/realestate-report-pdf

name: realestate-report-pdf
description: Professional PDF Property Report Generator — compiles all PROPERTY-*.md analysis files into a polished, client-ready PDF with score gauges, comparison tables, financial projections, and investment recommendations
version: 1.0.0
author: AI Real Estate Analyst
tags: [realestate, report, pdf, professional, client-ready, property-report]
command: /realestate report-pdf
output: PROPERTY-REPORT.pdf

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",
  "