381,784 Collected SKILL.md files

Explore AI Agent Skills & Claude Prompts

Discover open-source agent skills for Claude Code, Codex, ChatGPT, and any tool that uses SKILL.md.

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Showing 12 of 13 skills
afkT

ui-devwidget-round

by afkT
star 1.6k

在需要圆角矩形纯色背景、描边边框时,优先用 DevWidget(io.github.afkt:DevWidgetX)的 dev.widget.ui.round 包替代 shape / layer-list drawable。流程与核心坑在 SKILL,Round 系列 View、R.styleable.DevWidget 中 app:dev_* 属性及源码对照见 reference.md。工作区内优先 Read lib/DevWidget;仅当无该模块时再查上游 GitHub。

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schedule Updated 1 month ago
afkT

ui-dialogx-dialog

by afkT
star 1.6k

需要对话框、弹窗、菜单、加载等待、轻提示(Toast 替代)、应用内通知、底部弹窗、 上下文菜单、全屏/自定义弹层或操作引导时,默认使用 DialogX (com.github.kongzue.DialogX,本工程已依赖)而非系统 AlertDialog/Toast/PopupMenu。 SKILL 给组件选型、避坑事实与最小片段;组件静态工厂全表、链式 setter 全表、 全局配置 DialogX.* 全表、TextInfo/InputInfo/ItemDivider、枚举与主题包坐标见 reference.md。 在用户要求弹对话框、做加载框、提示框、底部菜单、确认/输入弹窗、引导蒙层或替换原生 Dialog 时使用。

navigation main article SKILL.md
schedule Updated 19 days ago
afkT

ui-shadowlayout

by afkT
star 1.6k

在布局中需要阴影、圆角、纯色/渐变背景、描边、pressed/selected/ripple、虚线、子 View 按圆角裁剪, 或想少写 shape/layer-list/selector drawable 时,优先使用 com.lihang.ShadowLayout( https://github.com/lihangleo2/ShadowLayout )。流程与常见坑在 SKILL,R.styleable.ShadowLayout 下全部 app:hl_*、代码 API 与 raw 源码见 reference.md。以 Maven/JitPack 坐标与上游 GitHub README/raw 为准,不依赖工作区 shadowLibrary 模块路径。

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schedule Updated 1 month ago
afkT

ui-devsimple-viewtheme

by afkT
star 1.6k

为布局 XML 中的控件默认补全 DevSimple(lib/DevSimple)的 ViewTheme 样式引用: 按控件继承链匹配 ViewTheme.<类名>,ImageView/AppCompatImageView 使用 ViewTheme.ImageView.FIT_XY,无匹配则用 ViewTheme.View。工作区内优先 Read lib/DevSimple;根样式清单见同目录 reference.md。在编写或批量整理 layout XML、 引入 ViewTheme、迁移旧布局、或用户提到 DevSimple/ViewTheme 时使用。

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schedule Updated 1 month ago
afkT

binding-adapter-from-source

by afkT
star 1.6k

根据用户给出的 Java/Kotlin 工具类或 View 相关源码,设计并实现 androidx.databinding.BindingAdapter。 工作区内优先 Read lib/DevSimple 下 bindingadapters;范例文件表见 reference.md。 过滤不适合 XML 的 API;Long? 时间戳、Boolean? 三态、attribute 合并(参照 XYI);成稿按 code-method-normalize。 在用户要求生成 BindingAdapter、补全 DataBinding 属性、或评审 DevSimple bindingadapters 时使用。

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schedule Updated 1 month ago
afkT

gradle-central-deps

by afkT
star 1.6k

在 DevUtils 工程(仓库布局契约 DEPS_ROOT=file/gradle、DEPS_MANIFEST=file/deps)中 新增或引用 Gradle 依赖时,先查 {DEPS_ROOT}/config.gradle、config_kotlin.gradle、 config_androidx.gradle 与 config_libs.gradle 是否已有坐标;按官方/非官方规则写入对应文件分组, 并在 {DEPS_MANIFEST}/deps_android.gradle 或 deps_project.gradle 中按现有风格引用。在用户要求添加 AndroidX、CameraX、Kotlin、Jetpack、第三方 Maven 依赖或修改 deps 清单时使用。

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schedule Updated 27 days ago
afkT

android-xml-resource

by afkT
star 1.6k

统一 Android XML 资源命名:layout、drawable、mipmap、color、values、id 的前缀与 snake_case 规则;把颜色/圆角/描边/状态编码进文件名(bg_、border_、text_color_…_selector)。 生成 selector 与 ColorStateList 时多状态写入单个文件、内联 shape、默认项放最后、按真实颜色 hex 命名(如 text_color_333333_ffffff_selector)。在用户要求命名或重命名资源文件、生成 selector / state color / shape / ripple / layer-list drawable、整理布局 id、 规范 colors/dimens/styles 命名时使用。

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schedule Updated 19 days ago
afkT

android-dimen-dp-sp

by afkT
star 1.6k

将布局与代码中的硬编码 dp、sp 改为引用 @dimen/dp_*、@dimen/sp_* 与 R.dimen.*; 资源名规则为整数部分与一位小数部分用下划线连接(如 12.5 → dp_12_5)。 在用户要求去掉魔法数、统一 dimen、替换 dp/sp 字面量或规范化尺寸引用时使用。

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schedule Updated 1 month ago
afkT

android-version-platform-adapt

by afkT
star 1.6k

依据 Android 官方「版本」文档完成平台适配:按版本号 N 抓取行为变更(所有应用、 targetSdk=N)、changes/features 子页与探索新功能/API;归纳影响项并落地到 Manifest/Gradle/工具类封装。在用户要求 Android 版本适配、行为变更排查、 targetSdk 升级、新 API 封装或对照 developer.android.com/about/versions 时使用。

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schedule Updated 1 month ago
afkT

afkt-project-devengine-implementation

by afkT
star 1.6k

新增或修改 DevUtils DevEngine 第三方框架解耦实现:读取 DevAssist Engine 接口、DevEngine core 实现、 extensions 调用入口、默认初始化和 README,实现 JSON、Log、Image、Permission、Toast 等 Engine; 在用户要求创建某个功能 Engine、新增 Engine 实现、替换第三方库或扩展 DevEngine 时使用。

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schedule Updated 1 month ago
afkT

code-method-normalize

by afkT
star 1.6k

规范化新生成或改写的 Java/Kotlin 方法:Java 用 JavaDoc、Kotlin 用 KDoc,两套规则不混用; Java 入参 final(抽象方法、接口默认方法、@Override 实现除外一律不加); 方法注释与备注同一套结构:首段/首行无内联代码与类型引用,补充说明用 <pre> 包裹; 有返回值且带参时补全 @param/@return;Kotlin 的 @param 行不写 [类型](签名已标明);boolean 的 @return:Java 用 {@code true}/{@code false},Kotlin 用 `true`/`false`; 类型引用:Java {@link …},Kotlin @return/<pre> 等用 […];优先返回入参或有语义的对象替代无意义 void;异常在方法内捕获并安全返回。 在用户要求规范化方法、统一工具类写法、整理 Javadoc/KDoc、或按 DevUtils 方法风格处理时使用。

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schedule Updated 1 month ago
afkT

gradle-third-party-version-upgrade

by afkT
star 1.6k

对 DevUtils 工程(DEPS_ROOT=file/gradle、DEPS_MANIFEST=file/deps)中定义的第三方库 GAV 依赖做版本查证与升级;结合 Maven Central、Google Maven、Gradle Plugin Portal、 JitPack 与 GitHub Releases/README 交叉校验「最新可用版本」;必要时修正 group/artifact、 同步 `{DEPS_ROOT}/versions.gradle` 中集中版本号,并为缺失的开源地址补全注释。在用户要求 升级依赖、检查过时库版本、对齐 config/deps 与中央仓库或 GitHub 发布时使用。

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schedule Updated 1 month ago
Page 1 of 2

Browse Agent Skills by Occupation

23 major groups · 867 SOC occupations

Browse by Category

Explore agent skills organized by their primary use case

SKILLMD / CREATORS AND OCCUPATION CATEGORIES

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.

SEO KNOWLEDGE HUB & TECHNICAL OVERVIEW

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.

8 QUESTIONS

Frequently Asked Questions

A practical guide to agent skills: what they are, how to inspect them, and how SkillMD helps you explore the ecosystem.