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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motion-framer
by edobryModern animation library for React and JavaScript (Motion, formerly Framer Motion). Create production-ready animations with motion components, variants, gestures (hover/tap/drag), layout animations, AnimatePresence exit animations, spring physics, and scroll-based effects. Use when building interactive UI components, micro-interactions, page transitions, scroll-triggered reveals, or complex animation sequences. Vendored from freshtechbro/claudedesignskills 2026-05-19.
incident-memo
by edobrySynthesis-level postmortem for a multi-incident working session. Triggered when the session has produced 2 or more retrospectives, merged 3 or more PRs, or the user explicitly asks for an incident memo / day synthesis / meta-retrospective. Distinct from the retrospective skill (per-incident); this skill operates at the cross-incident layer and produces a Notion page + companion memory entry.
incident-memo
by edobrySynthesis-level postmortem for a multi-incident working session. Triggered when the session has produced 2 or more retrospectives, merged 3 or more PRs, or the user explicitly asks for an incident memo / day synthesis / meta-retrospective. Distinct from the retrospective skill (per-incident); this skill operates at the cross-incident layer and produces a Notion page + companion memory entry.
develop-codemod
by edobryDevelop safe, effective codemods using AST-based approaches with mandatory boundary validation. Use when writing a codemod, migrating patterns, automating code transformations, or fixing import paths across the codebase.
develop-codemod
by edobryDevelop safe, effective codemods using AST-based approaches with mandatory boundary validation. Use when writing a codemod, migrating patterns, automating code transformations, or fixing import paths across the codebase.
classify-before-deferring
by edobryBefore ending a turn with a question to the user, OR writing a deferred-action recommendation ("I'll file that as a follow-up," "out of scope to address right now," "worth filing separately," "let's track that," "parking lot this," "circle back later," "flag as follow-up") without doing the action in the same turn, classify the draft as Class A (verifiable by lookup), Class B (default already clear from CLAUDE.md / user-preference rule), Class C (genuinely ambiguous principal-stakes choice), or R3 (recommending-instead-of-acting). Act on the classification instead of writing the draft. Use whenever a draft question, "want me to X or Y," "should I," "I'd file that," "we should investigate," "not in scope for current PR but," "for now leaving X as," "noting for later," or any deferral-shaped prose is about to land — OR when a process checklist or gate step asks you to "state a strategy/decision/plan" (a checklist-manufactured trigger that does not read as a deferral).
classify-before-deferring
by edobryBefore ending a turn with a question to the user, OR writing a deferred-action recommendation ("I'll file that as a follow-up," "out of scope to address right now," "worth filing separately," "let's track that," "parking lot this," "circle back later," "flag as follow-up") without doing the action in the same turn, classify the draft as Class A (verifiable by lookup), Class B (default already clear from CLAUDE.md / user-preference rule), Class C (genuinely ambiguous principal-stakes choice), or R3 (recommending-instead-of-acting). Act on the classification instead of writing the draft. Use whenever a draft question, "want me to X or Y," "should I," "I'd file that," "we should investigate," "not in scope for current PR but," "for now leaving X as," "noting for later," or any deferral-shaped prose is about to land — OR when a process checklist or gate step asks you to "state a strategy/decision/plan" (a checklist-manufactured trigger that does not read as a deferral).
loop
by edobryRun a prompt or slash command on a recurring interval (e.g. /loop 5m /foo). Omit the interval to let the model self-pace. When the user wants to set up a recurring task, poll for status, or run something repeatedly on an interval (e.g. "check the deploy every 5 minutes", "keep running /babysit-prs"). Do NOT invoke for one-off tasks.
loop
by edobryRun a prompt or slash command on a recurring interval (e.g. /loop 5m /foo). Omit the interval to let the model self-pace. When the user wants to set up a recurring task, poll for status, or run something repeatedly on an interval (e.g. "check the deploy every 5 minutes", "keep running /babysit-prs"). Do NOT invoke for one-off tasks.
pz-voice
by edobryWrite in the principal's literary voice — the corpus-grounded register Eugene developed across the pee_zombie Twitter corpus (2020-2025). Terse, semicolon-heavy, technically loaded, presupposing reader literacy, operative-ontology-grounded (causation > description; process > object; agency > capability; structural > behavioral). Use when drafting any Minsky-voice prose surface: position papers, RFCs, manifestos, About pages, blog posts, marketing-site copy, or any artifact that should sound like Eugene wrote it. Composes with the cultural-code architecture (GitS / Eva / Iso / Magilumiere / Macx) in marketing-site-design / minsky-brand — pz-voice is the *signal* (substance); cultural codes are the *channel* (audience-facing register). Together: voice carries WHAT is claimed; codes carry HOW it feels.
pz-voice
by edobryWrite in the principal's literary voice — the corpus-grounded register Eugene developed across the pee_zombie Twitter corpus (2020-2025). Terse, semicolon-heavy, technically loaded, presupposing reader literacy, operative-ontology-grounded (causation > description; process > object; agency > capability; structural > behavioral). Use when drafting any Minsky-voice prose surface: position papers, RFCs, manifestos, About pages, blog posts, marketing-site copy, or any artifact that should sound like Eugene wrote it. Composes with the cultural-code architecture (GitS / Eva / Iso / Magilumiere / Macx) in marketing-site-design / minsky-brand — pz-voice is the *signal* (substance); cultural codes are the *channel* (audience-facing register). Together: voice carries WHAT is claimed; codes carry HOW it feels.
tailwind-v4-shadcn
by edobry| Production-tested setup for Tailwind CSS v4 with shadcn/ui, Vite, and React. Use when: initializing React projects with Tailwind v4, setting up shadcn/ui, implementing dark mode, debugging CSS variable issues, fixing theme switching, migrating from Tailwind v3, or encountering color/theming problems. Covers: @theme inline pattern, CSS variable architecture, dark mode with ThemeProvider, component composition, vite.config setup, common v4 gotchas, and production-tested patterns.
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.