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...
il-localization-audit
by wobushiren79客栈传说 · 多语言文本审计。检查 Excel 文本 ID 在多语言文本表中的覆盖率、Bean 中 LanguageCache 字段使用规范、代码中硬编码中文字符串、TextEnum 注册情况。上线前与新增系统时使用。
il-minigame-scaffold
by wobushiren79客栈传说 · 小游戏脚手架。一键生成新 MiniGame 全套结构：Handler（继承 BaseMiniGameHandler）+ Builder + Bean + Character Bean + UI（Main/Settlement/Component）+ Cpt 组件骨架 + 枚举追加清单 + Builder Prefab 路径建议。新增烹饪/弹幕/算账/辩论/战斗/出生类小游戏时使用。
il-minigame-state-audit
by wobushiren79客栈传说 · 小游戏状态机与事件配对审计。检查所有 MiniGame*Handler 的 InitGame/StartGame/EndGame 状态机完备性、RegisterEvent/UnRegisterEvent 配对、MiniGameEnum 各 switch 是否补齐 case、Builder Prefab 是否存在、UI 是否注册。新增小游戏后或调试小游戏诡异行为时使用。
il-scene-init-check
by wobushiren79客栈传说 · 场景 Handler 挂载与初始化检查。扫描 Unity 场景文件 (.unity YAML)，检查所有 Handler 是否挂载、是否在场景初始化脚本中调用 InitData()、是否注册了对应的 UIEnum 映射。新增 Handler 后或场景跑不通时使用。
il-spine-setup
by wobushiren79客栈传说 · Spine 角色资源接入指引。引导将一套 Spine 资源（atlas/json/png）正确接入项目：放置目录、Addressable 标记、SpineManager 加载验证、动画状态枚举注册。新增 NPC/角色/特效时使用。
il-sqlite-schema-check
by wobushiren79客栈传说 · SQLite 表结构与 DB Bean 字段一致性审计。读取 StreamingAssets/SQLiteDataBase 中的 .db 文件 schema，对比 Assets/Scripts/Bean/MVC 下 Bean 字段，找出类型不符、字段缺失、表缺失、孤立字段。新增/修改数据库表后或上线前使用。
il-addressable-audit
by wobushiren79客栈传说 · Addressables 资源审计。扫描 Assets/AddressableAssetsData/AssetGroups 与 Assets/Scripts 中的 Addressable 地址引用，找出未注册地址、孤立资源、命名违规、Group 归属异常等问题。用于例行体检或上线前检查。
il-build-prep
by wobushiren79客栈传说 · Steam 上线前置检查。打包前一次性校验：版本号一致性、steam_appid 配置、Addressables 是否需要重新 Build、调试代码残留、Logger 开关、Debug 场景、敏感字符串、TODO 遗留、平台开关等。每次出版本时使用。
il-datetime-check
by wobushiren79客栈传说 · 游戏日期/时间系统审计。检查新建游戏 gameTime 是否被正确初始化、日历配置(DateInfo)与时间流转常量(maxForDay/月数/季节)是否一致、季节映射与特殊日期(结婚日/建筑日/生日)比对逻辑、0/0/0 测试哨兵是否影响正式流程。新建游戏日历空白、日期显示异常、季节/天气错乱、跨天/跨月/跨年边界出错时使用。只读分析。
il-event-flow-trace
by wobushiren79客栈传说 · MsgEnum 全局事件链路追踪。分析每个全局事件的发布点（TriggerEvent）和订阅点（AddListener），找出未被订阅的事件、未被发布的死代码订阅、跨系统耦合关系。重构与调试事件死循环时使用。
il-gamble-scaffold
by wobushiren79客栈传说 · 赌博子游戏脚手架。一键生成新 Gamble 子游戏：Bean（继承 GambleBaseBean）+ UI（继承 UIBaseGamble）+ Item 组件 + 枚举追加清单 + 文本 ID 建议。比 MiniGame 体系轻量，适合骰子/卡牌/掷币等无场景对抗的玩法。新增赌博类玩法时使用。
sacrifice-system
by wobushiren79Demon Lord Roguelike 游戏的生物献祭升级系统开发指南。使用此SKILL当需要创建或修改生物献祭流程、献祭升级(等级提升)、献祭成功率公式(祭品数量/生物id/等级差修正 2^(祭品level-目标level))、保底机制、祭坛动画、献祭祭品消耗与装备退回、等级上限、献祭UI、升级手动加点UI(UICreatureAddAttribute)等，包括 CreatureSacrificeLogic 献祭逻辑、CreatureSacrificeBean 献祭数据、UICreatureSacrifice 献祭界面、UICreatureAddAttribute 升级加点界面、UICreatureManager 升级按钮、CreatureBean.UpLevelForSacrifice/CanUpLevel/IsMaxLevel/sacrificePityRate、CreatureUtil.GetSacrificeSuccessRate 成功率公式、CreatureUtil.GetAttributePointAddValue 单点增量、LevelInfo(level_exp/sacrifice_num/attribute_point) 等级配置、CreatureSacrifice_* 事件常量、UnlockEnum.Altar 祭坛解锁等。
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.