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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pneuma-kami
by pandazkiPaper-canvas web design. Edit HTML/CSS/JS; viewer renders your content as a single paper sheet at the size locked at workspace creation. Design language adapted from tw93/kami (MIT). Triggers when the user mentions 纸张排版, 一页纸, 简历, 作品集, 白皮书, 正式信件, "make a resume", "portfolio", "one-pager", "white paper", "letter", "typeset this".
pneuma-draw
by pandazkiPneuma Draw Mode workspace guidelines. Use for ANY task in this workspace: creating or editing diagrams, flowcharts, wireframes, mind maps, architecture diagrams, org charts, sketches, or any visual content on the Excalidraw canvas. This skill defines the Excalidraw JSON format, element types, binding rules, and color palette. Consult before your first edit in a new conversation.
pneuma-doc
by pandazkiPneuma Doc Mode workspace guidelines. Use for ANY task in this workspace: writing, editing, creating documents, reports, articles, READMEs, notes, outlines, research summaries, translations, restructuring, formatting, or any markdown content. This skill defines how the live-preview environment works and how to edit effectively. Consult before your first edit in a new conversation.
pneuma-preferences
by pandazkiPersistent user preference memory across sessions. Consult this skill BEFORE making any design, style, or aesthetic decisions — choosing colors, themes, layouts, fonts, tone of voice, content density, or visual direction. Also consult when starting a new creative task in any mode, when the user corrects your style choices, or when asked to analyze or refresh user preferences. Even if you think you know what to do, check preferences first — the user may have recorded specific constraints.
obsidian-knowledge
by pandazki{{customDescription}}
pneuma-slide
by pandazkiPneuma Slide Mode workspace guidelines. Use for ANY task in this workspace: creating or editing presentations, slide decks, pitch decks, adding or modifying slides, changing themes, layouts, or any presentation content. This skill defines the design workflow, height calculation rules, layout patterns, and quality checklist for the fixed-viewport slide environment. Consult before your first edit in a new conversation.
pneuma-modename
by pandazkiTODO: Describe what this mode's agent does and when it should activate. Example: "Expert at creating and editing [content type] in Pneuma {{displayName}} Mode. Works in a WYSIWYG environment where the user sees edits live in a browser preview panel."
pneuma-webcraft
by pandazkiPneuma WebCraft Mode workspace guidelines with Impeccable.style design intelligence. Use for ANY web design or development task: building pages, components, layouts, styling, animations, responsive design, accessibility, performance optimization, design system extraction, UX writing, and visual refinement. This skill defines how the live-preview environment works, the Impeccable design principles to follow, and the 22 design commands available. Consult before your first edit in a new conversation.
pneuma-session
by pandazkiRewrite the active Pneuma session's UI title + one-line summary so the launcher and ProjectPanel rows reflect what the session is actually about. Use this skill whenever the user asks to "整理 / 概括 / refresh / re-title / summarize this session", whenever the conversation has produced substantive work and the default title ("WebCraft session") is now uninformative, or before the user pauses a long session — the next time they reopen it, the row needs to say more than "<pneuma:env reason='opened'>". Cheap to run, persistent across reopens, mode-agnostic.
pneuma-project
by pandazkiProject-context awareness — multi-session workflows around one topic, shared materials and preferences, and cross-mode handoffs as a high-value user path.
pneuma-mode-maker
by pandazkiPneuma Mode Maker workspace guidelines. Use for ANY task in this workspace: creating modes, editing manifest.ts, pneuma-mode.ts, viewer components, skill prompts, seed files, publishing, forking, or any mode package development. This skill defines the ModeManifest reference, ViewerContract patterns, and publishing workflow. Consult before your first edit in a new conversation.
pneuma-clipcraft
by pandazkiAI-orchestrated video production on @pneuma-craft. Use whenever the user wants to generate, edit, or compose video clips, audio tracks, captions, or background music — including text-to-video / image-to-video generation, TTS narration, music generation, provenance tracking, and timeline composition. Trigger on phrases like "generate video", "make a clip", "add narration", "try another take", "add BGM", "edit project.json", "place on the timeline", "AIGC assets", "regenerate this shot", or any request that touches the exploded timeline or the dive-in panels. Also use when editing `project.json` by hand, registering assets, or wiring provenance edges. Do not assume the user knows the schema — they usually don't; read `references/project-json.md` before committing to an edit.
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.