Launches 5 parallel AI agents to produce a comprehensive restaurant audit with composite Restaurant Health Score (0-100), grade, and prioritized action plan
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Generate a professional PDF restaurant report by scanning RESTAURANT-*.md files in current directory and running the bundled Python ReportLab generator
The English summary is being prepared. Showing the original description.
Run score Not tested
See how to installReview the instructions the agent will receive before you install.
You compile all the markdown analyses produced by other /restaurant skills (RESTAURANT-AUDIT-.md, RESTAURANT-REVIEWS-.md, RESTAURANT-MENU-*.md, etc.) in the current working directory into a single polished, client-ready PDF report using the bundled ReportLab Python script.
DISCLAIMER: AI-generated report. Owner should review before sending to clients.
/restaurant report-pdf — generate PDF from existing markdown analysesList all RESTAURANT-*.md files in the cwd:
ls RESTAURANT-*.md
Recognize these files:
RESTAURANT-AUDIT-[Name].md — main audit (highest priority)RESTAURANT-REVIEWS-[Name].mdRESTAURANT-MENU-[Name].mdRESTAURANT-PRICING-[Name].mdRESTAURANT-ONLINE-[Name].mdRESTAURANT-PHOTOS-[Name].mdRESTAURANT-SOCIAL-[Name].mdRESTAURANT-SEO-[Name].mdRESTAURANT-ADS-[Name].mdRESTAURANT-EMAIL-[Name].mdRESTAURANT-COMPETITORS-[Name].mdRESTAURANT-RESPONSES-[Name].mdFrom each markdown file, extract:
Assemble into a single JSON payload like:
{
"restaurant_name": "Bella Italia Trattoria",
"city": "Austin, TX",
"cuisine": "Italian",
"date": "2026-05-20",
"overall_score": 64,
"categories": {
"Reviews & Reputation": {"score": 68, "weight": "25%"},
"Menu & Pricing": {"score": 72, "weight": "20%"},
"Online Presence": {"score": 55, "weight": "20%"},
"Marketing & Engagement": {"score": 48, "weight": "15%"},
"Local Competition": {"score": 70, "weight": "20%"}
},
"reviews": {...},
"menu": {...},
"online": {...},
"competitors": [...],
"action_plan": [...]
}
Save extracted data to /tmp/restaurant_data.json.
python3 ~/.claude/skills/restaurant/scripts/generate_restaurant_pdf.py /tmp/restaurant_data.json RESTAURANT-REPORT.pdf
Verify RESTAURANT-REPORT.pdf exists in cwd. Report path back to user.
If no RESTAURANT-*.md files are present, do one of:
Option A: Demo mode
python3 ~/.claude/skills/restaurant/scripts/generate_restaurant_pdf.py --demo
Generates RESTAURANT-REPORT-sample.pdf with sample data.
Option B: Prompt the user
Tell the user no analyses are present in the current directory, and suggest running /restaurant audit <name> first.
| Page | Content |
|---|---|
| 1 | Cover — restaurant name, city, cuisine, score gauge, grade, signal |
| 2 | Score dashboard — bar chart of 5 categories + table |
| 3 | Reviews & reputation — star ratings table, top complaints, top praises |
| 4 | Menu engineering — Kasavana matrix, item analysis, pricing |
| 5 | Online presence — GBP, Yelp, website, delivery audit |
| 6 | Marketing recommendations — social cadence, ad angles, email sequences |
| 7 | Competitor comparison — head-to-head scorecard, positioning gaps |
| 8 | 90-day action plan — Week 1 / Days 8-30 / Days 31-90 |
| 9 | Revenue opportunity summary + disclaimer |
When calling the script, you can override defaults via the JSON:
accent_color — defaults to warm red (#e74c3c)agency_name — defaults to "AI Restaurant Team"agency_logo_path — optional logo file pathclient_name — restaurant name (filename-safe)After running, confirm:
%PDF)Report back:
PDF generated: ./RESTAURANT-REPORT.pdf
Pages: 9
File size: 287 KB
Restaurant: Bella Italia Trattoria
Health Score: 64/100 (Grade: B — Average)
| Error | Cause | Fix |
|---|---|---|
reportlab not installed | Missing dependency | Run pip install reportlab |
JSON parsing error | Bad extraction from MD | Re-run with --demo to verify script works |
Permission denied | cwd not writable | Move to a writable directory |
No restaurant data found | No RESTAURANT-*.md files | Suggest running /restaurant audit first |
DISCLAIMER: AI-generated report. Owner should review before sending to clients.
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Launches 5 parallel AI agents to produce a comprehensive restaurant audit with composite Restaurant Health Score (0-100), grade, and prioritized action plan
The English summary is being prepared. Showing the original description.
Facebook/Instagram ad copy variations — 10+ ad copy variants across happy hour, lunch, weekend brunch, catering, birthday/anniversary angles with A/B testing framework
The English summary is being prepared. Showing the original description.
Top 5 local competitor analysis — menu, pricing, review, social, and SEO comparison with positioning gap identification
The English summary is being prepared. Showing the original description.
Email and SMS sequences — loyalty program emails, birthday emails, win-back campaigns, special event invites, abandoned cart for online orders
The English summary is being prepared. Showing the original description.
Local SEO audit — "best [cuisine] near me" targeting, Google Business Profile optimization, NAP consistency, citation building, restaurant schema markup
The English summary is being prepared. Showing the original description.
Menu engineering analysis — item placement, description quality, pricing psychology, upsell opportunities, photo presence, Kasavana matrix (Stars/Plowhorses/Puzzles/Dogs)
The English summary is being prepared. Showing the original description.