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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flutter-change-notifier
by evancaImplements state management with ChangeNotifier and Provider in Flutter. Use when setting up ChangeNotifier models, providing them to the widget tree, consuming state with Consumer or Provider.of, or optimizing rebuilds.
provider
by evancaUses the Provider package for dependency injection and state management in Flutter. Use when setting up providers, consuming state, optimizing rebuilds, using ProxyProvider, or migrating from deprecated providers.
patrol-e2e-testing
by evancaCreate, maintain, and run end-to-end tests for Flutter apps using Patrol 4.x. Use when writing E2E tests, adding integration test coverage, testing native interactions like permissions or system dialogs, capturing UI bug regressions, or validating cross-platform behavior on Android, iOS, and Web.
mocktail
by evancaUses the Mocktail package for mocking in Flutter/Dart tests. Use when creating mocks, stubbing methods, verifying interactions, registering fallback values, or deciding between mocks, fakes, and real objects.
mockito
by evancaUses the Mockito package for mocking in Flutter/Dart tests. Use when generating mocks, stubbing methods, verifying interactions, capturing arguments, or deciding between mocks, fakes, and real objects.
testing
by evancaWrite, review, and improve Flutter and Dart tests including unit tests, widget tests, and golden tests. Use when writing new tests, reviewing test quality, fixing flaky tests, adding test coverage, structuring test files, or choosing between unit and widget tests.
riverpod
by evancaUses Riverpod for state management in Flutter/Dart. Use when setting up providers, combining requests, managing state disposal, passing arguments, performing side effects, testing providers, or applying Riverpod best practices.
flutterfire-configure
by evancaSets up Firebase for Flutter apps using FlutterFire CLI. Use when adding Firebase to a Flutter project for the first time, running flutterfire configure after adding new platforms or services, initializing Firebase in main.dart, or configuring separate Firebase projects for multiple app flavors. Trigger terms: flutterfire configure, Firebase setup, Firebase initialization, firebase_core, firebase_options.dart, app flavors, multi-environment Firebase.
flutter-pre-caching
by evancaPre-caches assets, fonts, images, and startup data in Flutter and Flutter Web. Use when preloading Google Fonts, asset/network images, Lottie/Rive animations, local JSON/config files, warming up initial API data, or optimizing Flutter Web startup.
flutter-errors
by evancaDiagnoses and fixes common Flutter errors. Use when encountering layout errors (RenderFlex overflow, unbounded constraints, RenderBox not laid out), scroll errors, or setState-during-build errors.
architecture-feature-first
by evancaStructure Flutter apps using layered architecture (UI / Logic / Data) with feature-first file organization. Use when creating new features, designing the project folder structure, adding repositories, services, view models (or cubits/providers/notifiers), wiring dependency injection, or deciding which layer owns a piece of logic. State management agnostic.
bloc
by evancaImplement Flutter state management using the bloc and flutter_bloc libraries. Use when creating a new Cubit or Bloc, modeling state with sealed classes or status enums, wiring BlocBuilder/BlocListener/BlocProvider in widgets, writing bloc unit tests, refactoring state management, or deciding between Cubit and Bloc.
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.