image-to-code-skill

A guide for faithfully turning a supplied screenshot, Figma frame, or approved visual reference into a web interface. It treats the reference as the specification and covers analysis before implementation.

In plain words
What is it for?
Use it to inspect the reference, record its structure and visual details, then build and compare the corresponding web interface.
Why use it?
It helps prevent accidental restyling, invented content, or loss of important layout and branding details during implementation.

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/zhpeng24/devkit/image-to-code-skill
Any agent
npx skills add zhpeng24/devkit --skill image-to-code-skill
Clone the repo
git clone --depth 1 https://github.com/zhpeng24/devkit

Made for: Claude Code, Codex.

Per session 31 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 579 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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 $0.00031 $0.00579
Opus 5 $0.00015 $0.00290
Sonnet 5 $0.00006 $0.00116
Haiku 4.5 $0.00003 $0.00058

Measured yesterday against content hash 03b275267c87, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

image-to-code-skill 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 yesterday.

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.

skills/image-to-code-skill/SKILL.md · 40 lines

How it starts

The opening of the file, as written. The whole thing — 40 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Image to Code

Treat the reference as a specification. A supplied screenshot, Figma frame, or approved image is authoritative; do not regenerate, restyle, or replace it merely because image generation is available.

If a request calls a reference approved or final but also asks to “capture the vibe,” modernize it, or skip comparison, surface the conflict. Keep the approved source authoritative unless the user explicitly changes that status; do not silently downgrade fidelity to meet a deadline.

Choose the source

Reference state Action
Supplied or approved Analyze and implement it faithfully.
No authoritative reference; visual exploration requested or materially useful Propose/generate the smallest useful reference set, then confirm it before treating it as implementation input.
Pure image delivery Route to imagegen-frontend-web.

Never invent business claims, data, testimonials, logos, or asset rights. Preserve existing brand assets and mark unknown content as placeholder content.

Analyze before coding

View each source at readable resolution. Record its viewport and inventory: section order; containers and grid; alignment and responsive behavior; typography; palette; spacing; radii; borders and shadows; imagery; controls; interactive, loading, empty, and error states. Reuse project assets/components when they match the source.

When a source lacks readable detail, request a clearer reference or create a fresh, task-scoped detail reference with the needed information. Do not crop the authoritative source and mistake the crop for new design authority.

Implement and compare

  1. Inspect the existing stack, routes, tokens, and dependencies. Implement in that stack; do not default to React, Next, Tailwind, or a replacement design system.
  2. Build geometry first, then type, color, spacing, imagery, controls, and states. Keep semantics, keyboard access, contrast, reduced motion, and responsive behavior intact.
  3. Render the target route at the source viewport and compare side-by-side or with an overlay. Fix measurable differences in layout, type scale/line breaks, spacing, color, borders, and assets. Check responsive variants deliberately rather than extrapolating from one viewport.
  4. Run relevant project build/lint/tests and report both command results and any fidelity gaps or unavailable assets.

Read the full file on GitHub · 40 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. yesterday First seen · 40 lines · 31 tokens per session scan A 03b275267c87

Subscribe to this mod's changes

image-to-code-skill is a skill published in the GitHub repository zhpeng24/devkit (2 stars, last pushed 1mo ago), licensed MIT. It adds 31 tokens to every session and 579 once invoked, about $0.0002 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 2026-08-31.

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