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 JairoTorregrosa/jaiskills --skill image-to-frontendgit clone --depth 1 https://github.com/JairoTorregrosa/jaiskillsWrote 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/jairotorregrosa/jaiskills/image-to-frontend)<a href="https://agentmods.dev/skills/jairotorregrosa/jaiskills/image-to-frontend"><img src="https://agentmods.dev/badge/skills/jairotorregrosa/jaiskills/image-to-frontend/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/jairotorregrosa/jaiskills/image-to-frontend"><img src="https://agentmods.dev/badge/skills/jairotorregrosa/jaiskills/image-to-frontend.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 | $0.00125 | $0.01560 |
| Opus 5 | $0.00063 | $0.00780 |
| Sonnet 5 | $0.00025 | $0.00312 |
| Haiku 4.5 | $0.00013 | $0.00156 |
Grade A, and why
image-to-frontend 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 12d 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 — 90 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Image → Frontend (gpt-image via askcodex)
End-to-end workflow: brief → 4 visual variants → chosen variant → detailed build spec → real frontend code → iterate to pixel-close. Powered by the askcodex CLI (askcodex image create / askcodex image edit) from the askcodex repo. No API key needed — askcodex reuses the credentials codex login already stored.
GI2 is genuinely strong: photorealistic web/mobile app screenshots, accurate text rendering, world knowledge, identity preservation across edits. But ~3 in 4 attempts fail to closely replicate a complex reference without strategy. Follow the steps; do not shortcut.
Step 1 — Brief
Collect from the user (or draft and confirm):
- Business / product name and one-line pitch
- Audience and tone (expert + grounded? playful? minimal?)
- Primary CTAs (book a call, sign up, try demo)
- Aesthetic preferences, palette, vibe references
- Type (SaaS dashboard, marketplace landing, personal site, mobile app, infographic)
- Existing brand assets, icons, color tokens (paths)
Ambiguity is fine in step 2 — it surfaces options. Explicit direction massively improves variant quality.
Step 2 — Generate 4 stylistic variants
Fire 4 askcodex image create commands in parallel (background Bash calls in one message), each with a distinct visual direction (e.g. editorial-serif, bento grid, glass-morphism, brutalist-mono). Same brief, different style. Save to mockups/v1.png … mockups/v4.png.
Use the prompt template in examples/variant-prompt.md as the quoted prompt: askcodex image create "<prompt>" -o mockups/v1.png. Verify outputs with file mockups/v*.png. Read each PNG with the Read tool to view, then show the variants to the user.
Step 3 — Image → detailed build spec
Once the user picks a variant, do not jump straight to "build it." Generate a meticulous build spec from the chosen image. Pass the PNG to a strong code/vision model and ask it to enumerate:
What ships with it
6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 12d ago First seen · 90 lines · 125 tokens per session scan A 0e5568298ffa
image-to-frontend is a skill published in the GitHub repository JairoTorregrosa/jaiskills (5 stars, last pushed 11d ago), licensed MIT. It adds 125 tokens to every session and 1,560 once invoked, about $0.0006 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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