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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hung-yi-lee
by voidfulExplain machine learning, deep learning, generative AI, LLMs, AI agents, and speech modeling in a Hung-Yi Lee-inspired teaching style. Use this skill when the user wants 李宏毅式教學: roadmap-first structure, intuition before math, black-box-to-mechanism explanations, everyday analogies, anticipating student confusion, practical debugging, and research-grounded context.
academic-research
by voidfulComplete academic research skill suite covering the full pipeline: paper reading (read/explain papers with storytelling), idea generation (brainstorm research directions), experiment design (plan experiments, ablation, baselines), proof writing (mathematical proofs, LaTeX theorems), paper writing (draft to camera-ready for top venues like NeurIPS/ICLR/ACL), paper review (structured 4-step review with scoring), and professor fit analysis (evaluate advisors, cold emails, interview strategy). Trigger keywords: read paper, brainstorm, experiment design, prove, write paper, review, professor fit, advisor, cold email, LaTeX, research, NeurIPS, ICLR, ACL, arXiv, 讀論文, 寫論文, 審稿, 實驗設計, 數學證明, 研究方向, 教授分析, 選指導教授.
professor-fit-analyzer
by voidfulanalyze a professor from google scholar, publication lists, personal websites, lab pages, and field-specific bibliographic databases (e.g., DBLP, PubMed, SSRN, PhilPapers, MathSciNet, arXiv, Scopus) to evaluate research strength, mentoring quality, collaboration network, lab resources, research taxonomy, future directions, applicant fit, outreach emails, and interview strategy. designed for students at all levels — PhD applicants, master's students, and undergraduate researchers (capstone/thesis/independent study) — across all academic disciplines. use when the user wants to assess whether a professor or lab is worth applying to, compare advisors, prepare a cold email, find a thesis or capstone advisor, infer future research openings, or build a structured dossier from public academic evidence.
idea-generation
by voidful學術研究的 Idea 產生技能——從發散到收斂，系統化地產出高品質研究構想。當使用者想腦力激盪研究方向、找新 research idea、或問「我接下來可以做什麼研究」時，一定要使用此技能。觸發詞包括：brainstorm、想 idea、研究方向、下一步做什麼、有什麼可以研究的、找 gap、research proposal。適用於任何階段的學術研究構想生成。
paper-reading
by voidful太奶讀論文 — 一位百歲阿嬤用繁體中文、生活比喻和動漫梗，帶你讀懂學術論文。當使用者提供論文 PDF、arXiv 連結、或貼上論文文字，並想理解論文內容時，一定要使用此技能。觸發詞包括：讀論文、解釋論文、看不懂、幫我理解這篇、這篇在說什麼、paper reading、explain this paper。適用於任何學術論文的直觀導讀。
paper-writing
by voidful頂級會議論文寫作技能——以嚴格 reviewer 視角指導從草稿到終稿的完整寫作流程。當使用者要寫論文、改善論文草稿、修改特定章節（introduction、method、experiments、conclusion）、潤色學術英文、回應 reviewer 意見，或問「這段怎麼寫」時，一定要使用此技能。觸發詞包括：寫論文、paper writing、improve my paper、幫我修改、review comments、rebuttal、LaTeX、NeurIPS/ICLR/ACL 投稿。適用於所有學術論文寫作場景。
paper-review
by voidful學術論文審稿技能 — 以結構化四步驟流程完成深度論文審查，涵蓋批判性審查、分數預測、要點精煉與正式審稿產出。當使用者需要 review 一篇論文、模擬 reviewer 反應、評估論文能否被接收、或幫助判斷論文優缺點時，一定要使用此技能。觸發詞包括：review 這篇、幫我審稿、reviewer 會怎麼說、這篇能上嗎、paper review、給分數、找 weakness。適用於任何學術論文的審稿模擬與評估。
proof-writer
by voidful數學證明撰寫技能 — 從主張提取到 LaTeX 排版的完整證明工作流。當使用者需要撰寫或驗證數學定理、引理、命題的形式證明，或需要推導公式、整理理論分析時，一定要使用此技能。觸發詞包括：數學證明、prove、theorem、lemma、proposition、推導、理論分析、寫 proof、LaTeX 數學、formal proof。適用於機器學習理論、統計學習理論、最佳化等需要嚴謹數學推導的場景。
experiment-design
by voidful學術研究實驗設計技能——從研究假設到可重現實驗計畫的完整流程。當使用者需要規劃實驗、設計 ablation study、選擇 baseline、確定評估指標，或問「我應該跑哪些實驗」時，一定要使用此技能。觸發詞包括：實驗設計、experiment design、ablation、baseline、跑什麼實驗、evaluation metric、如何驗證方法。適用於機器學習、NLP、CV 等領域的實驗規劃。
html-report
by voidfulGenerate a rich, INTERACTIVE HTML report (not markdown!) and publish it to GitHub Pages. Use when the user asks for a "report", "writeup", "summary page", "dashboard", "share link", or anything that benefits from interactivity, charts, sortable tables, tabs, search, syntax highlighting, math, diagrams, or visual polish that markdown cannot deliver. Returns a public URL the user can share.
mian-hun
by voidful台灣拉麵客製化推薦引擎與知識百科 — Taiwan Ramen AI Skill. 1069+ shops, 6 broth types, personalized recommendations. Triggers on: 拉麵/ramen/推薦/豚骨/雞白湯/味噌/鹽味/醬油/沾麵/ 拌麵/排隊拉麵/台北拉麵/台中拉麵/高雄拉麵/想吃拉麵/今天吃什麼/ 麵屋/湯頭/麵體/叉燒/溏心蛋 and any Taiwan ramen related query.
code-review
by voidfulUse when the user wants a code review instead of implementation. Prioritizes correctness bugs, behavioral regressions, missing tests, and risky assumptions.
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.