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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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）。
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。
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。
introduction-writer
by yipng05-max定性社会学论文引言写作工具。根据研究问题、研究背景、文献空白生成引言章节全文， 符合C刊与SSCI学术规范，禁止套话，贡献表述具体指向理论对话。 当用户需要撰写论文引言、开头章节，或在 ta-research-workflow 中到达"引言写作"检查点时触发。
negative-case-finder
by yipng05-max质性研究负面案例分析工具（Negative Case Analysis）。研究者提供当前的暂定命题和 相关材料，skill 系统识别与命题不一致的案例，并将不一致区分为四种类型： 真反例、边界案例、维度差异、数据不足——分别给出命题修订方向。 当用户说"帮我找反例""挑战一下这个命题""这个结论成立吗""负面案例分析" "有没有不符合的情况""检验一下这个命题""有没有例外"时触发此 skill。 注意：这不是"找不同意见"的工具，而是用来发展更有边界感的命题的分析工具。
ta-findings-writer
by yipng05-maxTA（主题分析）研究发现章节写作工具。将主题分析的主题汇总表转化为发现叙事， 根据理论定位（A/B/C）和叙事结构（并列/串联）生成每个主题的完整分析段落， 引语服务于分析，不堆砌。 当用户需要撰写研究发现章节，或在 ta-research-workflow 中到达"研究发现写作"检查点时触发。
ta-framework-writer
by yipng05-maxTA（主题分析）理论框架章节写作工具。将理论框架建构结果转化为论文正文章节， 根据理论定位（A理论驱动/B经验驱动/C敏感性概念）自动生成对应形态的章节文字。 当用户需要将理论框架写入论文、撰写理论框架章节， 或在 ta-research-workflow 中到达"理论框架章节写作"检查点时触发。
ta-methods-writer
by yipng05-maxTA（主题分析）研究方法章节写作工具。自动读取项目目录中已有的编码文件、理论框架文档、 主题汇总等材料，推断研究设计的已知信息，仅就无法推断的细节向研究者提问， 生成研究方法章节草稿供确认修改。 当用户需要撰写研究方法章节，或在 ta-research-workflow 中到达"研究方法写作"检查点时触发。
thematic-analysis
by yipng05-max基于 Braun & Clarke 反思性主题分析框架的质性研究辅助工具。 支持两种输入模式： （1）直接提供原始访谈文本 → skill 逐份完成 TA 初始编码，汇总后进入主题识别； （2）提供已有初始编码池 → 直接进入聚类、审查、命名建议流程。 输出结构化候选主题表，明确标注边界模糊编码与待研究者裁定的命名建议。 当用户提到"主题分析"“主题编码”"帮我聚类编码""从编码提炼主题""Braun Clarke""候选主题" "这些编码怎么归主题""帮我看看主题结构""对访谈做主题分析"时触发此 skill。 注意与 grounded-coding 的区别：grounded-coding 面向程序化扎根理论的类属建构与理论关系； thematic-analysis 面向 Braun & Clarke 路线的语义主题识别，输出主题结构而非理论命题。
lit-writeup
by nealcarenDraft publication-ready Theory sections for sociology research. Guides structure, paragraph functions, sentence craft, and calibration based on analysis of 80 Social Problems/Social Forces articles.
revision-coordinator
by nealcarenOrchestrate manuscript revision by routing feedback to specialized writing skills
paper-bib
by s-choungGenerate verified BibTeX entries from DOI, title, or paper-ref output
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.