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...
grade-tcg-card
by pjt222Grade a trading card using PSA, BGS, or CGC standards. Covers observation-first assessment (adapted from meditate's unbiased observation), centering measurement, surface analysis, edge and corner evaluation, and final grade assignment with confidence interval. Supports Pokemon, MTG, Flesh and Blood, and Kayou cards. Use when evaluating a card before professional grading submission, pre-screening a collection for high-grade candidates, settling condition disputes between buyers and sellers, or estimating the grade-dependent value spread for a card.
play-custom-multiplier-dice-game
by gabrielmoreiraManages a turn-based dice game where players accumulate points with specific score multipliers (x2 at 25, x4 at 100), aiming for a goal of 250 points.
mtg-rules
by KaelSenseiMagic: The Gathering comprehensive rules reference and rules engine. Use this skill whenever the user asks about MTG rules, card interactions, game mechanics, timing, priority, layers, state-based actions, combat, stack resolution, or any rules question about Magic: The Gathering. Also trigger on: 'how does X work in MTG', 'can I do X in response to Y', 'what happens when', 'is this legal', 'rules question', 'judge call', 'layer system', 'replacement effects', 'triggered abilities', 'state-based actions', 'commander damage', 'color identity', or any dispute about how a card or mechanic works. Even casual questions like 'does this combo work' or 'what happens if I play X' should trigger this skill.
werewolf-judge
by njzjz狼人杀法官技能（流程执行版）。 只负责状态机推进、协议校验、公开播报，不负责玩家扮演策略。
referee-report
by expectedparrotSynthesize investigated katz issues into a narrative referee report
rules-batcher
by dkordubanBatch 5-20 rules/card questions into one Haiku agent call that reads PDF + docs once and answers all. Replaces per-question rules-lawyer calls. Triggers on: /rules-batcher, batch rules questions, look up these cards, verify these rules.
encounter-judge
by framersaiFair adjudication of game encounters — combat, puzzles, social challenges
warhammer-40k-agents-arbitrator
by G3N7Rules Judge & Dispute Resolver. Use when the user asks to talk to the Arbitrator, needs a rules ruling, has a rules dispute, asks about phase sequences, complex interactions, or FAQ lookups.
match-result-updater
by glenmanUpdates match results for Shanghai Port FC teams. Invoke when user says 'update match result' or '更新比赛结果' with team name, opponent, and score.
rule-8-special-cards
by kelody104サイキック、ドラグハート、GR、神帝、ゴッドなど特殊なカードや仕組みのルール（全文）
rules-lookup
by Lehi-InnovationUse when the user asks a boardgame rules question via /rules command, or asks about rules for a game that exists in the rules/ directory. Answers using a tiered lookup (summary file, extracted rulebook, BGG forums, intuition) and auto-creates a PR to update the rules summary when new information is found.
contrarian
by lispking杠精 skill — 抬杠专用。你说什么我都反驳，永远能找到不同角度来杠。
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.