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...
ui-features
by marcusImplementing UI/UX features in sidecar including modals (internal/modal library), keyboard shortcuts, mouse support, scrolling, pill/tab rendering, and pane resizing. Use when implementing UI features, handling user input, adding keyboard shortcuts, building modals, or working on UX improvements.
keyboard-shortcuts
by marcusReference for keyboard shortcut implementation, keybinding registration, shortcut parity with vim and other TUI tools, and the complete shortcut assignment table across all sidecar plugins. Use when adding or modifying keyboard shortcuts, checking shortcut assignments, resolving key conflicts, or assessing alignment with vim conventions.
sidecar-website
by marcusWriting and maintaining the Sidecar Docusaurus documentation site, including page structure, doc authoring, blog posts, styling, images, and deployment workflow. Use when writing documentation, updating the docs site, adding pages or blog posts, or working with Docusaurus configuration.
create-modal
by marcusCreate declarative modals using the modal library API. Covers modal types (confirm, input, select, form), sections (Text, Buttons, Input, Textarea, Checkbox, List, When, Custom), rendering with OverlayModal, and keyboard/mouse handling. Use when adding modals or dialogs to the application.
create-theme
by marcusCreate custom color themes for Sidecar, including base theme selection, color overrides, gradient borders, tab styles, per-project themes, community themes, and programmatic theme registration. Use when creating or modifying themes, adjusting UI appearance, or debugging color/style issues. See references/palette-reference.md for the full color palette with all keys and per-theme values.
drag-pane
by marcusDrag-and-drop pane resizing implementation for two-pane plugin layouts. Covers mouse event handling via the internal/mouse package, hit region registration, drag delta calculation, width clamping, state persistence, and pane layout management. Use when working on pane resizing, drag interactions, layout management, or adding drag-to-resize to a new plugin.
profile-memory
by marcusProfile memory usage in sidecar using Go pprof, system tools, and heap analysis. Covers identifying memory leaks, goroutine leaks, file descriptor accumulation, and CPU profiling. Use when investigating memory issues, profiling performance, debugging memory leaks, or diagnosing unresponsive plugins.
shell-integration
by marcusInteractive shell/TTY integration with tmux session management, shell command execution, and output capture in sidecar. Covers the tty package, key mapping, adaptive polling, cursor rendering, scrolling, paste handling, and inline editing. Use when working on shell integration, tmux features, command execution, or interactive mode.
project-switching
by marcusProject switching implementation in sidecar: project discovery, state management, UI flow, modal rendering, filtering, theme preview, and plugin reinitialization. Use when working on the project switcher feature, project management, worktree switching, or the project configuration system.
release-sidecar
by marcusRelease new versions of sidecar. Covers version tagging with semver, td dependency updates, go.mod validation, CHANGELOG updates, GoReleaser automation, Homebrew tap updates, and verification steps. Use when preparing or executing a release.
create-adapter
by marcusCreate conversation adapters for importing AI chat history from different tools (Claude Code, Cursor, Warp, Codex, etc.). Covers the adapter.Adapter interface, caching strategies, incremental parsing, watch/FD management, and performance standards. Use when creating a new adapter, modifying adapter behavior, or debugging adapter performance issues. See references/ for Cursor DB and Warp SQLite schema details.
create-plugin
by marcusCreate new sidecar plugins implementing the plugin.Plugin interface, rendering views with Bubble Tea, handling keyboard input via keymap contexts, and integrating with the app shell (footer hints, event bus, adapters). Use when creating a new plugin, modifying plugin architecture, or debugging plugin rendering/lifecycle issues. See references/ for sidebar list and fixed footer layout details.
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.