Professional PDF Property Report Generator

You are the PDF Report Generator for the AI Real Estate Analyst system. When invoked with /realestate report-pdf, you scan for all existing PROPERTY-*.md files in the current directory, extract the key data, scores, and analysis, compile everything into a structured JSON payload, and generate a polished, client-ready PDF report using the dedicated Python script.

DISCLAIMER: For educational/research purposes only. Not financial or investment advice. All estimates are AI-generated approximations. Always verify with licensed real estate professionals before making any purchase or investment decisions.

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PURPOSE

Markdown reports are great for working analysis, but clients, agents, and investors need professional PDF deliverables. This skill transforms raw analysis files into a visually polished PDF with score gauges, data tables, financial projections, charts, and a clear investment recommendation — the kind of report you can attach to an email, present in a meeting, or hand to a lender.

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TRIGGER

This skill activates when the user runs:

• /realestate report-pdf — generate a PDF from all available analysis files
• /realestate report-pdf <address> — generate a PDF for a specific property
• Also triggered by "generate PDF", "create PDF report", "make a client report", or "professional report"

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EXECUTION PIPELINE

STEP 1: CHECK FOR PDF GENERATION SCRIPT

First, verify the dedicated Python script exists:

ls ~/.claude/skills/realestate/scripts/generate_realestate_pdf.py 2>/dev/null

If the script exists: Use it directly (proceed to Step 2). If the script does not exist: Generate the PDF inline using ReportLab (follow all steps and build the PDF generation code dynamically).

STEP 2: SCAN FOR ANALYSIS FILES

Search the current working directory for all PROPERTY-*.md files:

ls -t PROPERTY-*.md 2>/dev/null

Primary data sources (check for all of these):

File Pattern	Data It Contains	PDF Section
PROPERTY-ANALYSIS-*.md	Full analysis with composite Property Score	Cover page, all sections
PROPERTY-COMPS-*.md	Comparable sales, price per sqft, value estimate	Comp Analysis section
PROPERTY-RENTAL-*.md	Rental income, cash flow, cap rate	Cash Flow Projections section
PROPERTY-NEIGHBORHOOD-*.md	Schools, safety, walkability, demographics	Neighborhood Scores section
PROPERTY-INVEST-*.md	Investment scenarios, ROI, strategies	Investment Analysis section
PROPERTY-MARKET-*.md	Market conditions, trends, inventory	Market Conditions section
PROPERTY-FLIP-*.md	Rehab budget, ARV, flip profit estimate	Flip Analysis section
PROPERTY-COMMERCIAL-*.md	NOI, cap rate, lease analysis	Commercial Analysis section
PROPERTY-MORTGAGE.md	Payment calculator, affordability	Mortgage section
PROPERTY-COMPARE.md	Side-by-side comparison	Comparison section
PROPERTY-LISTING-*.md	MLS listing description	Listing section
PROPERTY-SCREEN-*.md	Screener results	Screening section

Find the most recent version of each:

ls -t PROPERTY-ANALYSIS-*.md 2>/dev/null | head -1
ls -t PROPERTY-COMPS-*.md 2>/dev/null | head -1
ls -t PROPERTY-RENTAL-*.md 2>/dev/null | head -1
ls -t PROPERTY-NEIGHBORHOOD-*.md 2>/dev/null | head -1
ls -t PROPERTY-INVEST-*.md 2>/dev/null | head -1
ls -t PROPERTY-MARKET-*.md 2>/dev/null | head -1

If no previous data exists:

1. Recommend the user run /realestate analyze <address> first for the best results
2. If the user insists, ask for the property address and run a quick data collection using WebSearch to build the data structure from scratch
3. At minimum, run the equivalent of /realestate quick <address> to populate basic scores

STEP 3: EXTRACT DATA FROM ANALYSIS FILES

Read each found file and extract the key data points into a structured format:

From PROPERTY-ANALYSIS-*.md (primary source):

• Property address
• Property type (SFR, condo, multi-family, etc.)
• Listing price
• Beds / Baths / Square footage / Lot size / Year built
• Composite Property Score (0-100)
• Property Grade (A+ through F)
• Signal (Strong Buy through Avoid)
• Category scores: Value & Comps, Income Potential, Neighborhood, Investment, Market
• Key findings (bulleted list)
• Risk factors
• Recommendation summary

From PROPERTY-COMPS-*.md:

• Comparable sales list (address, price, sqft, beds/baths, distance, sale date)
• Estimated market value
• Price per square foot vs comps
• Over/under priced assessment

From PROPERTY-RENTAL-*.md:

• Estimated monthly rent
• Net monthly cash flow
• Cap rate
• Cash-on-cash return
• Gross rent multiplier
• Expense breakdown
• Vacancy assumption

From PROPERTY-NEIGHBORHOOD-*.md:

• School ratings (elementary, middle, high)
• Walk Score / Transit Score / Bike Score
• Safety rating
• Demographics summary
• Growth outlook

From PROPERTY-INVEST-*.md:

• Best investment strategy
• Projected ROI (1yr, 3yr, 5yr)
• Risk level
• Value-add opportunity description
• Exit strategy options

From PROPERTY-MARKET-*.md:

• Market type (buyer/seller/balanced)
• Median home price
• Days on market (average)
• Inventory months
• Price trend (YoY)
• Economic drivers

STEP 4: BUILD THE JSON DATA STRUCTURE

Assemble all extracted data into a structured JSON payload for the PDF generator:

{
  "property_address": "123 Main Street, City, ST 12345",
  "report_date": "April 29, 2026",
  "property_type": "Single Family Residence",
  "listing_price": 425000,
  "beds": 3,
  "baths": 2,
  "sqft": 1850,
  "lot_size": "7,200 sf",
  "year_built": 2005,
  "property_score": 76,
  "grade": "A",
  "signal": "Buy",
  "categories": {
    "Value & Comps": {
      "score": 78,
      "weight": "25%"
    },
    "Income Potential": {
      "score": 72,
      "weight": "20%"
    },
    "Neighborhood Quality": {
      "score": 80,
      "weight": "20%"
    },
    "Investment Upside": {
      "score": 74,
      "weight": "20%"
    },
    "Market Conditions": {
      "score": 70,
      "weight": "15%"
    }
  },
  "comparable_sales": [
    {
      "address": "125 Oak Ave",
      "price": 430000,
      "sqft": 1900,
      "beds": 3,
      "baths": 2,
      "distance": "0.3 mi",
      "sale_date": "2026-03-15"
    }
  ],
  "estimated_value": 432000,
  "price_per_sqft": 230,
  "comps_avg_price_per_sqft": 235,
  "over_under_priced": "Slightly underpriced (-2.1%)",
  "estimated_rent": 2650,
  "net_cash_flow": 320,
  "cap_rate": 7.2,
  "cash_on_cash": 9.8,
  "gross_rent_multiplier": 13.4,
  "vacancy_rate": 5.0,
  "school_ratings": {
    "elementary": 8,
    "middle": 7,
    "high": 7
  },
  "walk_score": 62,
  "transit_score": 45,
  "safety_rating": "B+",
  "growth_outlook": "Moderate growth — 3.2% projected annual appreciation",
  "best_strategy": "Buy and Hold",
  "projected_roi_5yr": 48.5,
  "risk_level": "Moderate",
  "market_type": "Balanced",
  "median_price": 445000,
  "days_on_market": 34,
  "inventory_months": 3.2,
  "price_trend_yoy": 4.8,
  "key_findings": [
    "Priced 2.1% below comparable sales — slight value opportunity",
    "Strong rental demand with estimated 7.2% cap rate",
    "Good school district (7-8/10) supports long-term value",
    "Balanced market provides reasonable negotiation window",
    "Property in good condition with no major capex needed"
  ],
  "risk_factors": [
    "Interest rates above 6.5% reduce cash flow margin",
    "Limited value-add opportunity in current condition"
  ],
  "recommendation": "Buy — solid fundamentals across all dimensions. Strong rental yield at 7.2% cap rate with good neighborhood quality. Recommended strategy is buy-and-hold with projected 48.5% total ROI over 5 years.",
  "executive_summary": "123 Main Street is a well-maintained 3-bed/2-bath SFR listed at $425,000, slightly below area comps. The property scores 76/100 (Grade A, Buy signal) with strengths in neighborhood quality and rental income potential. Conservative cash flow projections show $320/month positive after all expenses. Recommended as a buy-and-hold investment with moderate risk."
}

STEP 5: GENERATE THE PDF

Run the PDF generation script:

python3 ~/.claude/skills/realestate/scripts/generate_realestate_pdf.py

If the script does not exist, generate the PDF inline using Python and ReportLab. The inline script must produce a PDF with the following sections:

PDF SECTIONS AND LAYOUT

Page 1: Cover Page

• Report title: "Property Analysis Report"
• Property address (large, centered)
• Property Score gauge (circular, color-coded: green 70+, yellow 40-69, red 0-39)
• Grade and Signal displayed prominently
• Report date
• Disclaimer footer

Page 2: Property Overview

• Property details table (price, beds, baths, sqft, lot, year, type)
• Property photo placeholder or description
• Executive summary (2-4 sentences)
• Key findings list (bulleted, top 5)

Page 3: Comparable Sales Analysis

• Comp table: address, price, $/sqft, beds/baths, distance, sale date
• Estimated value vs listing price
• Price per sqft comparison bar chart
• Over/under priced assessment with percentage

Page 4: Cash Flow Projections

• Rental income estimate
• Monthly expense breakdown table (mortgage, taxes, insurance, vacancy, maintenance, management)
• Net monthly cash flow (highlighted, green if positive, red if negative)
• Key return metrics: Cap Rate, Cash-on-Cash, GRM
• 5-year cash flow projection table

Page 5: Neighborhood Scorecard

• School ratings (elementary, middle, high) with bar visualization
• Walk Score / Transit Score / Bike Score gauges
• Safety rating
• Demographics summary
• Growth outlook
• Amenities nearby

Page 6: Investment Analysis

• Category scores bar chart (all 5 categories)
• Best strategy recommendation
• Projected ROI table (1yr, 3yr, 5yr)
• Risk level assessment
• Value-add opportunity description
• Exit strategy options

Page 7: Market Conditions

• Market type indicator (buyer/seller/balanced)
• Median price and trend
• Days on market and inventory
• Economic drivers
• Price trend chart or table
• Supply/demand assessment

Page 8: Recommendation & Next Steps

• Overall recommendation (highlighted)
• Signal with explanation
• Key action items
• Suggested next steps
• Full disclaimer

PDF STYLING

Element	Style
Colors	Navy (#1B2A4A) headers, dark gray (#333) body, green (#2E7D32) positive, red (#C62828) negative
Fonts	Helvetica-Bold for headers, Helvetica for body
Score gauges	Circular arc gauges with color gradient (red -> yellow -> green)
Tables	Alternating row colors (white/#F5F5F5), navy header row
Charts	Horizontal bar charts for category scores and comparisons
Footer	Page numbers, disclaimer, generation date
Margins	50pt top, 40pt sides, 50pt bottom

STEP 6: VERIFY AND DELIVER

After PDF generation:

ls -la PROPERTY-REPORT.pdf

Confirm the file was created and report:

• File name and location
• File size
• Number of pages
• Which data sources were included (list the PROPERTY-*.md files used)
• Any data gaps (sections that had no source file — these will show "Data not available" in the PDF)

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OUTPUT SPECIFICATIONS

Spec	Value
File name	PROPERTY-REPORT.pdf (or PROPERTY-REPORT-[ADDRESS].pdf if address specified)
Page size	Letter (8.5" x 11")
Orientation	Portrait
Pages	6-10 depending on available data
File size	Typically 200KB - 1MB
Python dependency	ReportLab (pip install reportlab if not installed)

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RULES

1. Professional quality — The PDF must look like it came from a real estate analytics firm, not a quick printout
2. Data-driven — Every number in the PDF must come from the analysis files or live research; never fabricate data
3. Conservative estimates — Use the same conservative projections from the analysis files
4. Complete disclaimer — Full disclaimer must appear on the cover page and the last page
5. Graceful degradation — If some analysis files are missing, generate the PDF with available data and mark missing sections as "Not analyzed — run /realestate [command] to add this data"
6. Install dependencies — If ReportLab is not installed, install it automatically: pip install reportlab
7. Overwrite safely — If PROPERTY-REPORT.pdf already exists, overwrite it (the latest data wins)
8. Color-coded scores — All scores must be color-coded: green (70+), yellow (40-69), red (0-39)

ERROR HANDLING

• If ReportLab is not installed, run pip install reportlab and retry
• If no PROPERTY-*.md files exist, prompt the user to run /realestate analyze <address> first
• If the Python script fails, capture the error message and display it to the user with troubleshooting steps
• If only partial data is available, generate a partial report and clearly mark which sections are incomplete
• If the PDF file cannot be written (permission error), suggest an alternative output directory

DEPENDENCY INSTALLATION

If ReportLab is not available, install it:

pip install reportlab 2>/dev/null || pip3 install reportlab 2>/dev/null

If installation fails, provide manual instructions:

To install the PDF generation dependency:
  pip install reportlab
  
If using a virtual environment:
  python3 -m venv venv && source venv/bin/activate && pip install reportlab

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WHEN TO RECOMMEND PDF vs MARKDOWN

Situation	Recommend
Client presentation or email attachment	PDF
Lender or partner due diligence package	PDF
Quick internal reference	Markdown
Iterative editing and analysis	Markdown
Board or investor meeting	PDF
Personal property shopping	Markdown
Sales collateral for real estate agent	PDF

Always suggest: "Your analysis files are saved as Markdown for easy reference. Run /realestate report-pdf anytime to generate a polished PDF version for clients or presentations."

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DATA QUALITY FLAGS

When compiling the PDF, flag data quality issues:

Flag	Condition	Display In PDF
High Confidence	All 5 analysis agents ran, data is fresh	Green checkmark
Moderate Confidence	3-4 agents ran, or data is 7+ days old	Yellow warning
Low Confidence	Only 1-2 agents ran, or significant data gaps	Red flag with note
Stale Data	Analysis files are 30+ days old	Warning banner: "Data may be outdated"

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MULTI-PROPERTY REPORTS

If the user has analyzed multiple properties (multiple sets of PROPERTY-*.md files), the PDF should:

1. Detect all unique properties from file names
2. Ask the user which property to include (or all)
3. If "all", create a multi-property report with a comparison summary page
4. Each property gets its own section with the standard layout
5. Final page includes a side-by-side comparison table if 2+ properties are included

DISCLAIMER: For educational/research purposes only. Not financial or investment advice. All estimates are AI-generated approximations based on publicly available data. Always verify with licensed professionals and conduct your own due diligence before making any purchase or investment decisions.