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.
npx skills add agentlas-ai/agentlas-desktop --skill image-to-codegit clone --depth 1 https://github.com/agentlas-ai/agentlas-desktopWrote 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.
[](https://agentmods.dev/skills/agentlas-ai/agentlas-desktop/image-to-code)<a href="https://agentmods.dev/skills/agentlas-ai/agentlas-desktop/image-to-code"><img src="https://agentmods.dev/badge/skills/agentlas-ai/agentlas-desktop/image-to-code/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.
<a href="https://agentmods.dev/skills/agentlas-ai/agentlas-desktop/image-to-code"><img src="https://agentmods.dev/badge/skills/agentlas-ai/agentlas-desktop/image-to-code.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00030 | $0.00712 |
| Opus 5.5 | $0.00012 | $0.00285 |
| Sonnet 5.5 | $0.00006 | $0.00142 |
| Haiku 4.5 | $0.00003 | $0.00071 |
Grade A, and why
image-to-code 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.
How it starts
The opening of the file, as written. The whole thing — 47 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill Purpose
Translates the pinned visual target design (assets/target-design.png) into a fully interactive, responsive frontend prototype by continuously referencing the pinned image, extracting design tokens, and reproducing every layout detail identically.
Preconditions
- A pinned visual target image (
assets/target-design.pngor equivalent screenshot/Figma node) must be available. Do not begin implementation from a text-only brief. - Adhere to
$critical-overridesand$qa-rubric.
Steps
- Load & Inspect Pinned Target:
- Open and continuously reference
assets/target-design.pngthroughout the entire implementation process. - Inspect exact typography scale (sizes, optical weights, letter-spacing), color tokens (backgrounds, accents, borders), and layout grid/spacing metrics directly from the image.
- Open and continuously reference
- Catalog & Produce Real Assets:
- Catalog all raster assets (hero imagery, avatars, product illustrations, badges) visible in the pinned design.
- Generate high-resolution assets using Image Gen matching the exact art direction and aspect ratios.
- Never replace image assets with handcrafted CSS shapes, div drawings, or emoji text glyphs. Use clean SVG icon libraries for standard icons.
- Scaffold & Build Frontend Code:
- Scaffold the prototype matching the intended form factor (mobile app 390x844 or desktop web).
- Write clean, modular HTML/CSS/JS or framework code faithfully matching the typography, colors, and layout rhythm of the pinned image.
- Implement functional interactive elements: navigation, tabs, inputs, filters, buttons, modal dialogs, and visible UI states (hover, focus, active, loading).
- Launch Local Preview Server:
- Start the local dev server to render the prototype in a real browser environment.
- Continuous 1:1 Side-by-Side QA (
$design-qa):- Capture live browser screenshots of the rendered prototype (
implementation.png). - Run side-by-side comparison against
assets/target-design.pngacross the 5 required fidelity surfaces. - Remediate all visual discrepancies (P0, P1, P2) in code until
design-qaproducesfinal result: passed.
- Capture live browser screenshots of the rendered prototype (
- Handoff: Keep the local development server active and present the completed, verified prototype to the user.
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.
- 1mo ago Changed · +4 lines de9ef61be6f8
- 1mo ago First seen · 43 lines · 30 tokens per session scan A 223bebe70f9c
image-to-code is a skill published in the GitHub repository agentlas-ai/agentlas-desktop (6 stars, last pushed yesterday), licensed Apache-2.0. It adds 30 tokens to every session and 712 once invoked, about $0.0001 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 2026-08-31.
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