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...
sync-upstream-feature
by InneiUse when porting a feature from the closed-source Shiroi repo to the open-source Shiro repo. Triggers on "sync from upstream", "port feature from Shiroi", "bring X from Shiroi to Shiro", or any task requiring code migration between the two repos.
yohaku-design
by InneiBuild new Yohaku-style UI: HTML mockups, React components, or mockup→React handoff. Triggers on "make a Yohaku mockup / design a new component / add a hero / modal / sheet / convert mockup to React / audit token compliance / 做一个 Yohaku 风的 mockup / 设计一个新组件 / mockup 转 React / 检查 token 合规".
generate-design-md
by InneiGenerate a DESIGN.md file for any brand or website by analyzing its visual design system. Use when the user asks to "generate a DESIGN.md for [brand/URL]", "create a design system doc for [site]", "extract the design tokens from [URL]", or "make a DESIGN.md like [brand]". Triggers on any request to document a website's design language in DESIGN.md format.
slack
by InneiUse when AI encounters Slack permalinks (https://*.slack.com/archives/...), Slack IDs (C.../U.../D.../F...), thread 讨论上下文, 搜索 Slack 历史消息, 按邮箱/handle 反查用户, 浏览频道最近动态, 下载 Slack 附件（截图/日志/CSV/PDF), 或需要把 @handle / #name / ^subteam 解析成 ID 时.
writing-live-e2e-tests
by InneiUse when writing, modifying, or running live E2E test scenarios in src/e2e/live/ for the kagura project. Triggers on Slack bot integration testing, live scenario creation, Codex/Claude provider live tests, status probe assertions, database assertions, and requests to run or debug live E2E tests.
session-handoff
by InneiProduce a self-contained handoff prompt for another agent (Codex, a fresh Claude session, a teammate) when the user wants to delegate continued work. Triggers on: "写一个 prompt 给 codex"、"交接一下"、"让 xxx 跟进"、"summary 一下再写个 prompt"、"handoff to another agent"、"write a prompt so X can continue", or any request to capture the current session state for continuation elsewhere.
session-to-skill-and-blog
by InneiTurn a completed non-trivial engineering session into a paired durable artifact: (1) a reusable skill under Innei's personal SKILL repo, and (2) a published blog post that narrates the journey and embeds the skill URL. Triggers on "把这个过程写成 skill 再写一篇 blog"、"沉淀一下这次的折腾"、 "productize this session"、"publish this as a skill and a writeup".
working-summary
by InneiUse when the user asks for a work summary, weekly report, 工作总结，周报，working summary, or sprint/period recap. Aggregates GitHub PR/commit/issue activity from configured repos, optionally reads Linear cycle issues via MCP, honors Chinese public holidays, and produces a markdown report. Default range is the previous Mon-Sun week.
acg-character-settei
by InneiUse when generating an ACG (anime/manga) character settei sheet (キャラ設定資料) — multi-view full-body lineup, expression list, detail callouts, color palette — from a template settei image plus a single character reference image. Built on top of gemini-image-generation; this skill encodes the layout-locking + identity-locking prompt pattern that reliably produces settei-style output.
chibi-sticker-sheet
by InneiUse when generating a LINE/WeChat-style chibi sticker sheet (4x8 grid, 32 expressions) from an anime character reference image via Gemini, including transparent PNG output and individual cell slicing.
gemini-image-generation
by InneiUse when a task requires Gemini text-to-image or image-to-image generation, including style transfer, character consistency, reference-image workflows, and watermark removal from Gemini-sourced images.
gemini-seo-image-assets
by InneiUse when a web project needs Google AI Studio or Gemini-generated favicon and Open Graph images, exported icon variants, and matching SEO metadata wiring.
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.