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
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实测评分 未实测
查看安装方法安装前可以查看交给 AI 代理的指令内容。
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
中文简介正在准备中。当前显示原文说明。
Facebook/Instagram ad copy variations — 10+ ad copy variants across happy hour, lunch, weekend brunch, catering, birthday/anniversary angles with A/B testing framework
中文简介正在准备中。当前显示原文说明。
Top 5 local competitor analysis — menu, pricing, review, social, and SEO comparison with positioning gap identification
中文简介正在准备中。当前显示原文说明。
Email and SMS sequences — loyalty program emails, birthday emails, win-back campaigns, special event invites, abandoned cart for online orders
中文简介正在准备中。当前显示原文说明。
Local SEO audit — "best [cuisine] near me" targeting, Google Business Profile optimization, NAP consistency, citation building, restaurant schema markup
中文简介正在准备中。当前显示原文说明。
Menu engineering analysis — item placement, description quality, pricing psychology, upsell opportunities, photo presence, Kasavana matrix (Stars/Plowhorses/Puzzles/Dogs)
中文简介正在准备中。当前显示原文说明。