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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sci-paper-reviewer
by aipochSimulates a strict SCI peer-review workflow; trigger when a user uploads or pastes a manuscript (PDF/DOC/DOCX/TXT) and requests an innovation score (1–12) plus experimental-logic vulnerability checks and revision suggestions.
aris-research-lit
by OpenLAIRSearch and analyze research papers, find related work, summarize key ideas. Use when user says "find papers", "related work", "literature review", "what does this paper say", or needs to understand academic papers.
academic-writing
by ZimoLiaoUse when the user needs help choosing or organizing an academic-writing workflow by deliverable, stage, or format, including review articles, guided reading, paper sections, PPT or slides, posters, and technical reports.
deep-research
by allenhutchisonConduct comprehensive, multi-source research and generate cited reports. Activate this skill when users want in-depth research on a topic, need synthesis across web and vault sources, or want a structured research report saved to their vault.
research
by breadboard-aiUplevel your researching abilities and learn how to research properly.
citation-management
by mkurmanComprehensive citation management for academic research. Search Google Scholar and PubMed for papers, extract accurate metadata, validate citations, and generate properly formatted BibTeX entries. This skill should be used when you need to find papers, verify citation information, convert DOIs to BibTeX, or ensure reference accuracy in scientific writing.
content-generation
by TFboy1基于代码仓库、笔记、实验数据或论文要求，全自动智能撰写学术论文初稿的主线管线。强制分章逐批检索代码、分步输出，内置规避上下文超限机制和人工审核卡点，无缝衔接格式化引擎。内置严格的学术 Prompt 准则与多模态图表检索能力。
grounded-theory-guide
by wentoraiApply grounded theory methodology to develop theory from data
paper-critique-framework
by wentoraiStructured framework for writing peer review reports and paper critiques
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。
grad-narrative
by asgard-ai-platformApply narrative research methods to understand human experience through stories, analyzing narrative structure, temporality, and meaning-making in life stories and oral histories. Use this skill when the user needs to analyze how people construct meaning through storytelling, examine narrative structure and plot, conduct life story or oral history research, or when they ask 'how do stories shape identity', 'how do I analyze a life narrative', or 'what does this story reveal about experience'.
grad-ant
by asgard-ai-platformApply Actor-Network Theory (Latour, Callon) to trace how human and non-human actors (actants) form networks through translation processes. Use this skill when the user needs to map sociotechnical assemblages, analyze how innovations stabilize or fail through network-building, trace the four moments of translation (problematization, interessement, enrollment, mobilization), or when they ask 'how did this technology become accepted', 'who and what holds this network together', or 'why did this innovation fail to gain traction'.
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.