Menu
Professional PDF Ad Strategy Report Generator
Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.
Based on SOC occupation classification
Full Ad Strategy Orchestrator. Launches 5 parallel subagents to build a complete advertising strategy from a single URL — audience personas, creative concepts, funnel architecture, competitive intelligence, and budget allocation. Produces a composite Ad Readiness Score (0-100) with a unified, client-ready strategy report.
Builds 5-7 detailed audience personas from a URL with demographics, psychographics, pain points, buying triggers, platform-specific targeting parameters, persona scoring, and negative audiences
Ad Performance Auditor — analyze existing ad campaigns and identify optimization opportunities
Budget Allocation & ROI Projector. Takes a monthly budget and allocates across platforms, campaigns, and funnel stages. Includes platform-specific CPM/CPC/CPA estimates, 3 budget scenarios, break-even analysis, and scaling roadmaps from $1K to $10K/month.
Competitive Ad Intelligence. Analyzes competitor advertising strategies including platform identification, landing page analysis, targeting reverse-engineering, positioning gaps, creative approach evaluation, and builds a "beat the competition" strategy with a competitor ad swipe file template.
Generates 10+ ad copy variations for any major ad platform with A/B testing, character-count compliance, and four proven copy frameworks (PAS, AIDA, BAB, 4Ps)
| name | ads-report-pdf |
| description | Professional PDF Ad Strategy Report Generator |
| version | 1.0.0 |
| author | AI Ads Strategist |
| tags | ["ads","pdf","report","strategy","client-ready"] |
| trigger | /ads report-pdf |
| output | ADS-STRATEGY-REPORT.pdf |
Generate a polished, client-ready PDF advertising strategy report using the ReportLab Python library. This skill scans the current working directory for all ads output files, extracts scores, audience personas, campaign structure, budget allocation, and competitive intelligence, compiles them into structured JSON, and produces a professional PDF with a cover page featuring an Ad Readiness Score gauge, audience persona cards, campaign funnel diagram, budget allocation chart, competitive positioning map, creative direction summary, and a prioritized 90-day action plan.
/ads report-pdf or /ads report-pdf <business name>| Format | Best For | Pros | Cons |
|---|---|---|---|
| Client presentations, email attachments, proposals, sales collateral | Professional appearance, charts and gauges, printable, consistent formatting | Requires Python script, harder to edit | |
| Markdown | Internal use, quick reference, iterative editing, version control | Easy to edit, fast to generate, git-friendly | Less visually polished, no charts |
Rule of thumb: If the report goes to a client, prospect, or executive, use PDF. If it is for internal use or further editing, use Markdown.
First, check if the dedicated PDF generation script exists:
ls ~/.claude/skills/ads/scripts/generate_ads_pdf.py 2>/dev/null
If the script exists: Use it directly (skip to Step 4). If the script does not exist: Generate the PDF inline using ReportLab (follow all steps).
Scan the current working directory for all ads skill outputs. Search for these files:
Primary data sources (search for all of these):
ADS-STRATEGY-*.md — Full strategy report (composite scores, all sections)ADS-AUDIENCE*.md — Audience personas, targeting parametersADS-COPY-*.md — Platform-specific ad copyADS-HOOKS*.md — Scroll-stopping hooksADS-CREATIVE-BRIEF*.md — Creative briefs for all formatsADS-VIDEO-SCRIPTS*.md — Video ad scriptsADS-FUNNEL*.md — Campaign funnel architectureADS-BUDGET*.md — Budget allocation planADS-COMPETITORS*.md — Competitive intelligenceADS-KEYWORDS*.md — Keyword strategy (Google Ads)ADS-TESTING-PLAN*.md — A/B testing planADS-LANDING*.md — Landing page auditADS-AUDIT*.md — Performance audit resultsSearch command:
ls ADS-*.md 2>/dev/null
For each file found, extract:
Compile all extracted data into a structured JSON object for the PDF generator:
{
"report_metadata": {
"business_name": "[Business Name]",
"website_url": "[URL]",
"industry": "[Industry]",
"report_date": "[YYYY-MM-DD]",
"generated_by": "AI Ads Strategist"
},
"ad_readiness_score": {
"composite_score": 0,
"audience_clarity": {"score": 0, "weight": 25, "findings": []},
"creative_quality": {"score": 0, "weight": 20, "findings": []},
"funnel_architecture": {"score": 0, "weight": 20, "findings": []},
"competitive_position": {"score": 0, "weight": 15, "findings": []},
"budget_efficiency": {"score": 0, "weight": 20, "findings": []}
},
"audience_personas": [
{
"name": "[Persona Name]",
"age_range": "[Age Range]",
"role_title": "[Role/Title]",
"pain_points": [],
"motivations": [],
"platforms": [],
"targeting_params": {}
}
],
"campaign_structure": {
"funnel_stages": [],
"campaigns": [],
"retargeting_flows": []
},
"budget_allocation": {
"total_monthly_budget": 0,
"platform_split": {},
"funnel_stage_split": {},
"projected_metrics": {
"estimated_impressions": 0,
"estimated_clicks": 0,
"estimated_conversions": 0,
"projected_cpa": 0,
"projected_roas": 0
}
},
"competitive_intelligence": {
"competitors_analyzed": [],
"positioning_gaps": [],
"messaging_opportunities": []
},
"top_ad_copy": {
"best_hooks": [],
"top_headlines": [],
"best_ctas": []
},
"action_plan": {
"week_1": [],
"week_2_4": [],
"month_2_3": []
},
"critical_findings": [],
"quick_wins": []
}
Run the PDF generation script with the compiled JSON data:
python3 ~/.claude/skills/ads/scripts/generate_ads_pdf.py
If the script does not exist, generate it inline. The script must produce a PDF with these sections:
Page 1: Cover Page
Page 2: Executive Summary
Page 3: Audience Analysis
Page 4: Creative Direction
Page 5: Campaign Architecture
AWARENESS → CONSIDERATION → CONVERSION → RETENTION
[Campaign] [Campaign] [Campaign] [Campaign]
[Budget %] [Budget %] [Budget %] [Budget %]
Page 6: Competitive Positioning
Page 7: Budget Allocation
| Metric | Month 1 | Month 2 | Month 3 |
|---|---|---|---|
| Spend | $X | $X | $X |
| Impressions | X | X | X |
| Clicks | X | X | X |
| Conversions | X | X | X |
| CPA | $X | $X | $X |
| ROAS | Xx | Xx | Xx |
Page 8: 90-Day Action Plan
Page 9: Appendix (if data available)
/ads keywords was run)/ads copy was run)/ads landing was run)/ads testing was run)Layout:
Colors:
Typography:
Charts (ReportLab Drawing):
After generating the PDF:
ls -la ADS-STRATEGY-REPORT.pdf
Report the file size and location to the user
Suggest next steps:
/ads audit after 7 days of campaign dataNot all skills may have been run before generating the PDF. Handle gracefully:
| Data Available | Behavior |
|---|---|
| Full strategy run (all 5 agents) | Generate complete report with all pages |
| Partial data (some agents) | Generate report with available sections, mark missing sections as "Not Yet Analyzed" |
| Only quick snapshot | Generate a mini-report (cover + executive summary + action plan) |
| No data files found | Inform user to run /ads strategy <url> first |
Minimum required data: At least one ADS-*.md file must exist. If none are found, display:
No ad strategy data found in current directory.
Run '/ads strategy <url>' first to generate the analysis,
then use '/ads report-pdf' to create the PDF report.
The final output is a single PDF file: ADS-STRATEGY-REPORT.pdf
Saved to the current working directory alongside the Markdown source files.
pip3 install reportlabADS-STRATEGY-REPORT.pdf for consistency