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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cut-release
by delexwCuts a new versioned release of claude-code-trace end-to-end without asking any questions. Detects commits since the last ancestral tag, classifies them with conventional-commit rules to pick the semver bump, bumps every version-bearing file in sync, writes the CHANGELOG entry, commits and tags on a release branch, pushes the tag to trigger the GitHub Actions release pipeline, fast-forwards `main` onto the release commit and pushes it, then cleans up the local branch. Use this skill whenever the user mentions cutting, tagging, bumping, or shipping a release — including "cut a release", "release v1.2.3", "tag and release", "bump version", "publish a release", "do a patch release", or "make a new release". Trigger even when the user does not name the version — the skill computes it. Also use proactively when the user finishes a unit of work and asks to publish it.
adr-author
by delexwGuide writing clear, enforceable Architectural Decision Records (ADRs) that work with AI-powered review systems and human teams alike. Use this skill whenever a user wants to create, improve, review, or learn about ADRs — including when they mention "architecture decision", "ADR", "document a decision", "tech decision record", or want to make an existing ADR more specific and enforceable. Also use when reviewing PRs that reference ADR compliance or when someone asks how to write decisions that automated tools can check.
animejs
by delexwWrite correct anime.js v4 animations with the right imports, API patterns, easing, timelines, and advanced features. Use this skill whenever the user asks to animate DOM elements, create CSS/SVG animations, build scroll-linked effects, add drag interactions, choreograph timeline sequences, or write any JavaScript animation code — even if they don't mention 'anime.js' by name. Also triggers when the user mentions 'animejs', 'anime.js', 'createTimeline', 'createDraggable', 'stagger', 'spring easing', or imports from the 'animejs' package. If the user wants smooth, performant web animations and isn't already using GSAP or Framer Motion, this skill applies.
inktui
by delexwBuild beautiful CLI apps with Ink (React for terminals), ink-ui components, and create-ink-app scaffolding. Use this skill whenever the user wants to build a terminal/CLI user interface with React, create interactive command-line tools, use Ink components like Box/Text, work with ink-ui widgets (Select, TextInput, Spinner, etc.), scaffold a new CLI app with create-ink-app, or write any JSX that renders to the terminal. Also triggers when the user mentions 'ink', 'ink-ui', '@inkjs/ui', 'CLI app with React', 'terminal UI', 'create-ink-app', or imports from the 'ink' package. If the user wants to build an interactive CLI and isn't already using blessed, prompts, or enquirer, this skill applies.
ladybugdb
by delexwExpert guide for LadybugDB — an embedded, in-process property graph database using openCypher. Use this skill whenever the user is working with LadybugDB, writing Cypher queries for LadybugDB, using the `lbug` CLI, importing `real_ladybug` in Python, using `@ladybugdb/core` in Node.js, or building any application with LadybugDB. Also triggers when the user asks about LadybugDB schema design, graph algorithms (PageRank, Louvain), HNSW vector search, full-text search, ATTACH to PostgreSQL/DuckDB/Delta Lake, LLM embeddings with CREATE_EMBEDDING, or bulk data import/export with COPY FROM/TO. Use even if the user just says 'ladybug graph db' or pastes a .lbug file path.
a2a-js-dev
by delexwBuild A2A (Agent-to-Agent) protocol applications using the @a2a-js/sdk TypeScript/JavaScript SDK. Use this skill whenever the user wants to create an A2A agent server, build an A2A client, implement agent-to-agent communication, set up agent discovery, handle A2A tasks/streaming, or write any TypeScript/JavaScript code that imports from @a2a-js/sdk. Also triggers when the user mentions 'A2A protocol', 'agent-to-agent', 'agent card', 'AgentExecutor', or references the a2a-js repository — even if they don't explicitly say 'A2A SDK'. Covers both server and client development, all three transport bindings (JSON-RPC, HTTP+JSON/REST, gRPC), and the full task lifecycle.
oxlint
by delexwRun and configure oxlint — the high-performance JavaScript/TypeScript linter built on the Oxc compiler stack. Use this skill whenever working in a project that has oxlint installed (check for `oxlint` in package.json devDependencies or an `.oxlintrc.json` / `oxlint.config.ts` config file). This includes when you need to lint code after making changes, fix linting errors, configure oxlint rules/plugins, set up or modify `.oxlintrc.json`, or migrate from ESLint.
tauri-v2
by delexwUse this skill whenever "tauri" appears in the user's message, or they reference tauri.conf.json, src-tauri, or @tauri-apps packages. Also use when converting a web app (React, Vue, Svelte, etc.) into a desktop application without Electron. Covers: creating Tauri v2 projects, Rust commands and IPC (invoke/events/channels), permissions and capabilities config, official Tauri plugins (fs, dialog, updater, stronghold, store, etc.), state management, window configuration, building/bundling for macOS/Windows/Linux/mobile, and debugging Tauri-specific errors like blank windows, lifetime errors in commands, or permission denied issues. Do NOT use for Electron, Flutter, Wails, or general Rust development unrelated to Tauri.
telegram-bot
by delexwInteract with Telegram — read messages from your channel and send messages back. Use this skill whenever the user mentions Telegram, wants to check messages, send to a channel, reply via Telegram, or any Telegram bot interaction — even if they just say "check Telegram", "send to channel", or "message Telegram".
oxfmt
by delexwRun and configure oxfmt — the high-performance JavaScript/TypeScript formatter built on the Oxc compiler stack, ~30x faster than Prettier. Use this skill whenever working in a project that has oxfmt installed (check for `oxfmt` in package.json devDependencies or an `.oxfmtrc.json` / `.oxfmtrc.jsonc` config file). This includes when you need to format code after making changes, set up oxfmt in a new project, migrate from Prettier, configure formatting rules, set up editor integration (VS Code, Neovim, Zed, JetBrains), or add formatting checks to CI/CD pipelines. Also use when the user mentions "oxfmt", "oxc formatter", "format with oxc", or asks about Prettier alternatives with better performance.
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.