过程中或编码后，直接 说出脑子里的想法——一个编码、一段资料、一个困惑、一个"这里有什么"的感觉—— skill 自动生成结构化的分析备忘录并保存为 Markdown 文件到本地。 适用于主题分析（TA）、扎根理论（GT）及一切质性研究方法。 与 memo-coach 的区别：analytic-memo 由 AI 代写分析内容； memo-coach 由研究者自己写，AI 只负责追问（专用于程序化扎根理论）。 当用户提到"写备忘录""记录分析思路""写 memo""分析笔记""帮我记下这个想法" "这个编码有点意思""这里好像有什么""这个值得记录" "这个受访者说的很奇怪"，或在编码/主题分析过程中表达任何需要捕捉的分析直觉时触发。
argument-auditor
by yipng05-max论证结构审查工具（Argument Auditor）。对学术写作中的论证单元（段落/小节/章节） 进行结构性审查，检查主张-证据-推理的完整性，识别逻辑跳跃、循环论证、 证据不足等问题，给出可操作的修改建议。 当用户说"帮我看看这段论证""这里逻辑对吗""审查一下这一节""论证有没有问题" "这个推理严密吗""帮我检查论证结构""这段话说服力够吗"时触发此 skill。 注意：这不是语言润色工具，而是专门针对论证逻辑的深度审查工具。 语言表达的问题不在本 skill 的处理范围内。
cjournal-analyzer
by yipng05-maxC刊（CSSCI来源期刊）论文全面分析工具。当用户提供一个具体的C刊期刊名称（如"管理世界"、 "社会学研究"、"经济研究"等）时，自动通过知网（CNKI）查询该期刊最近5年所有期次的文章 目录、作者和摘要信息，并生成专业的Word分析报告。报告包含：选题热点趋势、高频关键词、 研究方法偏好、核心作者群、栏目主题演变、研究空白识别、投稿方向建议等全维度分析。 触发条件：用户提到需要分析某个C刊/CSSCI期刊/核心期刊的发文趋势、选题偏好、投稿方向； 或提供中文学术期刊名称并要求查看近年发表论文的主题分布和趋势；或说"帮我分析一下XX期刊"。 注意：本skill用于期刊层面的宏观分析，不同于paper-analyzer（单篇论文拆解）和 literature-review-writer（文献综述写作）。
cnki-advanced-search
by yipng05-max知网（CNKI）高级检索论文自动化工具。当用户提供研究关键词或研究选题时，自动执行 三阶段检索：①主体联合检索（倒剥洋葱法，多组核心概念 AND 联合，获取直接相关文献）； ②独立补充检索（对各核心概念组分别单独检索，获取间接相关的背景文献与理论文献）； ③汇总合并（去重后按类别分色展示，输出统一 Excel 文件）。 触发条件：用户提到需要在知网/CNKI检索论文、高级检索、按关键词搜索CSSCI/C刊论文、 下载题录信息、获取论文摘要、按被引排序检索；或说"帮我在知网检索XX相关论文"、 "用知网高级检索搜索XX主题的C刊论文"、"帮我检索XX关键词的CSSCI论文"。
concept-clarifier
by yipng05-max概念辨析工具（Concept Clarifier）。深度解析社会学及相关学科概念的理论谱系、 内部张力、边界条件与操作化方式。区分近似概念之间的实质差异，防止概念混用。 当用户说"这个概念是什么意思""XX和XX有什么区别""这个概念能用在我的研究里吗" "这个理论的核心概念是什么""概念怎么操作化""这两个概念可以混用吗" "帮我梳理这个概念"时触发此 skill。 注意：这不是百科全书式的定义查询工具，而是帮助研究者在研究中 精准使用概念、理解概念局限、避免贴标签的深度分析工具。
conceptual-framework-builder
by yipng05-max基于 Ravitch & Riggan《理性且严谨》方法论，主动引导用户逐步完成学术研究概念框架（Conceptual Framework）的建构。 当用户提到概念框架、研究框架、理论框架建构、论文框架设计、研究方法论框架， 或者提到 Ravitch、Riggan、Reason & Rigor、理性且严谨， 或者需要为学术研究/学位论文/期刊论文建构系统性的概念框架时，使用此 skill。 即使用户只是笼统地说"帮我搭建研究框架"或"我的论文需要一个理论框架"，也应触发此 skill。
counterfactual-reasoning
by yipng05-max反事实思维工具（Counterfactual Reasoning）。对研究结论进行系统性压力测试： 枚举竞争性解释、检查选择性偏差、识别推论边界、模拟审稿人质疑。 当用户说"帮我挑战这个结论""有没有其他解释""审稿人会怎么质疑这个" "这个结论成立吗""反驳一下我的论点""还有没有别的可能""结论太强了吗" "帮我做压力测试"时触发此 skill。 注意：这不是"找不同意见"或"泼冷水"的工具，而是帮助研究者 在提交前系统性发现论证弱点、主动处理潜在质疑的防御性分析工具。
design-coherence-check
by yipng05-max研究设计内部一致性检查工具（Design Coherence Check）。系统检查研究设计在 本体论-认识论-方法论-具体方法四个层次之间的一致性，识别认识论矛盾、 方法论-方法不匹配、效度语言混用等深层问题。 当用户说"帮我检查研究设计""研究设计有没有问题""认识论和方法一致吗" "方法选对了吗""设计是否自洽""审稿人会质疑我的方法论吗" "我的方法论部分这样写合适吗""帮我审查方法章节的逻辑"时触发此 skill。 注意：这不是方法操作指导工具（具体编码/分析操作请用其他 skill）， 而是专门检查研究设计"哲学层"与"操作层"之间内在一致性的元方法工具。
feishu-paper-reviewer
by yipng05-max飞书文档论文审阅工具。直接在飞书云文档上进行学术论文审阅，支持高亮、删除线、加粗变色、划词批注、插入审阅意见等多种修订标记。当用户提到对飞书文档/云文档进行论文审阅、审稿、评阅、修改批注，或提供飞书文档链接要求审阅时触发。关键词：飞书论文审阅、飞书审稿、云文档评阅、飞书批注论文。
foreign-literature-search
by yipng05-max外文学术文献检索工具（社会科学方向）。基于 OpenAlex 开放 API（无需机构账号、无需 API key）， 执行三阶段检索：①主体联合检索（最具体组主检索+其他组后置文本过滤，实现真正 AND 语义） ②独立补充检索（研究对象背景文献 + 核心理论文献）③汇总去重分类 Excel。 同步生成 WoS/Scopus 布尔检索式供有机构权限的用户使用。 触发条件：用户提到需要检索外文/英文/SSCI文献、检索国外学术数据库、帮我搜英文文献、 foreign literature search、检索 SSCI/SCI 论文等。
grounded-coding
by yipng05-max程序化扎根理论编码（Grounded Theory Coding）工具。对访谈记录或其他质性资料进行系统化的 开放编码（识别事件→提炼类属→分析类属）、主轴编码（典范模型关系分析）、选择性编码（核心类属→研究问题→故事线→理论对话）， 每份访谈完成后自动保存为 Markdown 文件，所有访谈完成后按需生成汇总 Excel。 当用户提到需要进行扎根编码/扎根理论编码/开放编码/质性编码/grounded coding/open coding/ qualitative coding，或者提到需要对访谈资料/质性资料进行编码分析，或者上传/提供了访谈 记录的本地文件路径时触发此skill。即使用户只是笼统地说"帮我对这份访谈进行编码"、 "扎根编码分析"、"对访谈做开放编码"、"质性资料编码"、"帮我做扎根理论分析"，也应触发此skill。
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.