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...
qovery-ui
by QoveryDesign review and implementation guidance for the Qovery Console. Use for auditing UI components, refining spacing and color, selecting the right shared component, writing microcopy, and designing new surfaces from scratch.
qovery-console-standards
by QoveryQovery Console coding standards, architecture guidelines, naming conventions, testing practices, and development workflows. This skill should be used when writing, reviewing, or refactoring code in the Qovery Console monorepo to ensure consistency and quality.
react-useeffect
by QoveryReact useEffect best practices from official docs. Use when writing/reviewing useEffect, useState for derived values, data fetching, or state synchronization. Teaches when NOT to use Effect and better alternatives.
vercel-react-best-practices
by QoveryReact and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React/Next.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance improvements.
qovery-deploy
by QoveryDeploys any application, database, Helm chart, or Terraform module to Kubernetes via Qovery. Analyzes the user's codebase, creates missing Dockerfiles, provisions databases (container or managed), sets up environment variables, and deploys via Qovery CLI + API or Terraform provider. Supports Node.js, Python, Go, Java, Ruby, PHP, .NET, React, Vite, Next.js and more. Use when the user asks to deploy, ship, set up, or release an application on Qovery or Kubernetes via Qovery.
qovery-onboard
by QoveryGuided onboarding for new Qovery users. Acts as a personal cloud architect — collects the user's role, experience level, industry, compliance needs, and constraints, then recommends and sets up the optimal Qovery configuration. Handles cloud provider selection, cluster creation (managed or BYOK), project/environment structure, security defaults, cost defaults, team invitations, and migration from other platforms (Heroku, Vercel, Render). Use when a user is new to Qovery or wants to set up the platform from scratch.
qovery-optimize
by QoveryReduces Kubernetes cluster and application costs on Qovery. Analyzes historical resource consumption, factors in the user's business context (seasonal patterns, growth stage, reliability requirements), estimates external resource costs from public cloud pricing, and proposes right-sizing, autoscaling, environment scheduling, spot instances, and database mode changes. Generates a cost report with CSV export and applies changes via CLI+API or Terraform. Use when the user asks to reduce costs, right-size, or optimize Qovery resource spend.
qovery-preview
by QoveryCreates temporary preview environments from pull requests via Qovery. Detects or creates a blueprint environment, clones it for each PR, switches git branches, configures auto-shutdown, and handles cleanup. Supports full-stack and single-service previews. Use when the user asks for a PR preview, branch preview, ephemeral environment, or per-PR deployment on Qovery.
qovery
by QoveryRoute Qovery requests to the right specialized skill and handle quick operations (list, status, stop, restart, logs, clone, scale). Activates on any generic Qovery mention. Use when the user mentions Qovery without a specific action, needs a simple operational command, or wants to discover available Qovery capabilities.
qovery-speedup
by QoverySpeeds up Qovery deployments. Analyzes deployment timelines via the V2 deployment history API, identifies bottlenecks (build, startup, health check, scheduling, image pull), classifies them as user-controllable or Qovery infrastructure, and proposes fixes — Dockerfile optimization, build cache strategies, health check tuning, deployment stage parallelism, image pull optimization. Generates a diagnostic report for Qovery support when the bottleneck is infrastructure-side. Use when the user complains about slow deployments or long build times on Qovery.
qovery-terraform
by QoveryGenerate Terraform manifests from an existing Qovery setup. Reads the current configuration from the Qovery API, generates HCL for the Qovery Terraform provider, imports existing resources into Terraform state, and validates in a test clone. Supports single or multiple environments. Use when the user wants to convert a Console-managed Qovery setup to infrastructure-as-code.
qovery-troubleshoot
by QoveryDiagnoses and fixes deployment failures, application crashes, build errors, connectivity problems, stuck deployments, and cluster issues on Qovery. Uses a systematic 8-layer diagnosis with MCP Server integration, CLI, and API, and generates runbooks for recurring issues. Use when the user reports a Qovery deployment that is failing, broken, stuck, or crashing. (For slow deployments use qovery-speedup; for cost optimization use qovery-optimize.)
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.