adapt

adapt is a skill for Claude Code from fengshao1227/ccg-workflow. It costs 51 tokens per session (1,526 once invoked), scanned A, original, MIT.

A set of instructions for making a design work across screen sizes, devices, platforms, or usage situations. It covers layouts, breakpoints, and touch-friendly controls.

In plain words
What is it for?
Use it to adapt desktop designs for mobile, support different screen sizes and input methods, and account for conditions such as slow connections or quick-glance use.
Why use it?
It helps avoid interfaces that fit one situation but become hard to use on phones, tablets, or other devices.

Skill for Claude Code

Written for Claude Code: user-invocable in frontmatter.

Part of the ccg plugin — 58 skills, 12 commands, 7 agents shipped together

Good fit Use it to adapt desktop designs for mobile, support different screen sizes and input methods, and account for conditions such as slow connections or quick-glance use.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/fengshao1227/ccg-workflow/adapt
About the project

CCG is a command-line workflow engine that coordinates Claude, Codex, Gemini, and other models as specialized collaborators on coding tasks. It is used to analyze requests, choose a strategy, delegate work to model-specific roles, and combine their results. The catalogue entries provide the skills, commands, agents, and plugin that implement this workflow.

fengshao1227/ccg-workflow · 5,942 stars · on GitHub · ccg.fengshao1227.com

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 fengshao1227/ccg-workflow --skill adapt
Clone the repo
git clone --depth 1 https://github.com/fengshao1227/ccg-workflow

Made for: Claude Code.

Or install ccg, the plugin that ships this one along with the rest of its 58 skills, 12 commands, 7 agents.

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/fengshao1227/ccg-workflow/adapt/github.svg)](https://agentmods.dev/skills/fengshao1227/ccg-workflow/adapt)
Your own site
<a href="https://agentmods.dev/skills/fengshao1227/ccg-workflow/adapt"><img src="https://agentmods.dev/badge/skills/fengshao1227/ccg-workflow/adapt/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 adapt

Your own site · 80×15
<a href="https://agentmods.dev/skills/fengshao1227/ccg-workflow/adapt"><img src="https://agentmods.dev/badge/skills/fengshao1227/ccg-workflow/adapt.svg" alt="Reviewed on agentmods" width="80" 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. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
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 $0.00051 $0.01526
Opus 5.5 $0.00020 $0.00610
Sonnet 5.5 $0.00010 $0.00305
Haiku 4.5 $0.00005 $0.00153

Measured 1mo ago against content hash 4cf9af744f17, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, 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 1mo 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

Copies of this mod

8 near-identical copies found in the catalogue:

  • adapt — 98% identical, 2 lines differ
  • adapt — 98% identical, 2 lines differ
  • adapt — 95% identical, 44 lines differ
  • adapt — 95% identical, 6 lines differ
  • adapt — 95% identical, 9 lines differ
  • adapt — 95% identical, 9 lines differ
  • adapt — 95% identical, 6 lines differ
  • adapt — 95% identical, 9 lines differ
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. 1mo ago First seen · 200 lines · 51 tokens per session scan A 4cf9af744f17

Subscribe to this mod's changes

adapt is a skill published in the GitHub repository fengshao1227/ccg-workflow (5,942 stars, last pushed today), licensed MIT. It adds 51 tokens to every session and 1,526 once invoked, about $0.0002 per session on Opus 5.5. 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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