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 calesthio/generative-media-skills --skill ecommerce-product-imagerygit clone --depth 1 https://github.com/calesthio/generative-media-skillsWrote 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/calesthio/generative-media-skills/ecommerce-product-imagery)<a href="https://agentmods.dev/skills/calesthio/generative-media-skills/ecommerce-product-imagery"><img src="https://agentmods.dev/badge/skills/calesthio/generative-media-skills/ecommerce-product-imagery/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/calesthio/generative-media-skills/ecommerce-product-imagery"><img src="https://agentmods.dev/badge/skills/calesthio/generative-media-skills/ecommerce-product-imagery.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00097 | $0.05667 |
| Opus 5 | $0.00048 | $0.02833 |
| Sonnet 5 | $0.00019 | $0.01133 |
| Haiku 4.5 | $0.00010 | $0.00567 |
Grade A, and why
ecommerce-product-imagery 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 — 461 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Ecommerce Product Imagery
Use this skill to produce commercially usable product imagery, not just attractive pictures. The governing rule is: the image must help a buyer understand the actual purchasable product without inventing features, quantities, sizes, colors, materials, packaging, certifications, results, endorsements, or included accessories.
Operating principles
Separate three categories before creating anything:
- Documented product fact: confirmed by physical sample, client PDP, packaging, SKU sheet, spec sheet, or approved brand guide.
- Creative direction: lighting, setting, camera, styling, props, layout, crop, visual hierarchy, and mood.
- Regulated or platform-sensitive claim: performance claims, health/beauty outcomes, environmental claims, comparative superiority, pricing, discounts, guarantees, safety, awards, certifications, shipping promises, and "before/after" implications.
Do not let category 2 change category 1. Do not render category 3 unless the client supplies substantiation and approves the exact wording and visual implication.
Label your own assertions while working:
- Documented fact: cite the source artifact or page.
- Production heuristic: explain that it is a practical rule of thumb.
- Volatile platform fact: include the date checked and tell the user it must be re-checked before upload or ad launch.
- Legal/compliance risk: do not give legal advice; escalate to client, counsel, or marketplace review.
First response workflow
For any ecommerce image request, collect or infer the minimum viable brief:
- Product identity: brand, product name, SKU/ASIN/GTIN, variant names, bundle quantity, packaging version, included accessories.
- Channel: Amazon, Shopify/PDP, Google Merchant Center/Shopping, Meta ads, TikTok ads, retail media, email, marketplace carousel, A+ content, social organic, print, or internal concepting.
- Deliverables: count, orientation, aspect ratio, transparent/white background, lifestyle, infographic, comparison, size chart, localized versions, ad versions, thumbnails.
- Source material: packshots, packaging flats, label art, CAD/3D files, brand guide, prior approved imagery, physical sample photos, model/property releases.
- Truth constraints: dimensions, colors, material finish, ingredients, claims, certifications, warranty, target market, restricted categories.
- Use of generation/editing: full generation, background replacement, compositing, cleanup, upscaling, virtual placement, virtual try-on, synthetic models, or concept-only.
- Approval path: who approves product accuracy, claims, legal/compliance, marketplace upload, and final art.
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 · 461 lines · 97 tokens per session scan A c3e8e9b885c5
ecommerce-product-imagery is a skill published in the GitHub repository calesthio/generative-media-skills (170 stars, last pushed 2mo ago), licensed MIT. It adds 97 tokens to every session and 5,667 once invoked, about $0.0005 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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