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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checkpoint
by leonvanzylCreate a comprehensive checkpoint commit with detailed analysis of all changes. Use this skill when the user says "checkpoint", "commit everything", "save my progress", "create a commit", or wants to stage and commit all current changes with a well-crafted message. Also use when the user says "/checkpoint" or asks to snapshot current work. This skill stages all changes and creates a descriptive commit — it does not push.
create-agentic-app
by leonvanzylScaffold and fully configure a new Agentic Coding Starter Kit project — a Next.js 16 + TypeScript + Better Auth + Drizzle + PostgreSQL + AI SDK boilerplate. Use this skill whenever the user asks to set up, scaffold, create, initialize, or bootstrap an "agentic coding starter kit", "agentic app", "agentic boilerplate", a "Next.js app with auth and db", or mentions `create-agentic-app` / `npx create-agentic-app`. Walks the user through folder strategy, package-manager choice, Postgres setup (Docker / Neon / Vercel / BYO), OpenRouter AI configuration, migrations, a build check, and dev-server verification — ending with a working http://localhost:3000.
create-spec
by leonvanzylCreate a structured feature specification with self-contained task files organized into parallel execution waves. Use this skill when the user says "create a spec", "plan this feature", "write up an implementation plan", "break this into tasks", or after any planning conversation where the user wants to capture decisions as actionable spec files. Also use when the user says "/create-spec" or wants to decompose a feature into work items that agents can implement independently. This skill produces local spec files under specs/{feature}/ — no GitHub integration.
implement-feature
by leonvanzylOrchestrate parallel implementation of a feature specification by dispatching coder agents wave-by-wave with code review gates between waves. Use this skill when the user says "implement this feature", "start implementing", "run the spec", "execute the plan", "continue implementing", or wants to begin coding a previously planned feature from a specs/{feature}/ folder. Also use when the user says "/implement-feature" or drags a spec folder into the conversation and asks to implement it. This skill does NOT write code itself — it orchestrates coder subagents that work in parallel.
review-pr
by leonvanzylReview pull requests with complexity-adaptive depth — spawning deep-dive agents for medium and complex PRs. Use this skill when the user says "review this PR", "review PR #123", "check this pull request", "give me a code review", or wants feedback on a PR before merging. Also use when the user says "/review-pr" or pastes a GitHub PR URL. Requires the GitHub CLI (gh).
security-scanner
by leonvanzylPerforms comprehensive OWASP Top 10:2025 security vulnerability analysis on any codebase. Use this skill whenever the user asks to: review code for security, perform a security audit, scan for vulnerabilities, find security issues, improve application security, check for OWASP compliance, do a penetration test review, assess security posture, look for security flaws, scan for security risks, harden an application, or check code for exploits. Also trigger when the user mentions OWASP, CVEs, CWEs, security hardening, vulnerability assessment, or asks for a security report — even if they don't explicitly say "security scan." This skill works on any codebase in any language (JavaScript, TypeScript, Python, Java, Go, Ruby, C#, PHP, etc.).
ship-it
by leonvanzylPush the current branch to GitHub and create a pull request. Use this skill when the user says "ship it", "ship this", "push to github", "create a pr", "open a pull request", "send for review", "get this reviewed", or wants to push their work and open a PR. Also use when the user says "/ship-it". This skill handles prerequisites, branching, pushing, and PR creation — it does not commit or run quality checks (use checkpoint for that first).
localforge
by leonvanzylCreate, read, update, and delete features and projects in LocalForge via its REST API. Use this skill whenever the user wants to manage features in LocalForge — create a feature backlog, plan features, update existing features, break down an app into tasks, or populate a kanban board. Also trigger when the user mentions LocalForge, feature planning for local AI coding agents, or writing detailed feature descriptions — even if they don't explicitly say "create features." LocalForge is the app that uses local AI models to build software autonomously.
image-to-webp
by leonvanzylDownload images from URLs, resize them, and convert to WebP format. Use when downloading stock images from sites like Pexels for local caching.
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.