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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rust-best-practices
by loonghaoGuide 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.
qt-to-auroraview-migration
by loonghaoConvert an existing Qt/PySide/PyQt desktop project (QWebEngineView-based UI, QMainWindow browsers, Qt DCC tools) into an AuroraView project so it ships a lighter, Rust-powered WebView and gets full MCP automation for free. Use this skill whenever the user asks to "migrate", "convert", "port" a Qt/PySide/PyQt app to AuroraView, or when they want MCP-controllable Qt tooling.
rfc-creator
by loonghaoThis skill helps create RFC (Request for Comments) documents for proposing new features, architectural changes, or significant enhancements to the project. It provides templates, structure guidelines, and best practices for writing effective technical proposals. Use this skill when planning major changes that need team review and discussion.
self-improvement
by loonghaoCaptures learnings, errors, and corrections to enable continuous improvement. Use when: (1) A command or operation fails unexpectedly, (2) User corrects Claude ('No, that's wrong...', 'Actually...'), (3) User requests a capability that doesn't exist, (4) An external API or tool fails, (5) Claude realizes its knowledge is outdated or incorrect, (6) A better approach is discovered for a recurring task. Also review learnings before major tasks.
vx-best-practices
by loonghaoBest practices for using vx effectively. Use when following recommended patterns for tool management, project setup, and team workflows with vx.
vx-commands
by loonghaoComplete vx CLI command reference. Use when looking up specific vx command syntax, flags, output formats, or token-efficient forwarding. vx-native commands support --json, --toon, --compact, and --output-format; forwarded tools keep native output unless compact mode is explicitly requested.
vx-project
by loonghaoProject management guide for vx. Use when setting up a new project, configuring vx.toml, or managing project-level tool versions and scripts.
vx-troubleshooting
by loonghaoTroubleshooting guide for vx issues. Use when encountering installation failures, version conflicts, PATH issues, or other vx problems.
vx-usage
by loonghaoTeaches AI agents how to use vx, the universal dev tool manager. Use when the project has vx.toml or .vx/, or when the user mentions vx, tool version management, Git/GitHub operations, or cross-platform setup. vx auto-manages Node.js, Python, Go, Rust, and 142 providers via Starlark DSL provider.star files. Also covers MCP integration patterns and GitHub Actions.
vx-agent-workflow
by loonghaoToken-efficient command execution patterns for AI agents using vx. Use when running builds, tests, linting, GitHub operations, or any command that produces verbose output. Teaches agents to filter output cross-platform using vx-managed tools (vx rg, vx jq) instead of platform-specific syntax (Select-String, grep, findstr). Includes token measurement, savings tracking, and deep recipes for cargo, gh, pytest, and more.
vx-best-practices
by loonghaoBest practices for using vx effectively. Use when following recommended patterns for tool management, project setup, and team workflows with vx.
vx-commands
by loonghaoComplete vx CLI command reference. Use when looking up specific vx command syntax, flags, or output formats. All commands support --json for structured output and --output-format toon for token-optimized output.
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.