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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publish
by inspirepanPublish a new version of klaude-code to PyPI. This skill handles version bumping, changelog updates, git tagging, and package publishing. Use when the user wants to release a new version.
scene-cinematic
by inspirepanCreate a single wide cinematic scene image with strong atmosphere, mood lighting, and aerial perspective. Use for cinematic scene, narrative illustration, atmospheric scene, wide-angle scene, sci-fi mood, blog hero, podcast wide cover, story configuration. 用于电影感、氛围图、场景插画、宽幅故事图、叙事配图、博客头图、AI/sci-fi 主题首图。Do NOT use for 干货知识卡 / 信息图 / 海报排版 / 产品图 / UI 截图 / 四象限图。
editorial-poster
by inspirepanGenerate screen-print and duotone style editorial posters for movie posters, book cover posters, concert posters, cultural commentary covers, and limited-edition art prints. Use whenever the user mentions movie poster, book cover poster, editorial poster, screen-print, silkscreen, duotone, halftone, concert poster, alternative poster art, Mondo style, or 影评海报、书评封面、电影海报、演唱会海报、文化评论封面、丝网印刷海报、双色调海报. Strong visual core is screen-print silhouettes plus duotone color blocks plus halftone grain plus typography-as-composition. Do NOT use for slide decks, flowcharts, infographics, product photography, UI screenshots, or photorealistic key art.
flat-illustration
by inspirepanGenerate layered 2.5D flat design illustrations with clean shapes, visible depth, cel-shaded planes, soft top-right lighting, balanced spacing, plain backgrounds, and restrained palettes. Use when the user asks for 扁平插画, flat illustration, layered vector illustration, Live2D-like flat character art, simple geometric character/object art, or cute clean icon-like scenes. Do NOT use for UI mockups, product ads, comics, dense infographics, cinematic scenes, realistic photography, logos, or text-heavy posters.
comic-strip
by inspirepanMulti-panel comic strip and storyboard generator. Use when the user wants to turn a story, joke, dialogue, tutorial bit, or short scene into a comic strip, manga panels, story panels, graphic novel page, storyboard, 漫画, 分镜, 四格漫画, 故事板, 短漫. Supports two output modes — single-sheet (one image with 2/3/4/6 panels) or multi-image (N sequential images linked by an anchor reference). Do not use for single cover art, infographics, posters, or single illustrations.
tech-news-cover
by inspirepanGenerate Chinese tech / finance / industry news viral cover images — high-impact 16:9 covers in the style of 中国科技媒体头图 / B 站科技区爆款缩略图 / 财经新闻视觉 / 游戏行业信息图 / YouTube thumbnail / 港媒封面 / 行业突发新闻封面. Triggers on phrases like 港媒封面、科技新闻封面、爆款封面、公众号头图、B 站缩略图、财经封面、行业大事件封面、tech news thumbnail, viral news cover, industry breaking-news cover. Signature look: dense info-graphic layout, ultra-bold Chinese headline, big highlighted numbers, news tags (突发 / 重磅 / 官方宣布), data cards with old-vs-new values, product hero, emotion-driven palette. Do NOT use for 极简 Apple 风封面 (use article-cover-image), 电影海报 (use editorial-poster), 知识科普信息图 (use knowledge-infographic), 小红书卡系列 (use xhs-card-series), 漫画分镜 (use comic-strip).
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.