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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plan-experiment
by goodfire-aiCrystallize a research objective into a detailed experiment plan. Produces RESEARCH_OBJECTIVE.md (objective, hypotheses, success criteria) and PLAN.md (task spec, analysis DAG, sweep strategy, expected artifacts) in the active session's plan/ dir. Use this BEFORE /run-experiment whenever a new investigation starts. Input is either a free-form objective markdown blob, a path to such a file, or nothing (interactive elicitation).
paper-writing-pipeline
by GRIND-Lab-CoreFull paper writing pipeline. Orchestrates paper-plan → paper-figure-generate → paper-draft → paper-review-loop → paper-covert to go from upstream research artifacts to a polished, submission-ready manuscript package (Markdown → LaTeX → PDF + DOCX). Use when user says "write paper pipeline", "paper writing", or wants the complete paper generation workflow.
literature-review-agent
by leonardodalinkyStep 3 of the PaperOrchestra pipeline (arXiv:2604.05018). Execute the literature search strategy from outline.json — discover candidate papers via web search, verify them through Semantic Scholar (Levenshtein > 70 fuzzy title match, temporal cutoff, dedup by paperId), build a BibTeX file, and draft Introduction + Related Work using ≥90% of the verified pool. Runs in parallel with the plotting-agent. TRIGGER when the orchestrator delegates Step 3 or when the user asks to "find citations for my paper", "draft the related work", or "build the bibliography".
research-grants-ch
by Science-Discovery撰写中国科研基金申请书，覆盖国家自然科学基金（青年基金、面上项目）和中国博士后科学基金（面上资助）。适用于：(1) 撰写青年基金、面上项目、博士后基金申请书；(2) 准备立项依据、研究内容、研究基础等章节；(3) 编制项目经费预算；(4) 了解评审标准和提高竞争力。
response-to-referee
by Science-DiscoveryGenerate professional point-by-point responses to academic peer review comments and revise the manuscript accordingly. Use when the user needs to respond to referee/reviewer comments on a journal submission, conference paper, or any peer-reviewed manuscript. Combines the original paper content with reviewer feedback to produce polite, objective, evidence-based responses. Supports two-gate user approval: first for the response document, then for paper modifications. Outputs a LaTeX file using the three-part structure (The referee wrote / Our reply / Changes). Triggers: "respond to reviewer", "reply to referee", "address reviewer comments", "revision response", "rebuttal letter", "point-by-point response".
write-paper
by Science-DiscoveryWrite a complete academic paper in LaTeX format (.tex + .bib) from research project files. Use when the user asks to write, draft, or generate an academic paper, research paper, scientific paper, or LaTeX document based on project data, computational results, figures, derivations, or discussion files. Supports theoretical, computational, experimental, and review paper types with auto-detection of project structure and research type.
journal-intelligence
by eniktabFetch live submission requirements for any scientific journal before writing. Use before scientific-writing or any manuscript task: retrieves author guidelines, word limits, abstract format, figure limits, citation style, AI authorship policy (exact disclosure language), and LaTeX template via WebSearch/WebFetch. Stores results in journal_profile.yaml as the single source of truth for all formatting decisions. Covers Nature family, Cell Press, Elsevier, Wiley, Springer, PLOS, PNAS, ACS, RSC, IEEE, ACM, and any other journal.
capri-environmental
by chrispahmReference guide for CAPRI environmental indicators and emissions modeling. Use this skill when the user asks about GHG emissions, greenhouse gas, nutrient balances, NPK, nitrogen, phosphorus, potassium, energy use in agriculture, environmental indicators, IPCC methodology, manure management, fertilizer allocation, ammonia emissions, emission factors, carbon cycle, carbon balance, nitrate leaching, soil erosion, N2O, CH4, CO2 from agriculture, enteric fermentation, manure storage, manure application, over-fertilization, nutrient availability, atmospheric deposition, biological fixation, crop residues, emission intensity, tradable GHG permits, livestock density limits, nitrates directive, NEC directive, mitigation technologies, anaerobic digestion, feed additives, precision farming, or CAPDIS nitrogen disaggregation. Also trigger when exploring environmental data in CAPRI GDX files or writing scenario code that involves environmental constraints.
animal-disease-control
by taivopUse Agriculture and Food Board infectious animal disease sources for control measures, surveillance context, and operational response references.
scientific-writing
by artubssCompetência essencial para a ferramenta de pesquisa e escrita profunda. Escreva manuscritos científicos em parágrafos completos (nunca em pontos de marcação). Use processo de duas etapas: (1) criar esboços de seção com pontos-chave usando research-lookup, (2) converter para prosa fluida. Estrutura IMRAD, citações (APA/AMA/Vancouver), figuras/tabelas, diretrizes de relatório (CONSORT/STROBE/PRISMA), para artigos de pesquisa e submissões em revistas.
auto-research-writing
by deafenkenTurn a completed research run into a venue-targeted paper package with traceable claims, verified citations, reviewer-style self-critique, and publishable LaTeX artifacts. Use after auto-research-execution when `results.csv`, `results_summary.json`, and `run_report.md` exist and the user wants a full paper draft, revision loop, or negative-result framing. Read the target venue profile and use the fetched official LaTeX template first when available. Do NOT use for early-stage ideation, method design, or ad-hoc English polishing detached from experiment artifacts.
research-paper-writing
by RidenShoguneiEnd-to-end pipeline for writing ML/AI research papers — from experiment design through analysis, drafting, revision, and submission. Covers NeurIPS, ICML, ICLR, ACL, AAAI, COLM. Integrates automated experiment monitoring, statistical analysis, iterative writing, and citation verification.
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.