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...
foundation-models-os27-updater
by rudrankriyamUpgrade any Apple Foundation Models Swift app or package from OS 26-era APIs to OS 27 and Xcode 27 APIs. Use when modernizing LanguageModelSession code, GenerationOptions, tool calling, context-window handling, image input, Private Cloud Compute, shared LanguageModel runtimes, reasoning controls, transcript handling, custom executors, feedback, availability gates, docs, examples, tests, or build settings.
foundation-models-app-builder
by rudrankriyamBuild or modify Apple Foundation Models features in Swift, SwiftUI, iOS, and macOS apps. Use when adding or reviewing LanguageModelSession flows, model availability checks, streaming, GenerationOptions, @Generable structured output, DynamicGenerationSchema, tool calling, RAG, voice input/output, HealthKit-backed AI, multilingual responses, App Intents, reusable capability boundaries, or production error handling.
app-intents-expert-skill
by rudrankriyamExpert App Intents guidance for building Siri, Shortcuts, Spotlight, Apple Intelligence, and interactive snippet integrations on iOS 26+. Use when implementing AppIntent, AppEntity, AppEnum, EntityQuery, AppShortcutsProvider, SnippetIntent, SiriTipView, IndexedEntity, or when making an app work with Siri, Shortcuts, Spotlight, Apple Intelligence, Visual Intelligence, Action Button, or Apple Pencil. Also use when asked about App Intents architecture, intent-driven development, or migrating from SiriKit.
gpd-cli
by rudrankriyamManage Google Play Developer Console using the gpd CLI. Use when working with Android app publishing, Play Store releases, app reviews, Android vitals, in-app purchases, subscriptions, or when the user mentions Google Play, Play Store, Android publishing, or gpd.
gpd-cli-usage
by rudrankriyamGuidance for using the Google Play Developer CLI (flags, output formats, auth, pagination). Use when asked to run or design gpd commands for Play Console workflows.
gpd-id-resolver
by rudrankriyamResolve Google Play identifiers (package, tracks, version codes, products, subscriptions) using gpd. Use when commands require IDs or exact values.
gpd-metadata-sync
by rudrankriyamSync and validate Google Play metadata, listings, and assets with gpd, including Fastlane-style workflows. Use when updating store listings or translations.
gpd-ppp-pricing
by rudrankriyamSet region-specific pricing for Google Play subscriptions and products using gpd monetization commands. Use when adjusting prices by territory or PPP strategy.
gpd-release-flow
by rudrankriyamEnd-to-end release workflows for Google Play using gpd publish commands, tracks, rollouts, and edit lifecycle. Use when uploading builds or managing releases.
gpd-submission-health
by rudrankriyamPreflight Google Play releases, validate edits, and verify listing completeness with gpd. Use when shipping to production or troubleshooting a failed release.
gpd-betagroups
by rudrankriyamOrchestrate Google Play beta testing groups and distribution using gpd. Use when managing testers, internal testing, or beta rollouts.
gpd-build-lifecycle
by rudrankriyamTrack build processing, status, and retention for Google Play using gpd publish commands. Use when waiting on processing or managing releases.
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.