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 zubair-trabzada/ai-restaurant-claude --skill restaurant-photosgit clone --depth 1 https://github.com/zubair-trabzada/ai-restaurant-claudeWrote 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/zubair-trabzada/ai-restaurant-claude/restaurant-photos)<a href="https://agentmods.dev/skills/zubair-trabzada/ai-restaurant-claude/restaurant-photos"><img src="https://agentmods.dev/badge/skills/zubair-trabzada/ai-restaurant-claude/restaurant-photos/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/zubair-trabzada/ai-restaurant-claude/restaurant-photos"><img src="https://agentmods.dev/badge/skills/zubair-trabzada/ai-restaurant-claude/restaurant-photos.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 | $0.00029 | $0.02543 |
| Opus 5 | $0.00015 | $0.01272 |
| Sonnet 5 | $0.00006 | $0.00509 |
| Haiku 4.5 | $0.00003 | $0.00254 |
Grade A, and why
restaurant-photos 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 — 263 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Food Photography Audit & Shot List
You audit a restaurant's existing food photography across Google Business Profile, Yelp, Instagram, website, and delivery platforms — then produce a concrete shot list of what's missing, what to reshoot, and how to brief a photographer.
DISCLAIMER: AI-generated audit and creative direction.
When to use
/restaurant photos <name>— full photo audit + shot list- "do I need new food photos"
- "photography brief for [name]"
Why Photography Matters (Revenue Math)
Industry data:
- GBP listings with 30+ photos get 2x clicks vs listings with 10
- Instagram posts with food shots > 1k followers convert at 5-7% vs lifestyle shots at 2-3%
- DoorDash menu items with photos sell 30-50% more than items without
- 60% of diners say they choose restaurants based on Instagram photos
A typical 20-dish photo shoot ($1,500-$3,000) pays back in 8-14 days at a 30-cover/day restaurant.
Execution Pipeline
Step 1: Inventory Existing Photos
For each platform, capture:
- GBP photos — count, categories (food/interior/team/exterior/menu), oldest, newest
- Yelp photos — same
- Instagram grid — last 30 posts, % food vs other
- Website hero / menu photos
- Delivery platforms — % of items with photos
Step 2: Identify Missing Photos
Compare against menu. For each menu item, note:
- Has photo on website? Y/N
- Has photo on GBP? Y/N
- Has photo on DoorDash/Uber Eats? Y/N
- Has been featured on Instagram in last 90 days? Y/N
Items that need photos most urgently:
- Signature / Star items — your highest-margin best-sellers
- Puzzles (low pop, high margin) — photos lift these the most
- New menu additions — never had a photo
- Seasonal items — photos go stale
Step 3: Audit Photo Quality
For each existing photo, score 1-10:
- Lighting: natural soft light? No harsh overhead fluorescent?
- Composition: rule of thirds? Negative space? Story?
- Color: food colors pop? No yellow cast from incandescent?
- Styling: clean plate edges? Garnishes intentional? Linens/textures?
- Angle: appropriate to the dish (overhead for pizzas/bowls, 3/4 for height items like burgers)?
- Authenticity: looks like the actual dish guests receive?
- Consistency: matches other photos in style?
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 · 263 lines · 29 tokens per session scan A 06bd255c5dbf
restaurant-photos is a skill published in the GitHub repository zubair-trabzada/ai-restaurant-claude (26 stars, last pushed 3mo ago), licensed MIT. It adds 29 tokens to every session and 2,543 once invoked, about $0.0001 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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