Market Report Pdf

PDF Marketing Report Generator

Skill Purpose

Generate a professional, visually polished PDF marketing report using the Python script scripts/generate_pdf_report.py. This skill collects all available audit and analysis data, structures it into the expected JSON format, invokes the script, and produces a branded PDF with score gauges, bar charts, comparison tables, findings, and a prioritized action plan.

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

When to Use PDF vs Markdown

| Format | Best For | Pros | Cons | |---|---|---|---| | PDF | Client presentations, email attachments, sales collateral | Professional appearance, consistent formatting, visual charts, printable | Harder to edit, requires Python script | | Markdown | Internal use, quick reference, iterative editing, version control | Easy to edit, readable in any editor, git-friendly | Less visually polished, no charts |

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 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

Step 2: Build the JSON Data Structure

The scripts/generate_pdf_report.py script expects a JSON file as input with this exact structure:

{
  "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:

| Category | What It Measures | Scoring Guidance | |---|---|---| | Content & Messaging | Copy quality, value proposition, headline clarity, CTA text, brand voice consistency | 80+: Clear, benefit-driven, specific. 60-79: Adequate but generic. /tmp/report_data.json /dev/null || pip3 install reportlab

**Generate the report:**
```bash
python3 scripts/generate_pdf_report.py /tmp/report_data.json "MARKETING-REPORT-.pdf"

Replace `` with the target website's domain name (without protocol or www), using hyphens instead of dots. For example:

• 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-.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 (` -- Generates comprehensive audit data

1. Run /market competitors -- Adds competitor comparison data
2. Run /market seo -- Adds detailed SEO findings
3. Run /market landing -- Adds CRO analysis
4. Run /market report-pdf -- 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-.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.

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
• Source: zubair-trabzada/ai-marketing-claude
• License: MIT
• Homepage: https://www.skool.com/aiworkshop

Install and usage instructions live in the source repository linked above.