# Realestate Report Pdf > 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 - **Type:** Skill - **Install:** `agentstack add skill-zubair-trabzada-ai-realestate-claude-realestate-report-pdf` - **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior - **Seller:** [zubair-trabzada](https://agentstack.voostack.com/s/zubair-trabzada) - **Installs:** 0 - **Category:** [Content & Media](https://agentstack.voostack.com/c/content-and-media) - **Latest version:** 0.1.0 - **License:** MIT - **Upstream author:** [zubair-trabzada](https://github.com/zubair-trabzada) - **Source:** https://github.com/zubair-trabzada/ai-realestate-claude/tree/main/skills/realestate-report-pdf - **Website:** https://www.skool.com/aiworkshop ## Install ```sh agentstack add skill-zubair-trabzada-ai-realestate-claude-realestate-report-pdf ``` Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent. ## About # 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.** --- ## 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. --- ## TRIGGER This skill activates when the user runs: - `/realestate report-pdf` — generate a PDF from all available analysis files - `/realestate report-pdf ` — generate a PDF for a specific property - Also triggered by "generate PDF", "create PDF report", "make a client report", or "professional report" --- ## EXECUTION PIPELINE ### STEP 1: CHECK FOR PDF GENERATION SCRIPT First, verify the dedicated Python script exists: ```bash 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: ```bash 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:** ```bash 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 ` 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 ` 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: ```json { "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: ```bash 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: ```bash 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) --- ## 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) | --- ## 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 ` 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: ```bash 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 ``` --- ## 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." --- ## 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" | --- ## 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.** ## Source & license This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code. - **Author:** [zubair-trabzada](https://github.com/zubair-trabzada) - **Source:** [zubair-trabzada/ai-realestate-claude](https://github.com/zubair-trabzada/ai-realestate-claude) - **License:** MIT - **Homepage:** https://www.skool.com/aiworkshop Install and usage instructions live in the source repository linked above. ## Pricing - **Free** — Free ## Security capabilities Automated source analysis of v0.1.0 — what this tool can access: - **Network access:** no - **Filesystem access:** no - **Shell / process execution:** no - **Environment & secrets:** no - **Dynamic code execution:** no *"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.* ## Versions - **0.1.0** — security scan: passed — Imported from the upstream source. ## Links - Listing page: https://agentstack.voostack.com/l/skill-zubair-trabzada-ai-realestate-claude-realestate-report-pdf - Seller: https://agentstack.voostack.com/s/zubair-trabzada - Browse the marketplace: https://agentstack.voostack.com/browse --- Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.