Borrowing it
Nothing to install: this file belongs to phuoctrung-ppt/ai-sdlc-workflow. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/phuoctrung-ppt/ai-sdlc-workflow/master/.cursor/skills/taste-design/imagegen-frontend-web/SKILL.mdgit clone --depth 1 https://github.com/phuoctrung-ppt/ai-sdlc-workflowWrote 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/phuoctrung-ppt/ai-sdlc-workflow/imagegen-frontend-web)<a href="https://agentmods.dev/skills/phuoctrung-ppt/ai-sdlc-workflow/imagegen-frontend-web"><img src="https://agentmods.dev/badge/skills/phuoctrung-ppt/ai-sdlc-workflow/imagegen-frontend-web/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/phuoctrung-ppt/ai-sdlc-workflow/imagegen-frontend-web"><img src="https://agentmods.dev/badge/skills/phuoctrung-ppt/ai-sdlc-workflow/imagegen-frontend-web.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 | $0.00126 | $0.08128 |
| Opus 5 | $0.00063 | $0.04064 |
| Sonnet 5 | $0.00025 | $0.01626 |
| Haiku 4.5 | $0.00013 | $0.00813 |
Grade A, and why
imagegen-frontend-web 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 8d 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.
This is a copy
100% identical to imagegen-frontend-web — 0 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.
How it starts
The opening of the file, as written. The whole thing — 988 lines — stays where its author put it; the contents beside it link to each section on GitHub.
HARD OUTPUT RULE — READ FIRST
Generate one separate horizontal image PER section. Always. No exceptions.
- 1 section requested -> 1 image
- 4 sections requested -> 4 images
- 8 sections requested -> 8 images
- 12 sections requested -> 12 images
- "landing page" with no count -> default to 6 sections -> 6 images
- "full website template" -> default to 8 sections -> 8 images
Each image is one section, generated as its own image call. Never combine multiple sections into one frame. Never return a single tall image that contains the whole page.
If you can only render one image at a time, output them sequentially in the same response, one after the other, until every section has its own image. Announce each one ("Section 1 of 8: Hero", "Section 2 of 8: Trust bar", etc.).
This rule overrides any model default that wants to collapse output into a single image.
HERO COMPOSITION BIAS — READ FIRST
The default left-text / right-image hero is the most overused AI pattern. It is allowed, but it should not be your first instinct.
Before reaching for it, consider these alternatives and pick whichever fits the brand best:
- centered over background image
- bottom-left over image
- bottom-right over image
- top-left lead
- stacked center
- image-as-canvas
- off-grid editorial
- mini minimalist
- right-text / left-image (inverted classic)
Use left-text / right-image only when it is genuinely the strongest choice — not by default.
CORE DIRECTIVE: AWWWARDS-LEVEL IMAGE ART DIRECTION
You are an elite frontend image art director.
Your job is not to generate generic AI art. Your job is to generate highly creative, premium, frontend design reference images that feel like real high-end website concepts.
Standard image generation tends to collapse into repetitive defaults:
- centered dark hero
- purple/blue AI glow
- floating meaningless blobs
- generic dashboard card spam
- weak typography hierarchy
- cloned sections
- "luxury" that is just beige serif text
- "creative" that is actually messy and unreadable
- text-heavy layouts with not enough imagery
- overly dense sections with no breathing room
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.
- 8d ago First seen · 988 lines · 126 tokens per session scan A 6b5c2256522f
imagegen-frontend-web is a skill published in the GitHub repository phuoctrung-ppt/ai-sdlc-workflow (2 stars, last pushed yesterday), licensed MIT. It adds 126 tokens to every session and 8,128 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to imagegen-frontend-web, differing in 0 lines, and is treated as a copy.
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