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.
Querying local SQLite index...
literature-review
by zLanqingConduct comprehensive, systematic literature reviews using multiple academic databases (PubMed, arXiv, bioRxiv, Semantic Scholar, etc.). This skill should be used when conducting systematic literature reviews, meta-analyses, research synthesis, or comprehensive literature searches across biomedical, scientific, and technical domains. Creates professionally formatted markdown documents and PDFs with verified citations in multiple citation styles (APA, Nature, Vancouver, etc.).
generate-idea
by GRIND-Lab-CoreGenerate and rank research ideas given a broad direction. Use when user input "brainstorm ideas", "generate research ideas", "what can we work on", or wants to explore a research area for publishable directions.
generate-report
by GRIND-Lab-CoreConsolidates literature review, idea discovery, refined proposal, experiment plan, experiment results, and automated review into a single NARRATIVE_REPORT.md that is rich enough to drive the downstream paper-writing-pipeline (paper-plan → paper-figure-generate → paper-draft → paper-review-loop → paper-convert).
paper-draft
by GRIND-Lab-CoreTransforms output/PAPER_PLAN.md into a journal-quality Markdown manuscript draft for GIScience, GeoAI, spatial data science, and remote sensing venues (IJGIS, ISPRS JPRS, RSE, TGIS, AAG Annals). Consults referenced literature, experiment, figure, and claim artifacts; supports full drafts, partial drafts, and skeleton drafts depending on readiness. Never fabricates results, metrics, or citations — produces a claim-to-evidence map and coverage-gap report alongside the manuscript.
paper-review-loop
by GRIND-Lab-CoreReviews the manuscript produced by `paper-draft` (in `output/manuscript/`) as a demanding IJGIS / ISPRS JPRS reviewer-editor, cross-checks it against `output/PAPER_PLAN.md` and its evidence artifacts, then revises it into a stronger draft. Produces a reviewed manuscript, a revised manuscript, a structured review report, a prioritized issue log, a revision log, claim-risk notes, journal-fit notes, and next-loop priorities. Supports full, section-scoped, and mode-scoped review (structural / argument / novelty / methods / results-discussion / journal-fit / language / integrated). Safe on partial or skeletal drafts. Never fabricates results, citations, or figures.
research-refine
by GRIND-Lab-CoreTurn a vague research direction into a problem-anchored, elegant, frontier-aware, implementation-oriented method plan via iterative GPT-5.4 review. Use when the user says "refine my approach", "decompose this problem", "refine research plan", or wants a concrete research method that stays simple, focused, and top-venue ready instead of a vague or overbuilt idea.
research-mission-generator
by TibsfoxPackage conversation research into a GSD-ready mission package. Produces a three-stage Vision → Research → Mission pipeline as a LaTeX PDF following GSD/NASA SE methodology, designed to be handed to gsd-skill-creator for execution. Use this skill when the user has been discussing, researching, or brainstorming a topic and then asks to turn it into a research mission, research pack, mission package, or says 'package this as a mission', 'make this a research pack', 'turn this into a mission for skill-creator', 'create a research mission from this', or 'use the research mission skill'. The skill harvests findings, sources, and structure from the current conversation and any prior research, then produces the complete pipeline document. Also trigger if the user asks to 'create a research mission on [topic]' cold — in that case, conduct web research first, then package.
scientific-paper-writer
by eniktabFull-lifecycle scientific paper writing assistant. Fetches live journal guidelines, AI authorship policy, word limits, citation format, and LaTeX template for any journal before writing. Audits every draft for 25 AI writing patterns. Verifies every citation via web search. Produces publication-ready prose for Nature, Science, Cell, PNAS, NeurIPS, and any other venue.
peer-review
by LogauaEngstromSystematic peer review toolkit. Evaluate methodology, statistics, design, reproducibility, ethics, figure integrity, reporting standards, for manuscript and grant review across disciplines.
timing-opportunity
by kangarooking用户需要决定"何时做"而非"做什么"时; 当时机选择影响成败时; 当需要在"立即行动"和"等待更好时机"之间选择时; 当需要识别"不可干预"的危险窗口时。 不适用于: 时机不影响结果的场景、纯方案设计不需要时间维度的场景。
generate-questions
by MadGraphTeamGenerate evaluation questions about MadGraph and related tools with verified reference answers, using web research for real-world use cases.
geology-academic-scientific-editor
by gabrielmoreiraExpert academic editor specializing in geology. Refines text for grammar, clarity, and formal scientific tone while preserving specific geological terminology and citation integrity. Capable of synthesizing sources, generating rephrasing alternatives, and ensuring structural coherence.
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.