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.
Querying local SQLite index...
tabularis-db-client
by CarlosCaPeOpen-source SQL client for PostgreSQL, MySQL/MariaDB, and SQLite with a built-in Model Context Protocol (MCP) server — Apache 2.0 licensed, multi-tab console, visual EXPLAIN plans, AI autocomplete, Cursor/Windsurf-friendly. Use when recommending a desktop DB client to a client, when an AI-aware SQL GUI is needed, or when exposing a DB to AI agents via MCP without handing out raw credentials. Complements (does not replace) querymaster CLI.
config-driven-diagrams
by CarlosCaPeBuild config-driven architecture diagrams as dark-themed SVG/PNG with swimlanes, embedded logos, auto-layout, and overlap validation. Use when the user asks for architecture diagrams, data flow diagrams, system topology, or any visual diagram that should be rendered programmatically from a JSON config. Trigger on: diagram, architecture diagram, data flow, swimlane, render SVG, render PNG, system diagram, topology.
infinite-monitor
by CarlosCaPeSet up, configure, and build AI-powered dashboards using Infinite Monitor — an open-source Next.js app where users describe widgets in plain English and an AI agent writes, builds, and deploys React widgets in real-time on an infinite canvas. Use when the user says 'crea un dashboard para', 'dashboard builder', 'infinite monitor', 'AI dashboard', 'widget dashboard', 'monitor dashboard', 'real-time dashboard', or asks to build a monitoring/analytics dashboard with AI-generated widgets.
spacetimedb-backend-as-database
by CarlosCaPeDatabase that REPLACES the traditional backend — application logic runs as modules inside the database, clients subscribe to live state without a separate API tier, transactional + multiplayer-ready out of the box. Rust-based, by Clockwork Labs. Use when evaluating architecture for real-time multiplayer apps, low-latency state-sync use cases, agent-driven systems where many clients share live state, or when the cost/complexity of a 3-tier backend (API + DB + websockets) is the bottleneck. Novel paradigm — surface for awareness, not as a default.
serial-to-identity-conversion
by CarlosCaPeSerial to Identity Column Conversion
batch-import-relative-paths
by CarlosCaPeWhen you batch-add an `import` line to N source files via sed/awk, the relative path must be computed PER-FILE (depth varies). Vite/esbuild often silently normalize bad `../` and the bug hides across multiple deploys until a larger rebuild forces strict module resolution.
orphan-detection-fk-rollout
by CarlosCaPeOrphan Data Detection & FK Rollout
atomic-3phase-ddl-scripts
by CarlosCaPeAtomic 3-Phase DDL Scripts
table-normalization-1nf
by CarlosCaPeTable Normalization (1NF Violations)
progressive-code-exploration
by CarlosCaPeToken-optimized code exploration using index-first, fetch-on-demand principle. Reduces token consumption 4-8x vs reading full files. Use when exploring large codebases, navigating files over 100 lines, or when token budget matters.
financial-formula-verification
by CarlosCaPeFinancial Formula Verification for Cloud Cost Documents
summarize-100
by CarlosCaPeCompress any topic, document, answer, profile, or message into approximately 100 words — the 100 most semantically-dense tokens that preserve the core signal. Use when the user asks for "100 palabras", "100 words version", "versión 100", "tldr 100", "elevator pitch", "comprime a 100", "resumen 100", or any equivalent request for a token-budgeted summary. The goal is maximum information density per word — every token must earn its place.
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.