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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cli-demo-gif
by b-open-ioGenerate CLI demo GIFs using vhs (Charmbracelet). Use when creating terminal recordings for README files or documentation.
reinforce-skills
by b-open-ioInvoke this skill when: modifying any CLAUDE.md file, adding a new skill or agent to a plugin, user says 'update the skill map', 'add this to the map', 'register this agent', 'skills keep getting forgotten', 'I keep forgetting which skill to use', 'agents keep getting forgotten', 'add skill map', 'update agent map', 'sync skills to CLAUDE.md', or when setting up a new project. This skill injects compressed SKILL-MAP and AGENT-MAP directive blocks into CLAUDE.md so skill names and agent IDs persist across the session without fading from context. Skipping this means agents will forget skill names mid-session, fail to invoke the right skill, and guess at agent IDs — causing silent capability loss that is hard to diagnose.
charting
by b-open-ioFull-stack data visualization and charting intelligence. This skill should be used when the user asks to "create a chart", "visualize this data", "build a dashboard", "plot this", "graph these metrics", "show me a chart of", "make a bar chart", "create a line graph", "build a heatmap", or needs help choosing the right chart type, selecting a charting library, or engineering the data pipeline from raw database state to rendered visualization. Covers chart selection, data transformation, library choice by scale, performance optimization, and accessibility.
npm-publish
by b-open-ioThis skill should be used when the user wants to publish a package to npm, bump a version, release a new version, or mentions "npm publish", "bun publish", "version bump", or "release to npm". Handles version bumping, changelog updates, git push, npm publishing, and automatic token rotation via agent-browser when auth expires. Do not trigger for unrelated uses of "release" (e.g. GitHub releases, press releases).
runtime-context
by b-open-ioDetects agent execution environment (Claude Code, Vercel Sandbox, or local dev) and adapts behavior accordingly. This skill should be used when an agent or bot needs to understand what runtime it is in, what tools are available, or how to adapt its behavior across different execution contexts. Use this skill when building agents that may run in Claude Code as subagents AND as hosted bots in Vercel Sandboxes, or when a SOUL.md/SKILL.md needs to work across runtimes.
shaders
by b-open-ioThis skill should be used when writing custom shaders for Three.js, creating visual effects with GLSL or TSL (Three Shader Language) for WebGL and WebGPU, debugging shader issues, building post-processing pipelines, implementing noise functions, procedural textures, or custom materials. Covers shader workflow, TSL node system, GLSL patterns, debugging, performance optimization, and post-processing with pmndrs/postprocessing.
ezkl
by b-open-ioThis skill should be used when the user asks about "zero-knowledge ML", "zkML", "EZKL proofs", "prove ML inference", "verify model output", "ZK proof for machine learning", "ONNX to ZK circuit", "on-chain ML verification", "EVM verifier for ML", or needs to generate, verify, or deploy zero-knowledge proofs for machine learning models. Also use when working with @ezkljs/engine, ezkl CLI, or Lilith managed proving.
saas-launch-audit
by b-open-ioThis skill should be used when the user asks to "audit my SaaS", "check if I'm ready to launch", "review my launch checklist", "verify my pricing", "audit my payment setup", "check my AI visibility", "prepare for Product Hunt", "validate my SaaS for launch", or mentions launching a SaaS product. Provides a comprehensive, repeatable checklist with PASS/FAIL verification and actionable next steps.
x-user-timeline
by b-open-ioGet recent tweets from an X/Twitter user. Use when user asks "what has @username posted", "recent tweets from", "user's X posts", "show timeline for", "what is @user saying". Requires X_BEARER_TOKEN.
humanize
by b-open-ioInvoke this skill whenever producing text that a human will read — emails, messages, documentation, reports, blog posts, announcements, commit messages, or any prose draft. Trigger signals include: task is a writing or editing task, output will be sent or published, user says "humanize", "make this sound less AI", "de-AI this", "this sounds like ChatGPT", "make it sound more natural", or "edit this". Do not wait for the user to ask — apply this automatically before delivering any human-facing draft. Skipping this means delivering text with predictable AI patterns (filler openers, overused vocabulary, formulaic structure) that erodes trust and sounds generated.
front-desk
by b-open-ioThis skill should be used when the user asks 'who handles X?', 'what agents are available?', 'how do I contact Y?', 'team roster', 'what services do we use?', 'who should I talk to about Z?', 'what skills are available?', 'where do I find skill X?', or needs help routing to the right agent or service provider. Also use when connecting to live agent instances, checking availability, finding/installing skills, sending emails on behalf of the org, or drafting communications. Route SOC 2, audit readiness, policy drafting, and evidence-gathering questions to Anthony in product-skills, with Paul in bopen-tools for technical control validation.
ui-audio-theme
by b-open-ioGenerate cohesive UI audio themes with subtle, minimal sound effects for applications. This skill should be used when users want to create a set of coordinated interface sounds for wallet apps, dashboards, or web applications - generating sounds mapped to UI interaction constants like button clicks, notifications, and navigation transitions using ElevenLabs API.
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.