restaurant-report-pdf

Restaurant PDF Report Generator

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.

When to use

/restaurant report-pdf — generate PDF from existing markdown analyses
"make a PDF of the restaurant audit"
"client-ready report for [name]"

Execution Pipeline

Step 1: Scan Current Directory

List all RESTAURANT-*.md files in the cwd:

ls RESTAURANT-*.md

Recognize these files:

RESTAURANT-AUDIT-[Name].md — main audit (highest priority)
RESTAURANT-REVIEWS-[Name].md
RESTAURANT-MENU-[Name].md
RESTAURANT-PRICING-[Name].md
RESTAURANT-ONLINE-[Name].md
RESTAURANT-PHOTOS-[Name].md
RESTAURANT-SOCIAL-[Name].md
RESTAURANT-SEO-[Name].md
RESTAURANT-ADS-[Name].md
RESTAURANT-EMAIL-[Name].md
RESTAURANT-COMPETITORS-[Name].md
RESTAURANT-RESPONSES-[Name].md

Step 2: Extract Key Data

From each markdown file, extract:

Restaurant name (from filename or top-of-file)
Date
Score (if applicable)
Top findings
Top recommendations
Tables of data

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": [...]
}

Step 3: Write Temp JSON

Save extracted data to /tmp/restaurant_data.json.

Step 4: Run PDF Generator

python3 ~/.claude/skills/restaurant/scripts/generate_restaurant_pdf.py /tmp/restaurant_data.json RESTAURANT-REPORT.pdf

Step 5: Confirm Output

Verify RESTAURANT-REPORT.pdf exists in cwd. Report path back to user.

If No Markdown Files Exist

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.

PDF Structure (what the bundled script produces)

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

Customizations Available

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 path
client_name — restaurant name (filename-safe)

Output Validation

After running, confirm:

File exists
File size > 50KB (smaller = error)
File is valid PDF (first 4 bytes = %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 Handling

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