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-upgrade-sdk
by freemansoftProcedure for upgrading the Flutter and Dart SDK versions across the monorepo. Covers FVM config, GitHub actions, pubspec.yaml constraints, and changelogs.
adaptive-cards-hostconfig-theme
by freemansoftHow AdaptiveCards HostConfig maps to Flutter rendering, how ReferenceResolver bridges the two, how light/dark themes are structured, and how elements read theme-aware colors, fonts, and spacing. Use this before modifying styling, colors, spacing, or HostConfig parsing in any element.
dart-monorepo-workspace
by freemansoftWorkspace layout, fvm usage, correct working directories for commands, and inter-package dependency relationships for the Flutter-AdaptiveCards monorepo. Use this whenever running flutter/dart commands or navigating the project structure.
adaptive-cards-spec-compliance
by freemansoftAdvisor skill for implementing the Adaptive Cards and Templating specifications. Provides spec references, cross-SDK behavior notes (JavaScript, Android, iOS, .NET), element/action coverage guidance, and templating language rules. Use this before implementing or reviewing any new element, action, template feature, or HostConfig behavior to ensure parity with the official specification and other SDKs.
adaptive-cards-templating
by freemansoftHow the flutter_adaptive_template_fs package implements Adaptive Card templating, its architecture, and its missing features or shortcomings.
flutter-adaptive-cards-testing
by freemansoftTesting patterns, utilities, and golden image workflows for the flutter_adaptive_cards_fs library (and related package test dirs). Use this before writing or modifying tests under packages/flutter_adaptive_cards_fs/test/ or packages/flutter_adaptive_cards_host_fs/test/.
code-review
by freemansoftQuality gate and checklist for reviewing changes in the Flutter-AdaptiveCards monorepo. Covers monorepo hygiene, AC spec compliance, theming, keys, accessibility, and testing. Use this for both manual and AI-assisted final reviews before proposing changes.
release-engineer
by freemansoftRelease engineering protocol for the Flutter-AdaptiveCards monorepo. Covers versioning, tagging, pushing packages to pub.dev, post-release minor bumps, changelog updates, and pubspec.yaml dependency sync.
adaptive-cards-dart-flutter-fvm
by freemansoftFVM rules for the Flutter-AdaptiveCards monorepo. Prefix every `flutter` and `dart` shell command with `fvm`. Use when vendored dart/flutter skills show bare commands, when installing or switching the pinned SDK, or before analyze, test, pub, or build_runner workflows.
adaptive-cards-element-registry
by freemansoftHow to implement, register, and test a new Adaptive Card element type in this project. Covers the StatefulWidget + mixin pattern, CardTypeRegistry registration, and extension API. Use this before adding any new card element.
flutter-standard-practices
by freemansoftStandard patterns and best practices for Flutter development in the FlutterAdaptiveCards project. Covers Theming (Material 3) and JSON Serialization using code-generation. Load this skill when performing detailed UI or infrastructure work.
adaptive-cards-backend-host
by freemansoftOptional flutter_adaptive_cards_host_fs package — backend invoke serialization, PlainJson/Teams adapters, AdaptiveCardBackendHandlers, and response effects. Use when wiring Submit/Execute/Refresh/onChange to a flow-service or reviewing associatedInputs + invoke round-trips.
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.