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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tooluniverse-gene-enrichment
by Teng-bioPerform comprehensive gene enrichment and pathway analysis using gseapy (ORA and GSEA), PANTHER, STRING, Reactome, and 40+ ToolUniverse tools. Supports GO enrichment (BP, MF, CC), KEGG, Reactome, WikiPathways, MSigDB Hallmark, and 220+ Enrichr libraries. Handles multiple ID types (gene symbols, Ensembl, Entrez, UniProt), multiple organisms (human, mouse, rat, fly, worm, yeast), customizable backgrounds, and multiple testing correction (BH, Bonferroni). Use when users ask about gene enrichment, pathway analysis, GO term enrichment, KEGG pathway analysis, GSEA, over-representation analysis, functional annotation, or gene set analysis. 中文触发词：基因富集、GO富集、KEGG、Reactome、GSEA、ORA、pathway、功能注释、gene set、通路分析。
auto-deep-research
by Teng-bioAutomatically orchestrates web search, raw web context, and deep research for requests to search, 查资料, 搜资料, 深度搜索, 调研, 文献综述, 找论文/资料/来源, look up, research, gather sources, compare current tools, or verify online claims. Use when the user asks for search/research without naming a specific search skill; it routes only through Brave spellcheck, autosuggest, web search, LLM context, and local report orchestration. 中文追加触发词：寻找参考文献、搜索文献、查参考文献、找相关论文、找几篇论文、文献搜索、论文搜索、参考文献怎么找、根据文献找来源。
bug-repro-plan
by Teng-bioCreate a minimal repeatable bug reproduction plan with environment, steps, expected vs actual behavior, and evidence checklist. Use when the user asks 复现 bug, 最小复现, reproduce issue, bug repro, 问题复现步骤, or before debugging an unclear failure.
ci-failure-triage
by Teng-bioDiagnose CI/build/test pipeline failures and separate deterministic failures from flakes. Use when the user mentions CI 失败, pipeline failed, GitHub Actions, build failed, test failed in CI, flaky CI, 构建失败, or wants CI failure triage.
config-file-explainer
by Teng-bioExplain a configuration file and its key options, risks, and safe changes. Use when the user asks 解释配置, 看一下 config, config.toml, yaml/json/toml 配置, 配置项什么意思, 哪些配置可以改, configuration file explainer.
env-and-assets-bootstrap
by Teng-bioEnvironment and assets sub-skill for README-first AI repo reproduction. Use when the task is specifically to prepare a conservative conda-first environment, checkpoint and dataset path assumptions, cache location hints, and setup notes before any run on a README-documented repository. Do not use for repo scanning, full orchestration, paper interpretation, final run reporting, or generic environment setup that is not tied to a specific reproduction target. 中文触发词：复现环境、论文复现环境、conda环境、依赖安装、checkpoint路径、dataset路径、cache目录、复现准备。
find-skills
by Teng-bioHelps users discover, compare, and install agent skills. Use when the user asks how to do X with a skill, says find a skill / is there a skill, wants tools/templates/workflows, or in Chinese asks 搜索skill, 找skill, 找一下有没有, 有没有skill, 安装skill, 管理skill, 管理 skill 库, skill库, skill 库, 补全skill生态, 自动触发skill, or wants to extend agent capabilities. In a planning-with-files ecosystem, use this as the discovery layer and record candidates/decisions in findings.md and SKILL_INVENTORY.md.
grill-me
by Teng-bioInterview the user one question at a time to clarify a plan, requirement, research design, workflow, or implementation approach before execution. Use when the user wants to discuss/完善方案/商量方案/先别写代码/先不要写代码/先定流程/先完善流程/先问清楚/先规划/需求澄清/方案评审/风险梳理/把思路问透, or when they ask to stress-test a plan without immediately doing the task.
implement-paper
by Teng-bioImplement a research paper as an interactive marimo notebook together with the user. Start by understanding what the user wants to explore, fetch the paper via alphaxiv, then build a focused notebook. 中文触发词：实现论文、论文变代码、paper to code、交互式notebook、marimo、复现方法、教学演示notebook。
literature-method-data-miner
by Teng-bioInterpret short Chinese literature-method prompts as a request to mine research methods and data from papers. Use when the user says 文献是怎么做的、这篇文献怎么做的、这些文献怎么做的、参考文献是怎么做的、文献里的方法、文献方法、参考文献的做法、从文献找科研方法、从文献收集数据、正文和附录数据、补充材料数据、根据文献找实验设计, even when papers were not provided and should be found through deep research first. Works as a planning-with-files router for literature discovery, main-text/supplement data extraction, method comparison, and idea generation.
log-summarizer
by Teng-bioSummarize noisy logs, errors, command output, or run logs into likely causes and next steps. Use when the user provides 日志, 报错, error output, traceback, CI logs, run logs, 长任务失败日志, or asks 分析日志 / 看报错 / 找失败原因.
paper-context-resolver
by Teng-bioOptional narrow helper skill for README-first AI repo reproduction. Use only when the README and repository files leave a narrow reproduction-critical gap and the task is to resolve a specific paper detail such as dataset split, preprocessing, evaluation protocol, checkpoint mapping, or runtime assumption from primary paper sources while recording conflicts. Do not use for general paper summary, repo scanning, environment setup, command execution, title-only paper lookup, or replacing README guidance by default. 中文触发词：论文复现、复现细节、数据集版本、dataset split、预处理、评估协议、checkpoint映射、runtime假设、README缺口。 中文追加触发词：参考文献的做法、论文里的实验做法、论文方法怎么复现、文献方法细节、数据集split怎么做、metric怎么做、checkpoint怎么对应。
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.