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...
rexicon
by uhstray-ioGenerate a rexicon.txt codebase index containing the full file tree and every symbol with line numbers. Use this INSTEAD of Grep or Glob when exploring the codebase, finding where a function/struct/class is defined, or understanding project structure. Prefer this over multiple grep searches.
training
by uhstray-ioSocratic teaching mode — Sensei. Guides users to solutions without ever writing new code. Describes approaches, names relevant APIs/functions, provides documentation links, explains errors in plain language, and asks questions that lead the user to the answer themselves. Use when user says "training mode", "sensei mode", "teach me mode", "guide me through this", "teacher mode", "help me learn", "I want to learn", "no code just explain", or invokes /training. Also triggers when user says "explain without doing" or "don't write it, teach me".
strict-refactor
by uhstray-ioUse when a large function, component, or call site needs decomposing into named single-purpose units — extract computation blocks, guard clauses, setup phases, UI subtrees, or bundled parameter groups, or move a function to a better module. Triggers on "strict refactor", "/strict-refactor", "extract function", "extract component", "break up this function", "split into smaller functions", "group these parameters". Changes structure only — no logic, no renames, no designed abstractions.
strict-simplify
by uhstray-ioUse when reviewing code to replace redundant or verbose logic with a provably-equivalent simpler form — custom code that duplicates a stdlib/builtin or an existing project function, dead/no-op arguments, collapsible redundant expressions, duplicate inline logic that reimplements something already defined in the codebase. Triggers on "strict simplify", "/strict-simplify", "reduce redundant code". Does not restructure, rename, reformat, optimize, or fix bugs.
systematic-debugging
by uhstray-ioUse when encountering any bug, test failure, or unexpected behavior, before proposing fixes
explaining-changes
by uhstray-ioUse during implementation to narrate changes as they happen — activates for the session and explains each logical change, each completed plan task, and every change being committed, using brief prose plus simple ASCII diagrams. Triggers on "explain as you go", "narrate changes", "walk me through the changes", "explain what you're doing", and similar. Output is chat/CLI only — nothing is written to files.
explaining-plans
by uhstray-ioUse when augmenting or explaining a plan, spec, design, or RFC document — enriches the document in place with decision criteria, cited source context, target-outcome framing, and prose-introduced mermaid diagrams. Triggers on "explain the plan", "add rationale", "why did we choose", "cite the sources", "augment this spec", "add diagrams to the design", and similar. Composes with writing-plans rather than replacing it — writing-plans authors the plan; this skill makes it self-explaining.
memory-status
by uhstray-ioShow memory nexus statistics — drawer count, wings, rooms. Triggers on "memory status", "nexus stats", "how much is in memory", "what wings exist".
onboarding
by uhstray-ioUse when setting up Claude Code for the first time, configuring auto mode, enabling agent teams, or when user says "set up Claude", "configure my assistant", "onboard me", or "enable auto mode"
repo-memory
by uhstray-ioUse when the user says "remember", "don't forget", "save that", "keep that in mind", "recall", "what do we know about", "look that up", or any request to persist or retrieve project knowledge — stores memories in .claude/memory/ using the Claude Code memory format, committed to git so the whole team shares context.
session-resume
by uhstray-ioUse when starting a session that should continue prior work — phrases like "resume the session", "pick up where we left off", "continue from where we stopped", "where were we", "load the continuation file". Consumes the .claude/CONTINUE.md written by session-save: verifies it against the repo, re-hydrates context, briefs, then acts. Not for mid-session checkpoints (use grounding) or recalling stored facts (use repo-memory).
brainstorming
by uhstray-ioYou MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
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.