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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apollo-client
by apollographqlGuide for building React applications with Apollo Client 4.x. Use this skill when: (1) setting up Apollo Client in a React project, (2) writing GraphQL queries or mutations with hooks, (3) configuring caching or cache policies, (4) managing local state with reactive variables, (5) troubleshooting Apollo Client errors or performance issues.
mcp-apps-sync-docs
by apollographqlSyncs MCP Apps documentation with the @apollo/client-ai-apps changelog. Always use this skill whenever: (1) the user wants to update or sync the mcp-apps-*.mdx docs based on a new release or version range, (2) phrases like "sync docs from version X", "update docs from changelog", "what changed since v0.x.x", "docs are out of date", or "check the changelog" appear in context of MCP Apps documentation, (3) checking whether mcp-apps-*.mdx files reflect recent releases. The changelog URL and doc file paths are pre-configured — the user only needs to provide the minimum version to sync from.
review
by apollographqlReview a GitHub pull request for a Rust codebase. Focuses on security, performance, test coverage, and Rust idioms. Runs in GitHub Actions and posts comments directly to the PR.
skill-creator
by apollographqlGuide for creating effective skills for Apollo GraphQL and GraphQL development. Use this skill when: (1) users want to create a new skill, (2) users want to update an existing skill, (3) users ask about skill structure or best practices, (4) users need help writing SKILL.md files.
rover
by apollographqlGuide for using Apollo Rover CLI to manage GraphQL schemas and federation. Use this skill when: (1) publishing or fetching subgraph/graph schemas, (2) composing supergraph schemas locally or via GraphOS, (3) running local supergraph development with rover dev, (4) validating schemas with check and lint commands, (5) configuring Rover authentication and environment, (6) exploring or searching a graph's schema for agent-driven discovery (rover schema describe / rover schema search).
rust-best-practices
by apollographqlGuide for writing idiomatic Rust code based on Apollo GraphQL's best practices handbook. Use this skill when: (1) writing new Rust code or functions, (2) reviewing or refactoring existing Rust code, (3) deciding between borrowing vs cloning or ownership patterns, (4) implementing error handling with Result types, (5) optimizing Rust code for performance, (6) writing tests or documentation for Rust projects.
apollo-connectors
by apollographqlGuide for integrating REST APIs into GraphQL supergraphs using Apollo Connectors with @source and @connect directives. Use this skill when the user: (1) mentions "connectors", "Apollo Connectors", or "REST Connector", (2) wants to integrate a REST API into GraphQL, (3) references @source or @connect directives, (4) works with files containing "# Note to AI Friends: This is an Apollo Connectors schema".
apollo-federation
by apollographqlGuide for authoring Apollo Federation subgraph schemas. Use this skill when: (1) creating new subgraph schemas for a federated supergraph, (2) defining or modifying entities with @key, (3) sharing types/fields across subgraphs with @shareable, (4) working with federation directives (@external, @requires, @provides, @override, @inaccessible), (5) troubleshooting composition errors, (6) any task involving federation schema design patterns.
apollo-ios
by apollographqlGuide for building Apple-platform applications with Apollo iOS, the strongly-typed GraphQL client for Swift. Use this skill when: (1) adding Apollo iOS to a Swift Package Manager or Xcode project, (2) configuring `apollo-codegen-config.json` and running code generation, (3) configuring an `ApolloClient` with auth, interceptors, and caching, (4) writing queries, mutations, or subscriptions from SwiftUI views, (5) writing tests against generated operation mocks.
apollo-kotlin
by apollographqlGuide for building applications with Apollo Kotlin, the GraphQL client library for Android and Kotlin. Use this skill when: (1) setting up Apollo Kotlin in a Gradle project for Android, Kotlin/JVM, or KMP, (2) configuring schema download and codegen for GraphQL services, (3) configuring an `ApolloClient` with auth, interceptors, and caching, (4) writing queries, mutations, or subscriptions,
apollo-mcp-server
by apollographqlGuide for using Apollo MCP Server to connect AI agents with GraphQL APIs. Use this skill when: (1) setting up or configuring Apollo MCP Server, (2) defining MCP tools from GraphQL operations, (3) using introspection tools (introspect, search, validate, execute), (4) troubleshooting MCP server connectivity or tool execution issues.
apollo-router-plugin-creator
by apollographqlGuide for writing Apollo Router native Rust plugins. Use this skill when: (1) users want to create a new router plugin, (2) users want to add service hooks (router_service, supergraph_service, execution_service, subgraph_service), (3) users want to modify an existing router plugin, (4) users need to understand router plugin patterns or the request lifecycle. (5) triggers on requests like "create a new plugin", "add a router plugin", "modify the X plugin", or "add subgraph_service hook".
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.