PDF Marketing Report Generator

Skill Purpose

Generate a professional, visually polished PDF marketing report using the Python script

scripts/generate_pdf_report.py

When to Use

• User wants a PDF version of the marketing report (not just Markdown)
• User is preparing a deliverable for a client presentation
• User asks for a "polished report", "client-ready report", or "PDF report"
• User wants a visual report with charts and scores
• Triggered by

  /market report-pdf

  or

  /market report-pdf <domain>

When to Use PDF vs Markdown

Rule of thumb: If the report is going to a client or prospect, use PDF. If it is for internal use or further editing, use Markdown.

How to Execute

Step 1: Collect All Available Data

Gather data from all previous skill runs. Check for these files in the project directory:

Primary data sources:

• MARKETING-AUDIT.md

  -- Overall audit results

• LANDING-CRO.md

  -- Landing page conversion analysis

• SEO-AUDIT.md

  -- SEO findings

• BRAND-VOICE.md

  -- Brand voice analysis

• COMPETITOR-ANALYSIS.md

  -- Competitor comparison data

• FUNNEL-ANALYSIS.md

  -- Funnel analysis

• SOCIAL-AUDIT.md

  -- Social media audit

• EMAIL-AUDIT.md

  -- Email marketing audit

• AD-AUDIT.md

  -- Advertising audit

If no previous data exists:

1. Recommend the user run

   /market audit <url>

   first for the best results
2. If the user insists on generating a report without prior audits, analyze the provided URL directly and build the data structure from scratch
3. Use the analyze_page.py script to gather automated data:

   python scripts/analyze_page.py <url>

Step 2: Build the JSON Data Structure

The

scripts/generate_pdf_report.py

{
  "url": "https://example.com",
  "date": "March 1, 2026",
  "brand_name": "Example Co",
  "overall_score": 62,
  "executive_summary": "A 2-4 sentence summary of the overall marketing health, key opportunities, and estimated revenue impact of implementing recommendations.",
  "categories": {
    "Content & Messaging": {
      "score": 68,
      "weight": "25%"
    },
    "Conversion Optimization": {
      "score": 52,
      "weight": "20%"
    },
    "SEO & Discoverability": {
      "score": 74,
      "weight": "20%"
    },
    "Competitive Positioning": {
      "score": 48,
      "weight": "15%"
    },
    "Brand & Trust": {
      "score": 70,
      "weight": "10%"
    },
    "Growth & Strategy": {
      "score": 55,
      "weight": "10%"
    }
  },
  "findings": [
    {
      "severity": "Critical",
      "finding": "Description of the most important finding"
    },
    {
      "severity": "High",
      "finding": "Description of a high-priority finding"
    },
    {
      "severity": "Medium",
      "finding": "Description of a medium-priority finding"
    },
    {
      "severity": "Low",
      "finding": "Description of a lower-priority finding"
    }
  ],
  "quick_wins": [
    "First quick win action item",
    "Second quick win action item",
    "Third quick win action item"
  ],
  "medium_term": [
    "First medium-term action item",
    "Second medium-term action item",
    "Third medium-term action item"
  ],
  "strategic": [
    "First strategic action item",
    "Second strategic action item",
    "Third strategic action item"
  ],
  "competitors": [
    {
      "name": "Competitor A",
      "positioning": "Their market position",
      "pricing": "Their pricing model",
      "social_proof": "Their trust signals",
      "content": "Their content approach"
    },
    {
      "name": "Competitor B",
      "positioning": "Their market position",
      "pricing": "Their pricing model",
      "social_proof": "Their trust signals",
      "content": "Their content approach"
    }
  ]
}

Step 3: Field-by-Field Data Assembly Guide

url

(string, required)

The target website URL. Use the full URL including protocol.

date

(string, required)

The report generation date. Format: "Month DD, YYYY" (e.g., "March 1, 2026").

brand_name

(string, required)

The company or brand name. Used in competitor comparison table headers.

overall_score

(integer, 0-100, required)

The weighted average of all category scores. Calculate as:

overall_score = (content * 0.25) + (conversion * 0.20) + (seo * 0.20) + (competitive * 0.15) + (brand * 0.10) + (growth * 0.10)

executive_summary

(string, required)

A 2-4 sentence summary covering:

• Current marketing health assessment
• Top 1-2 most impactful findings
• Estimated revenue impact of implementing recommendations
• Recommended first step

Keep it concise and impactful. This appears on the cover page right below the score gauge.

categories

(object, required)

Exactly 6 categories with their scores. The categories map to these evaluation areas:

findings

(array, required)

An array of finding objects, each with

severity

finding

Severity levels:

• Critical

  -- Directly losing revenue or customers. Fix immediately.

• High

  -- Significant impact on growth. Fix within 1-2 weeks.

• Medium

  -- Meaningful improvement opportunity. Fix within 1 month.

• Low

  -- Nice-to-have improvement. Fix when time allows.

Writing effective findings:

• Be specific: "Homepage headline says 'Welcome to Our Platform'" not "Headline needs improvement"
• Quantify impact: "Missing meta descriptions on 8 of 12 landing pages"
• Reference benchmarks: "Page load time is 4.2s (benchmark: under 2s)"
• Include evidence: "No testimonials found on homepage, pricing page, or signup page"

Aim for 5-10 findings. Order from most to least severe.

quick_wins

(array, required)

3-5 action items that can be implemented within one week with minimal effort. Each should be a specific, actionable instruction.

Good quick win: "Rewrite the homepage headline from 'Welcome to Our Platform' to 'Cut Your Reporting Time by 75% -- Automated Analytics for Growth Teams'"

Bad quick win: "Improve the headline" (too vague)

medium_term

(array, required)

3-5 action items requiring 1-3 months to implement. These are more involved but have high impact.

strategic

(array, required)

3-5 action items requiring 3-6 months. These are foundational changes that require planning and sustained effort.

competitors

(array, optional)

Up to 3 competitor objects for the comparison table. If no competitor data is available, omit this field -- the script will skip the competitor section.

Step 4: Write the JSON File

Save the assembled data as a temporary JSON file:

# Write the JSON data to a temporary file
cat > /tmp/report_data.json << 'JSONEOF'
{
  ... assembled JSON data ...
}
JSONEOF

Step 5: Invoke the PDF Generator Script

Prerequisites check: First, verify that

reportlab

python3 -c "import reportlab" 2>/dev/null || pip3 install reportlab

Generate the report:

python3 scripts/generate_pdf_report.py /tmp/report_data.json "MARKETING-REPORT-<domain>.pdf"

Replace

<domain>

• example.com

  becomes

  MARKETING-REPORT-example-com.pdf

• myapp.io

  becomes

  MARKETING-REPORT-myapp-io.pdf

Demo mode (no arguments): Running the script without arguments generates a sample report with placeholder data:

python3 scripts/generate_pdf_report.py
# Creates: MARKETING-REPORT-sample.pdf

Step 6: Verify the Output

After generation, verify the PDF was created:

ls -la "MARKETING-REPORT-<domain>.pdf"

Report the file path and size to the user.

Step 7: Clean Up

Remove the temporary JSON file:

rm /tmp/report_data.json

PDF Report Contents

The generated PDF includes the following pages:

Page 1: Cover Page

• Report title: "Marketing Audit Report"
• Target URL
• Generation date
• Overall score gauge (circular visualization with color coding)
• Grade letter (A+ through F)
• Executive summary paragraph

Page 2: Score Breakdown

• Horizontal bar chart showing all 6 category scores with color coding
• Score table with category names, scores, weights, and status labels
• Color coding: Green (80+), Blue (60-79), Yellow (40-59), Red (<40)

Page 3: Key Findings

• Findings table with severity labels and descriptions
• Color-coded severity indicators (Critical = red, High = orange, Medium = yellow, Low = blue)
• Findings ordered from most to least severe

Page 4: Prioritized Action Plan

• Quick Wins section (This Week)
• Medium-Term section (1-3 Months)
• Strategic section (3-6 Months)
• Numbered action items in each tier

Page 5: Competitive Landscape (if competitor data provided)

• Comparison table with client vs up to 3 competitors
• Rows: Positioning, Pricing, Social Proof, Content

Final Page: Methodology

• Scoring methodology explanation
• Category weights and measurement criteria
• Footer: "Generated by AI Marketing Suite for Claude Code"

Color Scheme

The PDF uses a professional color palette:

Score-to-Color Mapping

• 80-100: Green (#00C853) -- Strong performance
• 60-79: Blue (#2D5BFF) -- Solid with room to improve
• 40-59: Amber (#FFB300) -- Needs attention
• 0-39: Red (#FF1744) -- Critical issues

Troubleshooting

ModuleNotFoundError: No module named 'reportlab'

pip3 install reportlab

overall_score

competitors

name

positioning

pricing

social_proof

content

python3 -c "import json; json.load(open('/tmp/report_data.json'))"

Integration with Other Skills

This skill works best when combined with other audit skills. The recommended workflow:

1. Run

   /market audit <url>

   -- Generates comprehensive audit data
2. Run

   /market competitors <url>

   -- Adds competitor comparison data
3. Run

   /market seo <url>

   -- Adds detailed SEO findings
4. Run

   /market landing <url>

   -- Adds CRO analysis
5. Run

   /market report-pdf <url>

   -- Compiles everything into a PDF

The PDF report skill will automatically look for output files from these skills and incorporate their data into the report JSON.

Output

• File:

  MARKETING-REPORT-<domain>.pdf

• Location: Project root directory
• Size: Typically 200KB-500KB depending on content volume
• Pages: 5-7 pages depending on whether competitor data and additional sections are included

Key Principles

• The PDF report is the most client-facing deliverable in the toolkit. Quality matters.
• Always verify the JSON data is complete and accurate before generating. Garbage in, garbage out.
• Use the PDF for initial client impressions and sales conversations. Follow up with the more detailed Markdown report if the client engages.
• Every score should be justifiable. If a client asks "why did I get a 52 in Conversion Optimization?", the findings should provide clear evidence.
• Round scores to whole numbers. Decimals imply false precision.
• Keep the executive summary tight -- 2-4 sentences maximum. Clients skim cover pages.
• If generating for a prospect (not yet a client), the report serves as a sales tool. Make the opportunities compelling and the action plan achievable.