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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playwriter
by remorsesControl the user own Chrome browser via Playwriter extension with Playwright code snippets in a stateful local js sandbox. Use this over other Playwright MCPs to automate the browser — it connects to the user's existing Chrome instead of launching a new one. Use this cli for navigating JS-heavy websites (Instagram, Twitter, cookie/login walls, lazy-loaded UIs) instead of webfetch/curl. ALWAYS load this skill before using any playwriter commands
critique
by remorsesGit diff viewer. Renders diffs as web pages, images, and PDFs with syntax highlighting. Use this skill when working with critique for showing diffs, generating diff URLs, or selective hunk staging.
new-skill
by remorsesBest practices for creating a SKILL.md file. Covers file structure, frontmatter, writing style, and where to place skills in a repository. Use when the user wants to create a new skill, update an existing skill, write a SKILL.md, or asks how skills work.
npm-package
by remorsesOpinionated TypeScript npm package template for ESM packages. Enforces src→dist builds with tsc, strict TypeScript defaults, explicit exports, and publish-safe package metadata. Use this when creating or updating any npm package in this repo.
holocron
by remorsesHolocron is a Mintlify-compatible docs site generator and Vite plugin. Use this skill when creating, migrating, customizing, or deploying a Holocron documentation site.
tuistory
by remorsestmux for AI agents. Run dev servers and TUIs in named background sessions that agents can read, wait on, snapshot, and type into. Replaces tmux with reactive waiting instead of blind `sleep`. Projects wrap their dev script with tuistory (`"dev": "tuistory -- next dev"`) so agents get a background session and humans get auto-attached. Use tuistory when you need to: - Run background dev servers or long-lived processes - Control interactive CLIs and TUIs (type, press keys, click, wait, snapshot) - Write Playwright-style tests for terminal apps **CLI** (`tuistory`) for background sessions. **JS/TS API** (`launchTerminal`) for programmatic control and tests.
termcast
by remorsesBuild TUIs with a Raycast-like React API using termcast. Implements @raycast/api components (List, Detail, Form, Action) rendered to the terminal via opentui.
usecomputer
by remorsesDesktop automation CLI for AI agents (macOS, Linux, Windows). Screenshot, click, type, scroll, drag with native Zig backend. Use this skill when automating desktop apps with computer use models (GPT-5.4, Claude). Covers the screenshot-action feedback loop, coord-map workflow, window-scoped screenshots, and system prompts for accurate clicking.
zele
by remorseszele is a multi-account email and calendar CLI for Gmail, IMAP/SMTP (Fastmail, Outlook, any provider), and Google Calendar. It reads, searches, sends, replies, forwards, archives, stars, and trashes emails, manages drafts, labels, attachments, and Gmail filters, and creates, updates, and deletes calendar events with RSVP and free/busy support. Output is YAML so commands can be piped through yq and xargs. ALWAYS load this skill when the user asks to check email, read/send messages, reply or forward, archive or trash threads, manage drafts or labels, download attachments, schedule meetings, check their calendar, RSVP to events, or when they run any `zele` command. Load it before writing any code or shell commands that touch zele so you know the correct subcommand structure, the Google vs IMAP feature matrix, the headless login flow, and the agent-specific rules.
errore
by remorseserrore is Go-style error handling for TypeScript: return errors instead of throwing them. Instead of Go's two-value tuple (val, err), functions return a single Error | T union. Instead of checking err != nil, you check instanceof Error. TypeScript narrows the type automatically — forget to check and your code won't compile. No wrapper types, no Result monads, just unions and instanceof. The errore npm package provides helper utilities (createTaggedError, tryAsync, matchError, findCause, partition) but the core pattern is zero-dependency. Benefits: every error is visible in the return type, callers can't forget to handle errors, flat control flow with early returns instead of nested try-catch, and errors carry typed properties with cause chains for debugging. ALWAYS read this skill when a repo uses the errore "errors as values" convention (errore.org). ALWAYS use errore for new TypeScript projects.
spiceflow
by remorsesSpiceflow is a super simple, fast, and type-safe API and React Server Components framework for TypeScript. Works on Node.js, Bun, and Cloudflare Workers. Use this skill whenever working with spiceflow to get the latest docs and API reference.
lintcn
by remorsesType-aware TypeScript lint rules in .lintcn/ Go files. Only load this skill when creating, editing, or debugging rule files. To just run the linter: `npx lintcn lint` (or `--fix`, `--tsconfig <path>`). Finds .lintcn/ by walking up from cwd. First build ~30s, cached ~1s. In monorepos, run from each package folder, not the root. Warnings don't fail CI and only show for git-changed files by default. Use `--all-warnings` to see them across the entire codebase.
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.