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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add-monitor-theme
by Nevo24Guide for adding a new visual theme to the Leap Monitor GUI. Covers the Theme dataclass, required color properties, contrast safety, and theme-manager registration. Use when adding or editing a monitor color theme.
add-client-command
by Nevo24Checklist for adding a new client command (for example !mycmd) to the Leap interactive client. Lists every location to update so the command handler, help text, and dispatcher stay in sync. Use when adding or modifying a Leap client command.
add-cli-provider
by Nevo24Step-by-step guide for adding a new AI CLI backend (provider) to Leap, such as a new coding assistant. Covers the CLIProvider Strategy pattern, state detection, input protocol, menu handling, configure_hooks and hooks_installed, and registry wiring, including custom CLI variants of the five base CLIs. Use when adding, implementing, or registering a new CLI provider.
create-macos-icon
by Nevo24How to convert a PNG image into a proper macOS .icns icon bundle for Leap Monitor. Use when creating or updating the application icon or the .icns asset.
codebase-map
by Nevo24Detailed map of the Leap codebase - the full src/ directory tree annotated with each module and script's role, plus the key-classes reference table mapping every important class or function to its file and purpose. Use this to locate a file, class, module, or helper, or to understand where functionality lives in Leap.
monitor-pr-tracking
by Nevo24Internals of the Leap Monitor SCM/PR-tracking subsystem and session table - GitLab/GitHub/Bitbucket polling and timeouts, PR status markers and merged/closed badges, sending PR comments, /leap auto-fetch, environment-variable tokens, GitHub Enterprise URL handling, the Bitbucket Cloud/Server dual-API provider, user notifications, persistent and pinned rows, the managed-clone dirty-tree sync dialog, the Add-Row flows, branch-mismatch and startup validation, and session-table UX (row ordering, row colors, tag aliases, live filter). Use this when working on monitor PR tracking, SCM polling, or session-table behavior.
monitor-code-signing
by Nevo24How Leap Monitor.app is code-signed with the per-user self-signed Leap Self-Signed certificate so macOS Accessibility and Notification grants survive every update, plus the related build, TCC, and Apple Silicon architecture troubleshooting. Use this when working on the Makefile signing steps, py2app builds, TCC/Accessibility persistence, or architecture-mismatch and signing failures.
cursor-editor-agent-tabs
by Nevo24How the Leap Monitor shows read-only rows for open Cursor (the editor) Agent/Composer tabs - the on-disk SQLite scan (scan_open_cursor_agents), status mapping, tab-level focus/jump via the Cursor extension (focus_cursor_window), synthetic row reconciliation, and the two close buttons. Use this when working on cursor_gui_scan.py, Cursor GUI agent rows, or Cursor tab navigation.
auto-approve-architecture
by Nevo24Internals of Leap's Claude auto-approve flow and the CLI state machine (CLIStateTracker)- the PermissionRequest hook, the AskUserQuestion exclusion, per-session auto_send_mode isolation and pin-file robustness, up/down arrow handling during dialogs and slash-command pickers, the on-input no-reset rule, and the TUI-menu fallback. Use this when modifying auto-approve behavior, the state tracker, hook handling in leap-hook-process.py, or Claude permission/dialog detection.
add-dialog
by Nevo24Guide for adding a new dialog or window to the Leap Monitor GUI. Covers ZoomMixin font-zoom setup, dialog geometry persistence, theme integration, the Cancel-bottom-left button-row convention, and the prefs persistence model that ad-hoc dialogs tend to get wrong. Use when creating a new monitor QDialog or window.
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.