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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release
by vaayneRelease workflow for Mori macOS workspace terminal and MoriRemote iOS app. Create releases with semantic versioned tags, update changelog, and trigger automated CI/CD builds. Use when the user asks to "release", "create a release", "tag a version", "update changelog", "prepare release", "cut a release", "publish to TestFlight", or discusses versioning and release artifacts.
mori-agent-bridge
by vaayneUse Mori CLI to enable agent-to-agent communication and coordination across Mori-managed panes. Use this whenever you need to launch a helper agent, send a message to another agent (Claude, Pi, Codex), read another agent's output, or coordinate multi-agent workflows. Prefer this over ad-hoc tmux whenever the task involves mori pane operations or agent-to-agent messaging.
kreuzberg
by vaayneExtract text, tables, metadata, and images from 91+ document formats (PDF, Office, images, HTML, email, archives, academic) using Kreuzberg CLI.
spec-dev
by vaaynePlan-first development workflow. Grill the idea, write one plan.md, then implement it phase by phase. Use this skill whenever the user wants to: stress-test a design, write or review an implementation plan, add or resolve review comments, track phased implementation progress, or audit open review threads. Also use when the user says things like "let's think this through", "poke holes in this", "what am I missing", "plan this out", or wants disciplined, reviewed code changes. Flow: Grill → Plan → Impl (phase by phase).
vertex-ai-image
by vaayneGenerate, edit, or understand images using Google Gemini models. Use when asked to "generate an image", "create a picture", "edit an image", "modify this photo", "describe an image", "what's in this image", "analyze this photo", "image to text", "text to image", "explain this screenshot", "visual question answering", or any task involving image generation, image editing, or visual understanding.
humanizer
by vaayneRemove AI writing patterns from prose and make drafts sound like a specific human wrote them. Use when drafting, editing, rewriting, reviewing, or cleaning copy, essays, docs, posts, emails, UI copy, bios, reports, or any text that feels generic, polished-but-dead, promotional, formulaic, or AI-generated. Combines comprehensive AI-writing pattern detection with a strict stop-slop final pass for direct, specific, human prose.
lark
by vaayneLark/Feishu CLI skills for Anna sessions. Covers workspace operations — calendar, docs, tasks, mail, and messenger — via the lark-cli tool. In Anna, lark-cli runs under an env-var auth model with binaries resolved from $ANNA_HOME/bin: LARKSUITE_CLI_USER_ACCESS_TOKEN, LARKSUITE_CLI_APP_ID, and LARKSUITE_CLI_BRAND are injected at session start; no manual config init or auth login is needed. Always read lark-shared first for identity selection (--as user vs --as bot), scope / permission-denied handling, token refresh, and the Anna-specific rules that override upstream documentation.
mori-agent-bridge
by vaayneUse Mori CLI to enable agent-to-agent communication and coordination across Mori-managed panes. Use this whenever you need to launch a helper agent, send a message to another agent (Claude, Pi, Codex), read another agent's output, or coordinate multi-agent workflows. Prefer this over ad-hoc tmux whenever the task involves mori pane operations or agent-to-agent messaging.
openlist
by vaayneManage files on an OpenList (AList-compatible) cloud storage server. Use when the user wants to list, search, copy, move, rename, delete, upload, or download files on cloud storage managed by OpenList/AList. Triggers on phrases like "openlist", "alist", "manage cloud storage", "network drive", or file operations against a self-hosted file server. Also use when the user mentions Emby, Jellyfin, or media library organization on cloud storage.
python-script
by vaayneCreate robust Python automation with full logging and safety checks. Use when tasks need complex data processing, authenticated API work, conditional file operations, or error handling beyond simple shell commands.
refine-code
by vaayneImprove existing code at any scale — from cleaning up a single function to restructuring an entire module hierarchy. Use this skill whenever the user wants to simplify code, refactor for readability, find architectural improvements, consolidate tightly-coupled modules, deepen shallow abstractions, reduce complexity, clean up after a feature implementation, or make a codebase more testable and navigable. Triggers on phrases like 'simplify this', 'clean up', 'refactor', 'improve architecture', 'make this cleaner', 'find refactoring opportunities', 'reduce complexity', or any request to improve code quality without changing behavior.
spec-issues
by vaayneTurn conversation context into a PRD and/or break it into vertical-slice implementation issues on GitHub. Auto-detects what's needed: writes a PRD first if none exists, skips straight to issue breakdown if a plan is already present. Use this skill whenever the user wants to create a PRD, write a spec, convert a plan into issues, create implementation tickets, break down work into tasks, go from idea to trackable issues, or says things like "let's break this down", "write tickets for this", "turn this into issues", "spec this out", "make a plan", or "what are the work items". Also use when the user has a rough idea and wants it structured into actionable GitHub issues.
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.