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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lab-report
by PiaoxuemoliWrite experiment and lab reports from source materials (lab manuals, PPT, Word, PDF). Use this skill whenever the user needs to write a lab report, experiment report, course report, 实验报告, 课程报告, or any structured academic/technical writeup based on experiment procedures. Proactively suggest this skill when the user mentions "lab report," "experiment report," "实验报告," or describes running an experiment that needs documenting.
paper-writer
by Piaoxuemoli学术论文路由器：收集需求、起 router-agent 调度 233 个专业 skills、 三代理流水线（调度→写作→导出）。主 agent 不加载任何 skill 内容。 Use when the user mentions 小论文, 课程论文, IEEE, 论文写作, 学术论文, 文献综述, literature review, paper writing, scientific paper, 研究报告.
coursework-helper
by PiaoxuemoliCreate low-friction coursework deliverables for general education, elective, and low-stakes college assignments, including PPT slides, short papers, reading reports, reflection essays, presentation scripts, discussion posts, and course summaries. Use this skill whenever the user mentions 水课, 通识课, 选修课, 小论文, 课程论文, 读书报告, 观后感, 汇报PPT, 课堂展示, 演讲稿, or asks to turn scattered course materials into a polished student-style deliverable.
study-index
by Piaoxuemoli整理课程材料为带目录索引的速查手册。适用于开卷考试复习资料、课程笔记整理、 知识收藏整理。输入多个 PPT/PDF/文档，输出结构化 Markdown（全部内容+索引）。 Use when the user mentions 开卷考试, 复习资料, 考试速查, 知识整理, 整理笔记, 资料汇总, 速查手册, or asks to organize scattered course materials into an indexed handbook for studying or open-book exams. Proactively suggest this skill when the user has multiple course files (PPTs, PDFs, documents) and needs to consolidate them for review or exam preparation.
gtars
by PiaoxuemoliHigh-performance toolkit for genomic interval analysis in Rust with Python bindings. Use when working with genomic regions, BED files, coverage tracks, overlap detection, tokenization for ML models, or fragment analysis in computational genomics and machine learning applications.
autoskill
by PiaoxuemoliObserve the user's screen via screenpipe, detect repeated research workflows, match them against existing scientific-agent-skills, and draft new skills (or composition recipes that chain existing ones) for the patterns not yet covered. Use when the user asks to analyze their recent work and propose skills based on what they actually do. Requires the screenpipe daemon (https://github.com/screenpipe/screenpipe) running locally on port 3030 — the skill has no other data source and will refuse to run if screenpipe is unreachable. All detection runs locally; only redacted cluster summaries reach the LLM.
qoobee-tf-skill
by PiaoxuemoliTest & Fix skill for ImmerseAI. Supports automated testing (AT-01~AT-14), manual test report fixing (HT-01~HT-10), and CR(Code Review) report output. Trigger with: 自动化测试, 自动测试, auto test, 人工测试, manual test, 测试报告, test report, 运行测试, run tests, CR, code review, 代码审查.
qoobee-tf-skill
by PiaoxuemoliTest & Fix skill for ImmerseAI. Supports automated testing (Agent runs code audits and auto-fixes) and manual test report fixing (Agent parses human test reports and fixes failures). Trigger with: 自动化测试, 自动测试, auto test, 人工测试, manual test, 测试报告, test report, 运行测试, run tests.
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.