Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/sanky369/vibe-building-skillsnpx agentmods add skills/sanky369/vibe-building-skills/product-photographyWrote 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/sanky369/vibe-building-skills/product-photography)<a href="https://agentmods.dev/skills/sanky369/vibe-building-skills/product-photography"><img src="https://agentmods.dev/badge/skills/sanky369/vibe-building-skills/product-photography/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/sanky369/vibe-building-skills/product-photography"><img src="https://agentmods.dev/badge/skills/sanky369/vibe-building-skills/product-photography.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 | $0.00156 | $0.01789 |
| Opus 5 | $0.00078 | $0.00894 |
| Sonnet 5 | $0.00031 | $0.00358 |
| Haiku 4.5 | $0.00016 | $0.00179 |
Grade A, and why
product-photography 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 — 135 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Product Photography
Turn a product description into a coherent shot list of prompt specs that generate commerce-grade product imagery. Prime rule: one product, one visual world. Every shot in a set shares the brand's lighting, palette, and treatment so listings, landing pages, and social posts read as one brand — a stunning hero next to a mismatched lifestyle shot is worse than two average matching ones.
When to use / when not to
Use for any imagery whose subject is a product: e-commerce listings, landing-page heroes, marketing shots, detail close-ups. Hand off instead when:
- The asset is a platform-sized post about the product (text overlays, promo
layouts) →
skills/creative/social-graphics - The user needs a logo/icon/pattern →
skills/creative/brand-asset - It's a general image with no product subject →
skills/creative/image-generation - No visual direction exists and multiple assets are coming →
skills/creative/creative-strategistfirst; consume its style block here - The product should move →
skills/creative/product-video(stills from this skill are its source frames)
Intake
Ask in one batch, only what's missing:
- Product — what is it, exactly? Materials, colors, distinguishing details. (If a real photo exists, ask for it as reference for accuracy.)
- Destination — listing, landing page, ads, social? (drives shot types and aspect ratios)
- Positioning — premium/luxury, accessible, playful, technical?
- Style anchor — Creative Direction Brief / style block, or brand colors?
- Automation —
FAL_API_KEYset, or deliver specs only?
Infer, don't ask: aspect ratios from destination (listing → 1:1, landing hero →
16:9, social feed → 4:5); resolution 2K default, 4K for heroes.
Don't stall: with product + destination known, assume the rest, label it, go.
Workflow
- Build the shot list. Map destination → shot types (decision rules):
- E-commerce listing → 1 clean shot + 1–2 detail shots (+ 1 lifestyle if the platform allows gallery images)
- Landing page → 1 hero shot + 1 lifestyle + 1 detail
- Social/ads → 1–2 lifestyle + 1 flat lay (product families) or hero
- "Just one image" → pick the single type that serves the stated use; say why
- Fix the shared treatment. Choose one lighting style and palette for the whole set from the style block (or infer from positioning: premium → dramatic or studio; approachable → natural golden hour; minimal brand → high-key). Every spec in the set uses it.
- Write one prompt spec per shot using the templates in
references/shot-library.md— read it now; it has the five shot-type templates plus the lighting/background/composition prompt language. Name colors in words and hex. Always state what must NOT appear (no watermark, no invented logos or label text unless the exact text is supplied, no clutter). - Variants. Hero and clean shots get 3–4 variants (
--num-images 3or distinct specs varying composition/background); detail and lifestyle shots get 2. The user picks winners. - Generate or deliver. If
FAL_API_KEY(orFAL_KEY) is set:
What ships with it
1 file 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 · 135 lines · 156 tokens per session scan A 66f845ce3f5c
product-photography is a skill published in the GitHub repository sanky369/vibe-building-skills (30 stars, last pushed 2mo ago), licensed MIT. It adds 156 tokens to every session and 1,789 once invoked, about $0.0008 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 2026-08-30.
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