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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hotwire-coder
by majesticlabs-devUse when implementing Hotwire features with Turbo Drive, Turbo Frames, and Turbo Streams. Applies Rails 8 conventions, morphing, broadcasts, lazy loading, and real-time update patterns.
pydantic-validation
by majesticlabs-devRecord-level data validation using Pydantic models. Field validators, model validators, and batch validation patterns.
dhh-coder
by majesticlabs-devWrite Ruby and Rails code in DHH's distinctive 37signals style. Use this skill when writing Ruby code, Rails applications, creating models, controllers, or any Ruby file. Triggers on Ruby/Rails code generation, refactoring requests, or when the user mentions DHH, 37signals, Basecamp, HEY, Fizzy, or Campfire style.
create-adr
by majesticlabs-devCreate Architecture Decision Records (ADRs) to document significant technical decisions, their context, alternatives considered, and consequences. Use when making architectural choices, selecting libraries/frameworks, or designing system components.
ruby-coder
by majesticlabs-devThis skill guides writing of new Ruby code following modern Ruby 3.x syntax, Sandi Metz's 4 Rules for Developers, and idiomatic Ruby best practices. Use when creating new Ruby files, writing Ruby methods, or refactoring Ruby code to ensure adherence to clarity, simplicity, and maintainability standards.
inertia-coder
by majesticlabs-devBuild modern SPAs with Inertia.js and Rails using React, Vue, or Svelte. Use when creating Inertia pages, handling forms with useForm, managing shared props, or implementing client-side routing. Triggers on Inertia.js setup, SPA development, or React/Vue/Svelte with Rails.
ansible-coder
by majesticlabs-devThis skill guides writing Ansible playbooks for server configuration. Use when hardening servers, installing packages, or automating post-provisioning tasks that cloud-init cannot handle.
anomaly-detector
by majesticlabs-devDetect anomalies in data using statistical and ML methods. Z-score, IQR, Isolation Forest, and time-series anomalies.
dhh-code-reviewer
by majesticlabs-devDHH-style code review. Reviews Ruby, Rails, and JavaScript code for convention violations, framework contamination, and unnecessary complexity.
cloud-init-coder
by majesticlabs-devThis skill guides writing cloud-init configurations for VM provisioning. Use when creating user_data blocks in Terraform/OpenTofu, or cloud-init YAML for AWS, DigitalOcean, GCP, or Azure instances.
cloudflare-coder
by majesticlabs-devThis skill guides provisioning Cloudflare infrastructure with OpenTofu/Terraform. Use when managing zones, DNS records, WAF rules, SSL settings, Page Rules, or cache configuration.
devops-plan
by majesticlabs-devGather DevOps context for infrastructure planning. Detects IaC tools, providers, and recommends skills. Use when Skill("blueprint") detects infrastructure work.
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.