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...
qt-qml-test-run
by TheQtCompanyRnDBuilds and runs Qt Quick Test (qmltestrunner / CTest) for a QML project, then writes a Markdown report. Use for "run qml tests", "run qmltestrunner".
qt-qml-test
by TheQtCompanyRnDGenerates Qt Quick Test cases (TestCase, SignalSpy, tryCompare) for QML components. Use for "write QML tests", "qml test", "qt quick test".
qt-ui-design
by TheQtCompanyRnDDesign or audit UI for Qt/QML, Qt projects, web, or embedded MPU or MCU targets. Use when creating screens, layouts, navigation, or auditing UX.
qt-qml-profiler
by TheQtCompanyRnDUse when the user is investigating QML / Qt Quick performance — both vague complaints ("the UI feels laggy", "this is slow", "frames are dropping", "the app stutters") and explicit asks to profile, find hotspots, or optimize bindings, signals, or rendering. Runs qmlprofiler on a 2D QML application, parses the .qtd trace, and analyzes hotspots against the source with frame-time, memory, and pixmap-cache summaries. Does NOT cover Qt Quick 3D.
qt-cpp-docs
by TheQtCompanyRnDGenerates standalone Markdown reference documentation for any Qt/C++ source files — Qt Widgets classes, Qt Quick backends, Qt/C++ modules, plain C++ utilities, structs, free-function headers, and entry points like main.cpp. Use this skill to document any .h or .cpp file: Qt classes, plain C++ code, utility helpers, or application startup files. Triggers on: "document this class", "write docs for my C++", "document main.cpp", "C++ API docs", "document my Qt app", or whenever C++ or header files are provided and documentation is needed. Works with single files, pasted code, or entire project folders. DO NOT use if the user asks for QDoc format output.
qt-cpp-review
by TheQtCompanyRnDInvoke when the user asks to review, check, audit, or look over Qt6 C++ code — or suggest before committing. Runs deterministic linting (60+ rules) then six parallel deep- analysis agents covering model contracts, ownership, threading, API correctness, error handling, and performance. Reports only high-confidence issues (>80/100) with structured mitigations. Read-only — never modifies code.
qt-qml-docs
by TheQtCompanyRnDGenerates standalone Markdown reference documentation for QML components and applications. Use this skill whenever you want to document QML files, create API reference docs for a QML component or module, document a Qt Quick application, or produce developer-facing documentation from .qml source code. Triggers on: "document this QML", "write docs for my QML", "create reference docs", "document QML component", "QML API docs", "document my Qt Quick component", "document my Qt app", or any time one or more .qml files are provided and documentation is needed. Works with single files, pasted code, or entire project folders. DO NOT use if the user asks for QDoc format output.
qt-qml-review
by TheQtCompanyRnDInvoke when the user asks to review, check, audit, or look over Qt6 QML code -- or suggest before committing. Runs deterministic linting (47+ rules) then six parallel deep- analysis agents covering bindings, layout, loaders, delegates, states, and performance. Optionally invokes system qmllint for type-level checks. Reports only high-confidence issues (>80/100) with structured mitigations. Read-only -- never modifies code.
qt-qml
by TheQtCompanyRnDApplies QML best practices when producing or working with QML source code. Use whenever QML code is the primary subject: writing, reviewing, fixing, refactoring, optimizing, or debugging QML files, components, or bindings. Do NOT trigger for purely conversational QML questions where no code is produced or examined (e.g. "explain how anchors work").
qt-figma-token-extraction
by TheQtCompanyRnDExtract design tokens, text styles, and variables from a Figma design system and produce a design-tokens.json plus ready-to-use QML singletons. Use this skill whenever someone wants to pull their design system out of Figma — whether they say "export tokens from Figma", "get design tokens", "set up my design system", "read our Figma design system", "get Figma variables into QML", "pull our color palette from Figma", "import design tokens", "extract colors/typography/spacing from Figma", or similar. Trigger this skill at the start of any design-system workflow that involves a Figma source.
qt-figma-component-generation
by TheQtCompanyRnDExtract component metadata from a Figma design system and generate production-ready QML controls. Use this skill whenever someone wants to turn Figma components into QML files — whether they say "generate components from Figma", "create QML controls based on a design system", "convert Figma components to QML", "build the component library", "extract button/input/checkbox from Figma", or anything similar. Requires design-tokens.json and QML design system singletons to already exist (from the token extraction skill). Uses Figma MCP to inspect components one at a time and maps variants, states, sizing, and token usage to idiomatic Qt Quick Controls 2 patterns. Trigger this skill at the component generation step of any QML design-system workflow.
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.