mobile-adaptation

mobile-adaptation is a skill for Claude Code, Codex from kangarooking/system-prompt-skills. It costs 90 tokens per session (1,669 once invoked), scanned A, original, MIT.

Mobile adaptation is a set of response rules for AI used on phones and tablets. It adjusts answer length, structure, and formatting to fit smaller screens.

In plain words
What is it for?
Use it to design mobile AI prompts with short answers, scan-friendly lists, screen-aware length limits, and mobile tools such as calendars or location.
Why use it?
Long answers and wide formats are harder to read on mobile devices, where users need the key result quickly.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to design mobile AI prompts with short answers, scan-friendly lists, screen-aware length limits, and mobile tools such as calendars or location.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/kangarooking/system-prompt-skills/mobile-adaptation
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add kangarooking/system-prompt-skills --skill mobile-adaptation
Clone the repo
git clone --depth 1 https://github.com/kangarooking/system-prompt-skills

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for mobile-adaptation

README.md
[![agentmods](https://agentmods.dev/badge/skills/kangarooking/system-prompt-skills/mobile-adaptation/github.svg)](https://agentmods.dev/skills/kangarooking/system-prompt-skills/mobile-adaptation)
Your own site
<a href="https://agentmods.dev/skills/kangarooking/system-prompt-skills/mobile-adaptation"><img src="https://agentmods.dev/badge/skills/kangarooking/system-prompt-skills/mobile-adaptation/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for mobile-adaptation

Your own site · 80×15
<a href="https://agentmods.dev/skills/kangarooking/system-prompt-skills/mobile-adaptation"><img src="https://agentmods.dev/badge/skills/kangarooking/system-prompt-skills/mobile-adaptation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 90 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,669 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00090 $0.01669
Opus 5 $0.00045 $0.00834
Sonnet 5 $0.00018 $0.00334
Haiku 4.5 $0.00009 $0.00167

Measured 11d ago against content hash a52724a1d93a, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

mobile-adaptation scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

mobile-adaptation/SKILL.md · 74 lines

How it starts

The opening of the file, as written. The whole thing — 74 lines — stays where its author put it; the contents beside it link to each section on GitHub.

移动端适配

R — 原文 (Reading)

Claude Mobile iOS 基于屏幕尺寸设定响应层级:手机一次显示 6-8 句话,简单问题 1-2 句、操作指南短列表、实质问题 2-3 段、复杂问题不超过 2 屏。集成移动原生工具(日历、提醒、位置、图表)。Claude for Word 禁止管道分隔的 Markdown 表格(任务窗格太窄)。Gemini 实现移动端专项输出压缩。核心模式:屏幕尺寸响应分级、移动原生工具集成、格式限制、答案优先策略。

I — 方法论骨架 (Interpretation)

  1. 屏幕尺寸感知分级:根据目标设备屏幕容量将回答分为 4 个层级,每个层级有明确的长度上限(句数、段数或屏数)。
  2. 答案优先策略:移动端用户注意力碎片化,回答结构必须"结论先行、细节后置",禁止铺垫性开场白。
  3. 格式限制清单:在窄屏场景中禁用特定格式——管道表格、深层嵌套列表、宽代码块、大段引用。
  4. 移动原生工具集成:利用移动设备独有能力(日历、提醒事项、地理位置、本地时间、图表显示)增强交互。
  5. 扫描友好结构:使用短列表、加粗关键词、分段标题等格式,使用户在 3-5 秒内定位核心信息。

A1 — 案例分析 (Past Application)

案例: Claude Mobile iOS 的四层响应分级

  • 问题: 移动端屏幕一次只能显示 6-8 句话,过长的回答需要大量滚动,严重影响移动场景下的信息获取效率。
  • 设计模式的使用: Claude Mobile iOS 将回答分为四个层级并设定严格长度约束——简单问题 1-2 句话直接回答,操作指南用最短列表,实质性问题 2-3 段,复杂问题不超过 2 个屏幕。所有层级均遵循"先给答案、无前言"原则。
  • 结论: 基于物理屏幕约束的量化分级比模糊的"尽量简短"指令有效得多,为模型提供了可执行的长度标准。

案例: Claude for Word 的表格格式禁令

  • 问题: Word 插件的任务窗格宽度极窄(约 300-400px),管道分隔的 Markdown 表格会溢出或折行混乱。
  • 设计模式的使用: Claude for Word 明确禁止在聊天中使用管道分隔的 Markdown 表格("No pipe-delimited markdown tables in chat"),改用结构化列表或自然语言描述替代。
  • 结论: 格式限制需要具体到特定的 Markdown 语法元素,泛化的"注意格式"指令无法精准解决窄屏适配问题。

A2 — 触发场景 (Future Trigger) ★

用户在什么情境下需要?

  1. 设计手机 App 内嵌 AI 助手的系统提示
  2. 优化现有桌面端系统提示以适配移动端
  3. 构建跨平台 AI 产品,需针对不同屏幕尺寸差异化输出
  4. 开发集成移动原生功能(日历、位置)的 AI 助手

语言信号

  • "移动端用户"
  • "手机屏幕上显示"
  • "小屏幕适配"
  • "需要集成日历/提醒/定位"
  • "App 内的 AI 助手"

与相邻 skill 的区分

  • voice-optimization 区别:语音优化关注听觉通道,移动适配关注视觉通道的物理约束;但两者共享简洁优先理念
  • citation-system 区别:引用在移动端需要特殊展示(如简化标记、折叠引用),但移动适配不涉及引用格式设计本身

E — 可执行步骤 (Execution)

  1. 步骤 1:定义屏幕响应分级表 - 完成标准:基于目标设备屏幕容量,定义 4 级响应策略(简单/操作/中等/复杂),每级规定最大句数、段数或屏数,并附具体示例。
  2. 步骤 2:编写格式限制清单 - 完成标准:列出在移动端禁止使用的格式类型(管道表格、深层嵌套列表、超过 60 字符的代码行等),并为每种禁止格式提供替代方案(表格→结构化列表、嵌套列表→扁平列举)。
  3. 步骤 3:设计答案优先输出结构 - 完成标准:在系统提示中声明"结论先行"原则,规定回答结构为:直接答案 → 关键细节 → 可选扩展,并禁止铺垫性开场白。
  4. 步骤 4:规划移动原生工具集成点 - 完成标准:列出可调用的移动原生能力(日历创建、提醒设置、位置查询、时间获取),为每个能力定义触发条件和调用格式。
  5. 步骤 5:添加扫描友好格式规范 - 完成标准:规定移动端输出的格式增强规则——关键信息加粗、列表项不超过一行、段落间空行分隔、使用 emoji 前缀(如适用)提升视觉扫描效率。

Read the full file on GitHub · 74 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 11d ago First seen · 74 lines · 90 tokens per session scan A a52724a1d93a

Subscribe to this mod's changes

mobile-adaptation is a skill published in the GitHub repository kangarooking/system-prompt-skills (183 stars, last pushed 4mo ago), licensed MIT. It adds 90 tokens to every session and 1,669 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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