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...
code-review
by xkazm04Review code for quality, security, and patterns. Scans changed files for bugs, OWASP vulnerabilities, SQLite injection, missing error handling, and anti-patterns specific to this Next.js + SQLite + Zustand stack.
triage-backlog
by xkazm04Systematically triage, challenge, and process a backlog of auto-generated idea files. Groups by context area, validates against codebase, filters BS, presents one approval gate, then executes autonomously with code review.
vibeman
by xkazm04Run a Vibeman pipeline on a project. Three modes — (A) goal-based development (PLAN > IMPLEMENT > VERIFY > REPORT), (B) audit-driven scan + triage + wave-based fix implementation, or (C) scan-and-decide (pick one context group; the skill auto-selects Idea scanners, generates a capped backlog, you accept/reject each, then it implements the approved scope). All run with quality gates, brain-signal recording, and structured per-wave/per-phase reporting.
npm-updates
by xkazm04Fetch npm package updates, analyze new features, and identify improvement opportunities for this app. Use when the user wants to explore what's new in dependencies or plan next development directions.
tdd
by xkazm04Test-driven development workflow. Write failing tests first, then implement minimal code to pass, then refactor. Uses Vitest with better-sqlite3 test database. Enforces RED → GREEN → IMPROVE cycle.
leonardo
by xkazm04Generate images with Leonardo AI (Lucid Origin model), remove backgrounds, and write SVG. For brand assets, UI illustrations, backgrounds, and icons.
code-review
by xkazm04Production-readiness code review for Rust backend and React frontend changes
leonardo
by xkazm04Generate images with Leonardo AI (Lucid Origin model), remove backgrounds, analyze with Gemini vision, and write SVG. For brand assets, UI illustrations, backgrounds, and icons.
sentry
by xkazm04Fetch active Sentry issues for the Personas project, diagnose root causes from stack traces, apply code fixes, and mark issues as resolved. Covers both Rust backend (tracing::error!, panics) and React frontend (exceptions, unhandled rejections).
triage-backlog
by xkazm04Systematically triage, challenge, and process a backlog of auto-generated idea files. Groups by context area, validates against codebase, filters BS, presents one approval gate, then executes autonomously with code review.
cr
by xkazm04Comprehensive code review for Next.js projects. Checks for bugs, performance issues, security vulnerabilities, and adherence to best practices.
ascent-onboard
by xkazm04Personalized AI-native onboarding for xkazm04/personas — generated by Ascent from a scan on 2026-06-10 (currently L4 Integrated, 75/100). Run it to adopt the highest-leverage practices (D4, D9, D8, D2) that move this repo toward autonomous, LLM-driven development.
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.