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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paper-polishing
by JeanDiableComprehensive academic paper draft feedback in ICML meta-review style. Analyzes correctness (equations, proofs, notation), motivation, methodology gaps, presentation quality, visualization improvements, and missing citations. Outputs structured feedback with overall assessment, strengths, critical/major issues, section-by-section comments, and a prioritized revision checklist. Use when the user wants "feedback on my paper", "polish my draft", "review my manuscript", "what should I improve", or "help me revise".
paper-reviewing
by JeanDiableConference-style academic paper peer review. Reads a paper PDF, assesses novelty, technical soundness, clarity, significance, reproducibility, and experimental design. Generates a structured review with summary, strengths, weaknesses, major/minor issues, questions, and scores. Supports NeurIPS, ICML, CVPR, ACL, AAAI, ICCV, ICLR formats with adjustable severity. Use when the user wants to "review a paper", "write a review", "assess this submission", "what are the weaknesses", or "generate a peer review".
citation-assistant
by JeanDiableAutomatic citation insertion for LaTeX manuscripts. Parses LaTeX source to find uncited statements (factual claims, method comparisons, background assertions, dataset references, numerical claims), searches for the correct papers via Semantic Scholar and DBLP, fetches official BibTeX (never fabricates), and inserts \cite{} commands. Returns modified .tex files and updated .bib file. Inspired by github.com/ZhangNy301/citation-assistant. Use when the user says "add citations", "find references for", "cite this", "my paper needs citations", or provides LaTeX files needing references.
homework-machine
by JeanDiableEnd-to-end university assignment completion. Analyzes assignment requirements (PDF or text), conducts research, writes implementation code with tests, drafts an academic report (LaTeX or Word) with accurate citations, and performs automatic paraphrasing for anti-plagiarism via translation round-trip (macOS only). Use when the user provides a homework assignment, coursework, lab report requirements, or says "complete this assignment", "do my homework", "write this report", "finish this coursework".
idea-to-proposal
by JeanDiableMulti-dimensional research idea evaluation with iterative refinement and publication-quality proposal generation. Takes any research idea (free text, structured brief, paper extension, or file path), scores it across 8 dimensions (novelty, technical soundness, feasibility, significance, clarity, experimental validity, scalability, positioning), refines autonomously for 2-3 rounds, then generates a detailed proposal with method design (architecture diagrams, equations, pseudocode), experiment plan (benchmarks, baselines, ablations, supplementary experiments), and qualitative/quantitative analysis plans. Use when the user wants to "evaluate my idea", "research proposal", "refine this idea", "idea to proposal", "score my research idea", or "turn this idea into a paper plan".
paper-triggered-survey
by JeanDiablePaper-triggered literature survey and idea generation. Given a PDF, arXiv URL, or tweet containing a paper reference, analyzes the paper, searches for related work (including papers the input may have missed), performs cross-domain exploration, and proposes 2-3 innovation directions. Use when the user provides a specific paper and wants to understand its landscape, find extensions, or generate research ideas. Also triggers for "survey this paper", "what's related to this", "extend this work".
survey-writing
by JeanDiableTaxonomy-focused literature review and survey paper writing. Given a research field or topic, searches comprehensively for papers, builds a multi-level taxonomy, and drafts a complete survey with introduction, background, per-category deep dives, comparison tables, and future directions. Unlike literature-survey, this skill focuses purely on organizing existing work — no cross-domain exploration or idea generation. Use when the user wants to "write a survey", "create a literature review", "organize papers into a taxonomy", or "draft a related work section".
rebuttal
by JeanDiableRebuttal pipeline for conference paper reviews. Parses reviewer feedback, classifies concerns by severity/type, builds a per-reviewer response strategy, and drafts a venue-compliant rebuttal with placeholders for pending experiments. Supports follow-up rounds. Use when user says "rebuttal", "reply to reviewers", "respond to reviews", "rebuttal draft", or wants to answer reviewer comments for a conference submission.
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.