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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openclaw-management
by freemaThis skill should be used when the user wants to interact with OpenClaw, delegate tasks to their AI assistant, or check gateway status. Activates for AI assistant delegation and orchestration.
spreadsheet-management
by freemaThis skill should be used when the user asks about Google Sheets, spreadsheets, cells, rows, columns, charts, or data tables. Activates for reading, writing, formatting, or analyzing spreadsheet data.
design-extraction
by freemaThis skill should be used when the user asks about Storybook, design systems, UI components, extracting HTML, or building interfaces using existing components. Activates for component listing, HTML extraction, theme analysis, or UI assembly.
jira-management
by freemaThis skill should be used when the user asks about Jira issues, tickets, projects, sprints, or bug tracking. Activates for creating, updating, searching, or commenting on Jira issues.
seo-blog
by freemaWrite player-focused, SEO-optimized blog posts for PixelDen. Use for any new article in content/blog/. Hard rule: NO technical/devlog content (no tech stack, code, internals, file paths, framework names). Audience is players, not developers.
code-review
by freemaSandwich-structured code review for PRs, MRs, diffs, or pasted code. Triggers on: "review this", "check my PR/MR", "what do you think of this code", "is this OK to merge", or any paste of code/diff where the user wants feedback. Produces motivating reviews that clearly separate blockers from nitpicks. Always use this skill — even for quick reviews. The structure matters.
jira
by freemaPost code review verdicts to Jira. Use after a code review is done and the user wants to sync the result to Jira. Triggers on: "post to Jira", "update the ticket", "sync review to Jira", or when a review is complete and Jira context is available (project key, issue number, or Atlassian MCP connected). Posts a concise verdict comment, flags blockers, and suggests workflow transitions.
image-generation
by freemaGenerate game assets via Google Gemini API and process them into final sprite files. Use when creating new game sprites, generating concept art, or running the asset pipeline (Gemini image generation, sprite sheets, Imagen).
pixelart-cleanup
by freemaUse when the user wants to clean halo fragments or imperfect alpha from native-resolution pixel art sprites (PixelLab, Aseprite, or handmade). Invokes the Node/TypeScript CLI at scripts/pixelart-cleanup. Do NOT trigger for upscaled AI pixel art where one logical pixel spans many real pixels — that needs grid detection instead.
phaser-gamedev
by freemaBuild 2D browser games with Phaser 3 (JS/TS): scenes, sprites, physics (Arcade/Matter), tilemaps (Tiled), animations, input. Trigger: 'Phaser scene', 'Arcade physics', 'tilemap', 'Phaser 3 game'.
pixellab
by freemaGenerate pixel-art tilesets and sprites via PixelLab MCP. Use when creating sidescroller platform tiles, top-down Wang tilesets, or other pixel-art assets through the PixelLab MCP server.
sprite-processing
by freemaUse when processing, cropping, splitting, or cleaning up existing sprite images or sprite sheets. Triggers on "crop sprite", "remove background", "split sprite sheet", "make transparent", "process PNG".
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.