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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graphify
by Cap-goUse for any question about a codebase, its architecture, file relationships, or project content — especially when graphify-out/ exists, where the question should be treated as a graphify query first. Turns any input (code, docs, papers, images, videos) into a persistent knowledge graph with god nodes, community detection, and query/path/explain tools.
native-builds
by Cap-goUse when working with Capgo Cloud native iOS and Android build requests, onboarding, credential storage, credential updates, and build output upload settings.
release-management
by Cap-goUse when working on Capgo OTA release workflows including bundle uploads, compatibility checks, channel management, cleanup, and encryption key setup.
usage
by Cap-goUse when operating the Capgo CLI for app setup, OTA bundles, channels, organizations, encryption keys, account lookups, MCP integration, GitHub support commands, and native cloud builds.
capacitor-accessibility
by Cap-goAccessibility guide for Capacitor apps covering screen readers, semantic HTML, focus management, and WCAG compliance. Use this skill when users need to make their app accessible.
subscription-app-revenue
by Cap-goRevenue playbook for getting a mobile or web subscription app from zero to early MRR. Use when users ask how to make revenue, reach $1K MRR, monetize an app, get first users, improve ASO, plan TikTok/Reels/Shorts or Reddit acquisition, design a paywall, choose freemium vs trial, price subscriptions, reduce churn, or build a simple growth loop for an app.
cocoapods-to-spm
by Cap-goGuide to migrating an existing Capacitor iOS app from CocoaPods to Swift Package Manager (SPM). Use this skill when users want Capacitor 8-style SPM projects, need to run or recover from spm-migration-assistant, replace Podfile/Pods/App.xcworkspace with CapApp-SPM, add debug.xcconfig, verify plugin SPM support, or remove CocoaPods from an app project.
cordova-to-capacitor
by Cap-goComplete guide for migrating from Apache Cordova to Capacitor. Use this skill when users need to modernize a Cordova/PhoneGap app to Capacitor, migrate plugins, or understand platform differences.
ionic-enterprise-sdk-migration
by Cap-goGuides the agent through migrating Capacitor apps from Ionic Enterprise SDK plugins to Capgo and Capacitor alternatives. Covers dependency detection, API replacement, local storage changes, and platform cleanup. Do not use for generic Capacitor version upgrades or Capgo live updates.
sqlite-to-fast-sql
by Cap-goGuides the agent through migrating SQLite and SQL-style Capacitor plugins to @capgo/capacitor-fast-sql. Use when replacing bridge-based SQL plugins, adding encryption, preserving transactions, or moving key-value storage onto Fast SQL. Do not use for non-SQL storage, generic app upgrades, or plugins that already wrap Fast SQL.
webapp-to-capacitor
by Cap-goGuide for migrating an existing web app, PWA, or SPA into a store-ready Capacitor iOS and Android app. Use this skill when users want to wrap or convert a web app into a mobile app, avoid thin WebView app store rejection, add native-feeling UX, handle permissions, offline behavior, account deletion, billing, testing, and Capgo live updates.
capacitor-app-upgrade-v5-to-v6
by Cap-goGuides the agent through upgrading a Capacitor app from v5 to v6. Use when the project is on Capacitor 5 and needs the v6 migration path. Do not use for other major versions, plugin-only upgrades, or non-Capacitor apps.
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.