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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deep-research-plan
by MagicCubeEnter "plan mode" for a deep-research or article-writing task — search the web, fetch sources, optionally run experiments in Python/Node, design one recommended outline and research strategy, then write a plain, scannable plans/<prefix>-<short-kebab-name>.md file. No article content is drafted in this mode. Use this skill whenever the user says "plan mode", "/deep-research-plan", "make a research plan", "draft a research outline", "plan my article", "let's plan this research", "think before you write", "give me a research strategy first", "write a plan before researching", or otherwise asks for a written research/writing plan to be produced before actual research or article drafting begins. Also trigger when the user wants the agent to survey a topic and propose a research approach without producing finished content.
coding-plan
by MagicCubeEnter "plan mode" for a coding task — read the relevant code, optionally ask clarifying questions, design one recommended approach, then write a plain, scannable plans/<prefix>-<short-kebab-name>.md file. No source files are edited in this mode. Use this skill whenever the user says "plan mode", "/coding-plan", "make a plan", "draft a plan first", "give me a plan before you code", "let's plan this out", "write a plan.md", "think before you code on this one", or otherwise asks for a written implementation plan to be produced before any changes happen. Also trigger when the user wants the agent to investigate a codebase and propose an approach without touching files. Even casual phrasing like "don't rush, think it through first" should trigger this skill.
video-storyboard
by MagicCubeGenerate storyboard image boards and matching video-generation prompt scripts for specific scenes in a short video plan. Use this skill whenever the user asks to create a storyboard, storyboard image, video prompt script, scene prompt, image-to-video prompt, shot board, or per-scene video-generation package, especially when they specify a scene number, duration, or an existing video plan. This skill saves outputs as storyboard/scene-XX.png and storyboard/scene-XX.md and enforces grid sizing, timing labels, and strict character, wardrobe, prop, and location continuity.
handoff
by MagicCubeRelay the current Claude Code session to Agentara for continued execution
fix-my-life
by MagicCubeInteractive life-fixing session. Use when user says "fix my life", "life review", "I'm stuck", "help me change", or "/fix-my-life".
lab-interpreter
by MagicCubeInterpret medical lab/test reports (blood panels, urine, liver/kidney function, thyroid, tumor markers, coagulation, cardiac enzymes, hormones, etc.) uploaded as images, PDFs, or text. Trigger whenever the user uploads a lab report, medical test result, or clinical diagnostic sheet — or says things like "help me read this report", "what do these results mean", "化验单", "检验报告", "帮我看看这个报告", "blood test results", "lab results", "体检报告", "检查报告单", "血常规", "尿常规", "肝功能", "肾功能", "甲功", "凝血", "interpret my labs", "are these results normal", "这些指标正常吗". Also trigger when the user uploads ANY medical-looking document with tables of values, reference ranges, or clinical test names — even if they don't explicitly ask for interpretation. Do NOT trigger for symptom triage (use emergency-triage instead), drug interaction queries, or general medical Q&A without an actual report to interpret.
bootstrap
by MagicCubeFirst-time onboarding for new Agentara users. Use when user says "bootstrap", "/bootstrap", "get started", "first time setup", or when memory/USER.md and memory/SOUL.md are empty/missing. Inspired by the movie Her — warm, curious, subtly brilliant. The goal is to make the user feel understood within minutes and want to keep going.
claude-usage
by MagicCubeCheck current Claude usage limits (session and weekly) with ASCII progress bars. Trigger when the user asks about usage, quota, limits, rate limits, how much Claude they've used, remaining capacity, or phrases like "check usage", "usage status", "how much quota left", "am I close to the limit", "用量", "额度", "配额".
current-time
by MagicCubeGet the current date (with day of week) and time (HH:MM:SS) in a specified timezone. Auto-trigger when needing to know current time, date, or day of week — including when guessing user's location based on schedule, answering "what time is it", "what day is it", "today is what date", or any context where accurate real-time clock data is needed. Also trigger proactively at the start of conversations to ground yourself in the current time.
daily-pollen
by MagicCube花粉过敏指数权威发布与实时监控。当用户询问花粉浓度、过敏风险、花粉预警， 或设置每日定时推送花粉播报时使用。数据来源：花粉通（中国天气网 × 北京同仁医院）+ wttr.in 天气。 支持自定义城市，支持设置 cron 定时每日推送。 触发关键词：花粉、过敏、花粉指数、花粉预警、花粉浓度、今天花粉、每日花粉、 pollen、allergy、pollen alert、花粉播报、花粉监控、每天推送花粉。
weather-report
by MagicCubeProvide real-time weather forecasts for any city worldwide. Use this skill whenever the user asks about weather, temperature, or climate conditions — including phrases like "weather in [city]", "what's the weather like", "weather forecast", "is it going to rain", "how hot is it", or any mention of checking current or upcoming weather conditions for a location. Also trigger when users ask about what to wear today, whether to bring an umbrella, or any question where weather data would help.
video-plan
by MagicCubePlan short-form video, vlog, travel, lifestyle, documentary, or cinematic social video scripts by negotiating duration, choosing a story arc and visual style, then splitting the film into timed scenes of 4-15 seconds. Use this skill whenever the user asks for a video plan, short video plan, scene breakdown, montage plan, travel video script, TikTok/Reels/Shorts storyboard, or cinematic short-form narrative, even if they only describe a loose theme or destination.
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.