adapt

adapt is a skill for Claude Code, Codex from qinye6/pi-ccg. It costs 51 tokens per session (1,526 once invoked), scanned A, a copy of adapt, MIT.

A responsive-design guide for adjusting interfaces to different screen sizes, devices, input methods, and usage situations.

In plain words
What is it for?
Use it to plan breakpoints, fluid layouts, touch targets, and adaptations for phones, tablets, desktops, and other platforms.
Why use it?
It helps prevent layouts, content, and controls from working on one device but becoming cramped or difficult to use on another.

Skill for Claude CodeCodex

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.

agentmods
npx agentmods add skills/qinye6/pi-ccg/adapt
Any agent
npx skills add qinye6/pi-ccg --skill adapt
Clone the repo
git clone --depth 1 https://github.com/qinye6/pi-ccg

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 adapt

README.md
[![agentmods](https://agentmods.dev/badge/skills/qinye6/pi-ccg/adapt.svg)](https://agentmods.dev/skills/qinye6/pi-ccg/adapt)
Your own site
<a href="https://agentmods.dev/skills/qinye6/pi-ccg/adapt"><img src="https://agentmods.dev/badge/skills/qinye6/pi-ccg/adapt.svg" alt="Measured on agentmods" height="20"></a>
Per session 51 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,526 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin 98% copy Near-identical to another mod 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 $0.00051 $0.01526
Opus 5 $0.00026 $0.00763
Sonnet 5 $0.00010 $0.00305
Haiku 4.5 $0.00005 $0.00153

Measured 5d ago against content hash 38b3abdf6066, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

adapt 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 5d 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.

Origin

This is a copy

98% identical to adapt — 2 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

templates/skills/impeccable/adapt/SKILL.md · 200 lines

How it starts

The opening of the file, as written. The whole thing — 200 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Adapt existing designs to work effectively across different contexts - different screen sizes, devices, platforms, or use cases.

MANDATORY PREPARATION

Invoke /frontend-design — it contains design principles, anti-patterns, and the Context Gathering Protocol. Follow the protocol before proceeding — if no design context exists yet, you MUST run /teach-impeccable first. Additionally gather: target platforms/devices and usage contexts.


Assess Adaptation Challenge

Understand what needs adaptation and why:

  1. Identify the source context:

    • What was it designed for originally? (Desktop web? Mobile app?)
    • What assumptions were made? (Large screen? Mouse input? Fast connection?)
    • What works well in current context?
  2. Understand target context:

    • Device: Mobile, tablet, desktop, TV, watch, print?
    • Input method: Touch, mouse, keyboard, voice, gamepad?
    • Screen constraints: Size, resolution, orientation?
    • Connection: Fast wifi, slow 3G, offline?
    • Usage context: On-the-go vs desk, quick glance vs focused reading?
    • User expectations: What do users expect on this platform?
  3. Identify adaptation challenges:

    • What won't fit? (Content, navigation, features)
    • What won't work? (Hover states on touch, tiny touch targets)
    • What's inappropriate? (Desktop patterns on mobile, mobile patterns on desktop)

CRITICAL: Adaptation is not just scaling - it's rethinking the experience for the new context.

Plan Adaptation Strategy

Create context-appropriate strategy:

Mobile Adaptation (Desktop → Mobile)

Layout Strategy:

  • Single column instead of multi-column
  • Vertical stacking instead of side-by-side
  • Full-width components instead of fixed widths
  • Bottom navigation instead of top/side navigation

Interaction Strategy:

  • Touch targets 44x44px minimum (not hover-dependent)
  • Swipe gestures where appropriate (lists, carousels)
  • Bottom sheets instead of dropdowns
  • Thumbs-first design (controls within thumb reach)
  • Larger tap areas with more spacing

Read the full file on GitHub · 200 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. 5d ago First seen · 200 lines · 51 tokens per session scan A 38b3abdf6066

Subscribe to this mod's changes

adapt is a skill published in the GitHub repository qinye6/pi-ccg (10 stars, last pushed 10d ago), licensed MIT. It adds 51 tokens to every session and 1,526 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 98% identical to adapt, differing in 2 lines, and is treated as a copy.

Related

Other skills, from other repositories

systematic-debugging

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.

obra/superpowers · 21 tokens

local-ai-agents

Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…

microsoft/ai-agents-for-beginners · 200 tokens

chronicle

Analyze Copilot session history for standup reports, usage tips, session search, and session reindexing. Use when the user asks for a standup, daily summary, usage tips, workflow recommendations, wants to search or find past sessions by keyword/file/PR, wants to reindex their session store, or asks about deleting…

microsoft/vscode · 72 tokens

imagegen

Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output…

openai/codex · 113 tokens

chat-pet-sprite-creation

Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.

microsoft/vscode · 53 tokens

cpu-profile-analysis

Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…

microsoft/vscode · 71 tokens