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...
nms-custom-entity
by MrPippi建立自定義 NMS 實體：繼承現有 Mob 類別、自訂 PathfinderGoal、替換 vanilla 實體行為 / Create custom NMS entities with custom PathfinderGoal AI
nms-packet-interceptor
by MrPippi透過 Netty ChannelDuplexHandler 注入玩家連線管線，攔截/修改 Clientbound 與 Serverbound 封包 / Intercept and modify packets via Netty pipeline injection
nms-packet-sender
by MrPippi產生封包發送工具類，透過 ServerPlayer.connection 將 Clientbound 封包推送至客戶端（Paper NMS + Mojang-mapped）/ Generate packet sender utility to push Clientbound packets via ServerPlayer.connection
nms-reflection-bridge
by MrPippi反射式 NMS 存取橋接：避開 CraftBukkit v1_21_R1 版本編譯依賴，透過 reflection 快取取得跨版本相容性 / Reflection-based NMS bridge for cross-version compatibility without Paperweight compile dependency
nms-version-adapter
by MrPippi多版本 NMS 相容性 Adapter 模式：抽象介面 + 版本特定實作 + runtime dispatch，讓同一 plugin 支援多個 MC 版本 / Multi-version NMS compatibility adapter pattern
nms-attribute-modifier
by MrPippi透過 NMS AttributeMap/AttributeModifier 動態修改實體屬性（MAX_HEALTH、ATTACK_DAMAGE 等），比 Bukkit API 更精確（Paper NMS + Mojang-mapped）/ Dynamically modify entity attributes via NMS AttributeMap/AttributeModifier
nms-block-entity
by MrPippi實作自定義 NMS BlockEntity（含 NBT 序列化、Tick 邏輯、客戶端同步），比 Bukkit BlockState 更靈活（Paper NMS + Mojang-mapped）/ Implement custom NMS BlockEntity with NBT serialization, tick logic, and client sync
nms-boss-event
by MrPippi透過 NMS ServerBossEvent 操作 Boss Bar 進度條、顏色、風格、可見性，實現每人獨立 Boss Bar（Paper NMS + Mojang-mapped）/ Operate Boss Bar progress, color, style, and per-player visibility via NMS ServerBossEvent
nms-chunk-access
by MrPippi透過 NMS LevelChunk 直接讀寫方塊、高度圖、ChunkSection 資料，比 Bukkit Chunk API 更快更底層（Paper NMS + Mojang-mapped）/ Direct LevelChunk block, heightmap, and ChunkSection access for high-performance operations
nms-custom-menu
by MrPippi繼承 AbstractContainerMenu 建立自定義容器 GUI，支援 slot 事件攔截與資料同步（Paper NMS + Mojang-mapped）/ Build custom container GUIs by extending AbstractContainerMenu with slot event handling
nms-data-component
by MrPippi操作 Minecraft 1.21 DataComponentType 物品組件系統，讀寫 CustomData、MaxStackSize、Enchantments 等組件（Paper NMS + Mojang-mapped）/ Read and write 1.21 DataComponentType item components including CustomData, MaxStackSize, Enchantments
nms-nbt-manipulation
by MrPippi直接操作 CompoundTag 讀寫物品、實體、方塊實體的 NBT 資料（Paper NMS + Mojang-mapped）/ Read and write NBT data on items, entities, and block entities via CompoundTag
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.