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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keyword-research
by EronredWhen the user wants to discover, evaluate, or prioritize App Store keywords. Also use when the user mentions "keyword research", "find keywords", "search volume", "keyword difficulty", "keyword ideas", or "what keywords should I target". For implementing keywords into metadata, see metadata-optimization. For auditing current keyword performance, see aso-audit.
ua-campaign
by EronredWhen the user wants to plan or optimize paid user acquisition campaigns. Also use when the user mentions "Apple Search Ads", "user acquisition", "paid ads", "UA", "ad campaign", "install campaign", "Facebook ads for apps", "TikTok ads", or "cost per install". For organic growth, see aso-audit. For launch-specific UA, see app-launch.
web-to-app-funnel
by EronredWhen the user wants to design or optimize the funnel that takes web visitors into installing and onboarding the app — including smart app banners, web-to-app deep links, deferred deep links, web onboarding (Stripe-paid web flow before app install), QR codes, "open in app" CTAs, and the trade-off between paying on web vs in-app. Use when the user mentions "web to app", "smart app banner", "Stripe before app", "web paywall before install", "Branch web SDK", "web funnel for app", "AppsFlyer OneLink web", "Universal Links", "App Links", "QR code to app", "open in app", "deferred deep link from web", or "should I sell on web first then push to app". For pure in-app onboarding, see onboarding-optimization. For deep link infra, see attribution-setup.
onboarding-optimization
by EronredWhen the user wants to improve their app's onboarding experience, increase activation rate, reduce Day 1 drop-off, or optimize the first-run flow. Use when the user mentions "onboarding", "first-run", "activation", "tutorial", "day 1 retention", "new user flow", "permission prompts", "sign-up conversion", "onboarding funnel", or "users dropping off early". For overall retention strategy, see retention-optimization. For paywall placement, see monetization-strategy.
localization
by EronredWhen the user wants to localize their App Store listing for international markets. Also use when the user mentions "localization", "translate my app", "international markets", "expand to new countries", "localize metadata", or "which countries should I target". For keyword research in specific markets, see keyword-research. For metadata writing, see metadata-optimization.
app-analytics
by EronredWhen the user wants to set up, interpret, or improve their app analytics and tracking. Also use when the user mentions "analytics", "tracking", "metrics", "KPIs", "App Store Connect analytics", "install tracking", "funnel", "attribution", or "how is my app performing". For A/B testing, see ab-test-store-listing. For retention metrics, see retention-optimization.
android-aso
by EronredWhen the user wants to optimize their Google Play Store listing — title, short description, full description, keywords, ratings, or Play Store-specific features. Use when the user mentions "Google Play", "Android", "Play Store", "Play Console", "short description", "full description indexed", "Google Play ASO", or wants Google Play-specific keyword, creative, or ratings strategy. For iOS App Store optimization, see aso-audit and metadata-optimization.
subscription-lifecycle
by EronredWhen the user wants to optimize their subscription business end-to-end — from trial start through renewal, cancellation, and win-back. Use when the user mentions "subscription lifecycle", "trial conversion", "churn", "cancellation", "win-back", "lapsed subscribers", "dunning", "billing retry", "grace period", "renewal rate", "subscriber LTV", or "resubscribe". For paywall design and pricing strategy, see monetization-strategy. For subscription analytics dashboards, see app-analytics.
ab-test-store-listing
by EronredWhen the user wants to A/B test App Store product page elements to improve conversion rate. Also use when the user mentions "A/B test", "product page optimization", "test my screenshots", "test my icon", "conversion rate optimization", "CPP", or "custom product pages". For screenshot design, see screenshot-optimization. For metadata optimization, see metadata-optimization.
app-clips
by EronredWhen the user wants to implement, optimize, or use App Clips for app discovery and conversion. Use when the user mentions "App Clip", "app clip code", "mini app", "instant app", "App Clip card", "App Clip link", "no download required", "instant experience", or wants to understand how App Clips appear in App Store search. For general App Store discoverability, see aso-audit. For marketing campaigns, see ua-campaign.
app-icon-optimization
by EronredWhen the user wants to design, test, or improve their app icon to increase tap-through rate and conversions in App Store search and browse. Use when the user mentions "app icon", "icon design", "icon A/B test", "icon variants", "tap-through rate", "icon conversion", "icon refresh", or wants to know what makes a good app icon. For screenshot optimization, see screenshot-optimization. For full listing A/B tests, see ab-test-store-listing.
app-launch
by EronredWhen the user wants to plan a launch strategy for a new app or major update. Also use when the user mentions "app launch", "launch plan", "launch checklist", "pre-launch", "launch day", or "how to launch my app". For ongoing ASO after launch, see aso-audit. For paid acquisition during launch, see ua-campaign.
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.