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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meta-pattern-recognition
by akornmeierSpot patterns appearing in 3+ domains to find universal principles
inversion-exercise
by akornmeierFlip core assumptions to reveal hidden constraints and alternative approaches - "what if the opposite were true?"
root-cause-tracing
by akornmeierSystematically trace bugs backward through call stack to find original trigger
mongodb
by akornmeierGuide for implementing MongoDB - a document database platform with CRUD operations, aggregation pipelines, indexing, replication, sharding, search capabilities, and comprehensive security. Use when working with MongoDB databases, designing schemas, writing queries, optimizing performance, configuring deployments (Atlas/self-managed/Kubernetes), implementing security, or integrating with applications through 15+ official drivers. (project)
memory-curate
by akornmeierUse when an agent's memory file has exceeded the soft or hard character limit and needs consolidation. Runs three stages in order — dedupe, score-and-drop, summarize — and stops as soon as the file is under the soft limit. Invoked by the orchestrator when `needs_curation: true` is returned from `mcp__agent-substrate__memory_write` or `mcp__agent-substrate__memory_append`, or proactively when a `warning` is returned near the soft limit.
motion-design-skill
by akornmeierEncodes Emil Kowalski's craft sensibility for UI polish, motion, and the invisible details that make software feel great. Use whenever the user is reviewing UI code, designing or critiquing an animation, building a component (button, modal, drawer, popover, toast, tooltip), or asking "why does this feel sluggish/janky/off." Triggers on mentions of Motion.dev, motion/react, motion-v, Framer Motion, Tailwind animation utilities, transitions, easing, springs, gestures, and prefers-reduced-motion. Defaults to Tailwind utilities, Motion (motion/react for React, motion-v for Vue), and modern CSS — even when the user does not name a stack explicitly.
create-new-skills
by akornmeierCreates new Agent Skills for Claude Code following best practices and documentation. Use when the user wants to create a new skill, extend Claude's capabilities, or package domain expertise into a reusable skill.
wrap-up
by akornmeierCaptures session summaries to Pinecone (Bucket 01 — Memory). At the end of a Claude session, generates a structured summary of what was discussed, what was decided, what was built, and saves it both as a dated markdown log and as an embedded vector in the user's personal Pinecone index. Trigger when user says "wrap up", "save this session", "summarize and save", "end session", "log this", or at natural session-end moments after substantial work is done. Requires Pinecone MCP server connected, or PINECONE_API_KEY env var if using the bundled Python script. Do NOT trigger for trivial sessions or quick one-question chats — only when there's strategic substance worth recalling later.
strategy-awareness
by akornmeierA strategic governance skill — not a memory feature. Maintains a living strategy document at ~/.claude/strategy.md (or user-chosen path). Auto-fires when the conversation surfaces strategic substance — customer signals, market signals, decisions, focus shifts, wins, hypotheses, new offers, or learnings. Captures the moment to the right named section, dates it, and reflects conflicts against the user's existing Decisions and Don't-Do list. On first run, runs a 6-question setup flow to bootstrap the file. Manual triggers include "run strategy setup", "show my strategy", "show me my strategy overview", "visualize my strategy", "edit my strategy", "what's my strategy on X", and "update my strategy". When the user says "show my strategy" or "strategy overview" or asks to visually edit, generate the interactive visualization HTML and open it in the browser (see VISUALIZE mode below). Do NOT use this skill for personal preferences (oat milk, name, voice tone) — those belong in Claude's general memory or ~/.claude/CL
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.