381,784 Collected SKILL.md files

Explore AI Agent Skills & Claude Prompts

Discover open-source agent skills for Claude Code, Codex, ChatGPT, and any tool that uses SKILL.md.

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Showing 12 of 17 skills
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postgis-spatial

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Use when working with geographic queries, geometry filtering, ST_* functions, or region-based candidate filtering. Load for PostGIS spatial operations, polygon intersections, distance calculations, or geographic bounds. Covers ST_Intersects, ST_Contains, GiST indexes, and geometry storage patterns.

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testing

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Run tests before and after changes. Only add tests for complex logic, not boilerplate. Database tests matter most.

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codebase-conventions

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Load when starting work in 10x-Mapmaster or when unsure about project standards. Covers file organization, naming conventions, type constraints, SQL style, and project-specific limits (200-char descriptions, answer enums). Use to understand how the codebase is structured and what standards to follow.

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database-first

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REQUIRED before implementing any game feature, scoring logic, state transition, or decision-making. ALL business logic lives in PostgreSQL - frontend is presentation only. Load this to understand where code belongs: database function vs component. Covers RPC patterns, SECURITY DEFINER, and anti-patterns.

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edge-functions

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Use when working with Deno edge functions, LLM integration, or embedding generation. Load for Deno.serve patterns, Zod request validation, OpenRouter LLM calls, and error handling. Covers function structure, CORS, and the call-llm/generate-embedding patterns.

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executing-tasks

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Use when working through any task checklist (not just OpenSpec). Complete one task, verify, mark done, then next. No skipping, no adding, no reordering.

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knowledge-sync

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Use after completing a refactor, implementing a major feature, or when skills may be outdated. Analyzes code changes, identifies affected skills, and proposes updates to keep team knowledge current. Also use when you notice a skill contains outdated patterns. Ask clarifying questions if changes are confusing.

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maplibre-camera

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Use when implementing map camera animations (flyTo, easeTo, jumpTo), handling zoom transitions, or managing bearing/pitch. Load for useMapCamera composable patterns, preventing camera feedback loops, promise-based animations, and globe visibility filtering. ALWAYS use the composable, never direct map access.

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openspec-apply

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Use when implementing an APPROVED OpenSpec change. Read tasks.md and follow EXACTLY. Do not deviate, add features, or skip tasks. Use with test-tdd for each code task.

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openspec-check

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Use BEFORE any implementation to check if specs exist for the capability. Run this first when starting any feature work. Reports: spec exists, no spec, or active change in progress.

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openspec-propose

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Use when adding features, changing behavior, or modifying architecture. Creates change proposal with specs and tasks. ALWAYS ask for approval before proceeding to implementation. Do NOT use for bug fixes or trivial changes.

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postgres-vectors

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Use when working with embeddings, semantic similarity, vector search, or the <-> <#> <=> operators. Load for pgvector queries, HNSW index creation, embedding storage, or similarity calculations. Covers distance operators, index strategies, and common pitfalls with 384-dimensional vectors.

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Browse Agent Skills by Occupation

23 major groups · 867 SOC occupations

Browse by Category

Explore agent skills organized by their primary use case

SKILLMD / CREATORS AND OCCUPATION CATEGORIES

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.

SEO KNOWLEDGE HUB & TECHNICAL OVERVIEW

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.

8 QUESTIONS

Frequently Asked Questions

A practical guide to agent skills: what they are, how to inspect them, and how SkillMD helps you explore the ecosystem.