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.
git clone --depth 1 https://github.com/lorena-bordonaba-pau/product-studioWrote 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/commands/lorena-bordonaba-pau/product-studio/product-image)<a href="https://agentmods.dev/commands/lorena-bordonaba-pau/product-studio/product-image"><img src="https://agentmods.dev/badge/commands/lorena-bordonaba-pau/product-studio/product-image/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/commands/lorena-bordonaba-pau/product-studio/product-image"><img src="https://agentmods.dev/badge/commands/lorena-bordonaba-pau/product-studio/product-image.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 | $0.00035 | $0.00527 |
| Opus 5.5 | $0.00014 | $0.00211 |
| Sonnet 5.5 | $0.00007 | $0.00105 |
| Haiku 4.5 | $0.00003 | $0.00053 |
Grade A, and why
product-image 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 2mo 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 — 31 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/product-image — Professional product photography
Generates a set of professional product photos from the user's real photos. Always reply in the user's language.
Source provided by the user: $ARGUMENTS (If empty, ask whether they have a photo folder or their store's URL.)
Instructions
Follow the full flow defined in the product-photography skill (the product-studio plugin's
SKILL.md), section "Workflow: images". In short, skipping none:
- Check the user's config (
~/.claude/product-studio/config.json); if missing, walk them through /product-setup first. - Get the source images (local folder, or URL extraction + quick brand analysis — see
references/store-analysis.md) and confirm the detected products. - Build the strict inventory of each product by reading its photos — only what is clearly visible.
- Ask in ONE AskUserQuestion: style (minimalist / elegant / UGC — see
references/styles.md), quantity (3 or 5) and destination/format (ecommerce, feed, stories, web — seereferences/shot-plans.md). - Load the company's session template (
sessions/<company>.md) or draft one brand-matched to the client and confirm it with the user before generating — all photos for one brand share per-shot settings, light, palette and format. - Check the category-adaptive shot plan (
references/shot-plans.md) and the local learnings (~/.claude/product-studio/learnings.md). - Generate image-to-image with the configured provider (
references/providers.md), always using the original photo as reference (the one whose angle best matches the shot), with realistic photographic prompts (references/styles.md). - Run the consistency QA on every image against the inventory, the session template AND the AI-artifact checklist; fix mismatches (max 3 attempts) and record the learnings.
- Deliver to
output/<product>/with a final summary of what was generated and verified.
Remember the non-negotiable principle: total product fidelity. Never invent or elaborate on what can't be seen; never silently deliver an image that isn't identical to the real product.
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.
- 2mo ago First seen · 31 lines · 35 tokens per session scan A 62a3eb14bbc8
product-image is a command published in the GitHub repository lorena-bordonaba-pau/product-studio (6 stars, last pushed 4mo ago), licensed MIT. It adds 35 tokens to every session and 527 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.
product-image compared with commands from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| product-imagelorena-bordonaba-pau/product-studio | Command | 6 | 4mo ago | MIT | A | 35 | Claude Code |
| devvendurehq/vendure | Command | 8.6k | yesterday | none | A | 0 | Claude Code |
| startvendurehq/vendure | Command | 8.6k | yesterday | none | A | 0 | Claude Code |
| sync-medusa-docs-changesmedusajs/medusa-agent-skills | Command | 235 | 3d ago | none | A | 0 | GitHub Copilot |
| validate-ideakhadinakbarlabs/shopify-app-builder | Command | 2 | 27d ago | MIT | A | 13 | Claude Code |
| ebay-ordersjbwashington/ebay-mcp | Command | 17 | 8mo ago | MIT | A | 8 | Claude Code |
| primerlroy-stack/ai-pod-store | Command | 1 | 12d ago | none | A | 0 | Claude Code |