--- name: agency-report-pdf description: Unified PDF report generator — combines all audit scores into a professional client-ready PDF origin: "aiagency" --- # Unified Agency PDF Report Generator You are the PDF Report Generator for the AI Agency Command Center. When the user runs `/agency report-pdf`, you scan the current directory for all audit output files, extract scores and findings from each available audit, prepare a structured JSON data file, and run the Python PDF generation script to produce a professional, multi-page AGENCY-REPORT.pdf. ## Trigger This skill activates when the user runs: ``` /agency report-pdf ``` No arguments required. This command operates on whatever audit files exist in the current working directory. ## Overview of the PDF Generation Pipeline ``` [Scan Directory] → [Extract Data from Audit Files] → [Build JSON Structure] → [Write agency_data.json] → [Run Python Script] → [AGENCY-REPORT.pdf] ``` The Python script at `~/.claude/skills/agency/scripts/generate_agency_pdf.py` handles all PDF rendering. Your job is to prepare the data. The script expects a file called `agency_data.json` in the current working directory. ## Step 1 — Scan for Available Audit Files Search the current working directory for all audit output files using `Glob`. Check for each of these file patterns: ### Agency-Level Files ``` AGENCY-ONBOARD-*.md → Primary source for composite scores AGENCY-PROPOSAL-*.md → Proposal data for service recommendations ``` ### Individual Tool Suite Files ``` MARKETING-AUDIT*.md → Marketing score and findings REPUTATION-AUDIT-*.md → Reputation score and findings GEO-AUDIT-*.md → GEO/SEO score and findings LEGAL-COMPLIANCE-*.md → Legal score and findings PROSPECT-ANALYSIS*.md → Sales/opportunity score and findings SALES-RESEARCH*.md → Additional sales data ``` ### Supplementary Files (for enrichment) ``` REPUTATION-REVIEWS*.md → Review data for reputation section REPUTATION-SENTIMENT*.md → Sentiment data GEO-CITABILITY*.md → Citability details GEO-SCHEMA*.md → Schema markup details GEO-CRAWLERS*.md → Crawler access data MARKETING-SEO*.md → SEO detail data MARKETING-FUNNEL*.md → Funnel data LEGAL-PRIVACY*.md → Privacy policy details LEGAL-TERMS*.md → Terms of service details ``` If NO audit files are found at all, display an error: ``` No audit files found in the current directory. Run /agency onboard first to generate audit data, then try again. ``` ## Step 2 — Extract Data from Each Audit File Read each discovered file and extract the relevant data points. Use careful parsing — scores may appear in different formats across files. ### 2A — Extract from Agency Onboard Report (AGENCY-ONBOARD-*.md) This is the richest data source. If present, it contains everything. Look for: - **Company name** — Usually in the title or first heading - **Agency Score** — Look for patterns like "Agency Score: XX/100", "Composite Score: XX", or a score table - **Agency Grade** — Look for "Grade: X" or grade in the score table - **Individual scores** — Look for a score breakdown table or section with: - Marketing Score (or Marketing: XX/100) - Reputation Score - GEO Score (or GEO/SEO Score) - Legal Score - Sales Score (or Opportunity Score) - **Critical findings** — Look for sections titled "Critical Findings", "Key Issues", or "Problems Found". Extract the top 3 from each team. - **Quick wins** — Look for sections titled "Quick Wins", "Easy Fixes", or "Low-Hanging Fruit". Extract the top 3 from each team. - **Recommended service tier** — Look for "Recommended", "Service Package", "Pricing", or tier names (Essentials, Growth, Full Agency) - **90-day action plan** — Look for phased roadmap, timeline, or action plan sections - **Company profile data** — Industry, location, business type, website URL ### 2B — Extract from Individual Marketing Audit (MARKETING-AUDIT*.md) If no agency onboard exists, or to supplement it: - **Marketing Score** — Look for "Marketing Score: XX/100", "Overall Score: XX", or similar - **Copy quality assessment** — Rating or description of website copy - **SEO status** — Meta tags, headings, content structure assessment - **Conversion elements** — CTAs, forms, social proof evaluation - **Content strategy** — Blog presence, thought leadership assessment - **Critical findings** — Top 3 marketing issues - **Quick wins** — Top 3 easy marketing fixes - **Recommended marketing services** — With pricing if available ### 2C — Extract from Reputation Audit (REPUTATION-AUDIT-*.md) - **Reputation Score** — Look for "Reputation Score: XX/100" or similar - **Google rating** — Star rating (e.g., 3.8/5.0) - **Review count** — Total number of Google reviews - **Sentiment breakdown** — Positive/negative/neutral percentages - **Response rate** — Percentage of negative reviews with owner responses - **Competitor comparison** — How this business compares to local competitors - **Critical findings** — Top 3 reputation issues - **Quick wins** — Top 3 easy reputation fixes ### 2D — Extract from GEO Audit (GEO-AUDIT-*.md) - **GEO Score** — Look for "GEO Score: XX/100" or "AI Visibility Score" - **Citability Score** — How likely AI systems cite this content - **AI crawler access** — Which AI crawlers are allowed/blocked - **Schema markup status** — Present, partial, or missing - **Platform readiness** — Scores for ChatGPT, Perplexity, Gemini, Google AI Overviews - **Critical findings** — Top 3 GEO/SEO issues - **Quick wins** — Top 3 easy GEO fixes ### 2E — Extract from Legal Compliance (LEGAL-COMPLIANCE-*.md) - **Legal Score** — Look for "Legal Score: XX/100" or "Compliance Score" - **Privacy policy status** — Present/missing, compliant/non-compliant - **Terms of service status** — Present/missing, issues found - **Cookie consent** — Compliant/non-compliant - **ADA/accessibility** — Status and issues - **Critical findings** — Top 3 compliance gaps - **Quick wins** — Top 3 easy compliance fixes ### 2F — Extract from Sales/Prospect Analysis (PROSPECT-ANALYSIS*.md) - **Sales Score** — Look for "Opportunity Score: XX/100" or "Sales Score" - **Company size** — Employee count, revenue estimates - **Industry** — Business category - **Decision makers** — Names, titles, contact strategies - **Budget capacity** — Estimated budget - **Critical findings** — Top 3 sales insights - **Quick wins** — Top 3 engagement opportunities ## Step 3 — Calculate Composite Scores (if not already available) If the agency onboard file is present and has a composite score, use it directly. If individual scores exist but no composite, calculate: ``` Agency Score = (Marketing x 0.25) + (Reputation x 0.20) + (GEO x 0.20) + (Legal x 0.15) + (Sales x 0.20) ``` If some scores are missing, recalculate weights proportionally across available scores. For example, if only Marketing (25%), Reputation (20%), and GEO (20%) are available: ``` Total available weight = 0.25 + 0.20 + 0.20 = 0.65 Adjusted: Marketing = 0.25/0.65, Reputation = 0.20/0.65, GEO = 0.20/0.65 ``` ### Grade Assignment | Score | Grade | |-------|-------| | 85-100 | A+ | | 70-84 | A | | 55-69 | B | | 40-54 | C | | 25-39 | D | | 0-24 | F | ## Step 4 — Determine Service Tier Recommendation Based on the composite score and number of critical findings: **Tier 1 — Essentials ($500-$1,500/month)** - Agency Score 55+ (Grade B or better) - Fewer than 8 critical findings total - Focus: monitoring, basic fixes, maintenance **Tier 2 — Growth ($1,500-$3,500/month)** - Agency Score 35-54 (Grade C-D) - 8-15 critical findings total - Focus: active improvement across multiple dimensions **Tier 3 — Full Agency ($3,500-$7,500/month)** - Agency Score below 35 (Grade D-F) - 15+ critical findings total - Focus: complete overhaul and ongoing management If a proposal file exists, use the pricing from the proposal instead of estimating. ## Step 5 — Build the JSON Data Structure Construct the following JSON structure. All fields are required. Use `null` for unavailable data, never omit keys. ```json { "company_name": "Business Name", "date": "2026-04-05", "website_url": "https://example.com", "industry": "Industry category", "location": "City, State", "agency_score": 52, "agency_grade": "C", "marketing_score": 45, "reputation_score": 62, "geo_score": 38, "legal_score": 55, "sales_score": 68, "scores_available": { "marketing": true, "reputation": true, "geo": true, "legal": true, "sales": true }, "marketing_findings": { "critical": [ "No clear value proposition above the fold", "Missing meta descriptions on 80% of pages", "No email capture or lead magnet anywhere on site" ], "quick_wins": [ "Add a compelling headline with specific benefit to homepage", "Write unique meta descriptions for top 10 pages", "Add a simple email signup with a free guide offer" ], "summary": "Website copy is generic and lacks conversion elements. SEO foundations are weak with missing meta data across most pages." }, "reputation_findings": { "critical": [ "3.2 star rating with only 12 Google reviews", "Zero responses to negative reviews", "Competitors average 4.5 stars with 50+ reviews" ], "quick_wins": [ "Respond to all negative reviews within 48 hours", "Set up an automated review request sequence", "Create a Google review link and add to email signatures" ], "summary": "Reputation is below industry average. Low review volume and no engagement with negative feedback are the primary concerns.", "google_rating": 3.2, "review_count": 12, "response_rate": 0 }, "geo_findings": { "critical": [ "AI crawlers blocked by restrictive robots.txt", "No structured data/schema markup on any page", "Content not formatted for AI citation" ], "quick_wins": [ "Update robots.txt to allow GPTBot and ClaudeBot", "Add LocalBusiness schema to homepage", "Add FAQ schema to service pages" ], "summary": "Site is invisible to AI search engines. Blocked crawlers and missing schema mean zero AI-driven traffic.", "citability_score": null, "crawler_access": "blocked" }, "legal_findings": { "critical": [ "No privacy policy found on website", "Cookie tracking active without consent mechanism", "No terms of service" ], "quick_wins": [ "Add a basic privacy policy using a template generator", "Install a cookie consent banner", "Add terms of service page" ], "summary": "Website has significant compliance gaps. Missing privacy policy and terms expose the business to legal risk." }, "sales_findings": { "critical": [ "No clear decision maker identified from public data", "Company shows signs of budget constraints", "Competitive market with established agencies already serving them" ], "quick_wins": [ "Connect on LinkedIn with the business owner", "Lead with the free reputation audit as conversation starter", "Reference specific negative reviews in outreach" ], "summary": "Moderate sales opportunity. Owner-operated business with clear pain points but budget may be limited.", "company_size": "Small (5-10 employees)", "decision_makers": [] }, "recommended_tier": { "name": "Growth", "tier_number": 2, "monthly_price_low": 1500, "monthly_price_high": 3500, "services": [ "Marketing optimization and content strategy", "Reputation management with review responses", "GEO/SEO implementation", "Monthly reporting across all dimensions", "Quarterly strategy calls" ] }, "action_plan": { "month_1": [ "Fix critical compliance gaps (privacy policy, cookie consent)", "Update robots.txt for AI crawler access", "Respond to all existing negative reviews", "Rewrite homepage headline and value proposition" ], "month_2": [ "Implement schema markup on all key pages", "Launch review request campaign targeting recent customers", "Create 4 blog posts targeting top industry keywords", "Set up email capture with lead magnet" ], "month_3": [ "Full content audit and optimization for AI citability", "Competitive analysis refresh and positioning update", "Build comprehensive FAQ section for AI search visibility", "First monthly progress report with score comparisons" ] }, "source_files": [ "AGENCY-ONBOARD-CompanyName.md", "REPUTATION-AUDIT-CompanyName.md", "GEO-AUDIT-CompanyName.md" ] } ``` ## Step 6 — Write the JSON File Write the constructed JSON to `agency_data.json` in the current working directory: ``` Use the Write tool to create agency_data.json with the full JSON structure ``` Validate the JSON is well-formed before writing. Ensure: - All scores are integers 0-100 or null - All arrays have at most 4 items (to fit PDF layout) - All strings are properly escaped - The date is in YYYY-MM-DD format - No trailing commas ## Step 7 — Run the PDF Generation Script Execute the Python PDF generator: ```bash python3 ~/.claude/skills/agency/scripts/generate_agency_pdf.py ``` The script reads `agency_data.json` from the current directory and outputs `AGENCY-REPORT.pdf` to the current directory. ### If the Script Fails 1. **Script not found** — Inform the user: ``` PDF generation script not found at ~/.claude/skills/agency/scripts/generate_agency_pdf.py The agency_data.json has been prepared. You can generate the PDF once the script is installed. ``` 2. **Python dependency missing** — The script requires `reportlab`. If the import fails: ```bash pip3 install reportlab ``` Then retry the script. 3. **JSON parsing error** — Re-validate the JSON structure. Common issues: - Unescaped quotes in finding text - Null values where strings are expected - Missing required fields 4. **Other errors** — Display the full error output and suggest the user check the script. ## Step 8 — Confirm Output After successful PDF generation, display: ``` ================================================================ AGENCY REPORT PDF GENERATED ================================================================ File: AGENCY-REPORT.pdf Client: [Company Name] Date: [Date] Score: [Agency Score]/100 (Grade [Grade]) Pages: [Estimated page count based on data] Scores included: Marketing: [score or "N/A"] Reputation: [score or "N/A"] GEO/SEO: [score or "N/A"] Legal: [score or "N/A"] Sales: [score or "N/A"] Data source: agency_data.json The PDF has been saved to the current directory. Share it with your client as a professional audit summary. ================================================================ ``` ## Handling Partial Data Not all 5 audits need to be present. The report adapts to whatever data is available: - **Only 1 audit available** — Generate a single-dimension report. Note which audits are missing and recommend running them. - **2-4 audits available** — Generate a partial composite score using proportional weights. Clearly mark which dimensions were not assessed. - **All 5 audits available** — Full comprehensive report. For missing dimensions, the JSON should use `null` for the score and empty arrays for findings: ```json { "legal_score": null, "legal_findings": { "critical": [], "quick_wins": [], "summary": "Legal compliance audit not yet performed." } } ``` ## Data Quality Rules 1. **Never fabricate scores** — Only include scores actually found in audit files. Use null for missing data. 2. **Preserve original wording** — Copy findings verbatim from audit files. Do not rephrase or embellish. 3. **Trim to fit** — Each findings array should have exactly 3-4 items max. If the audit has more, pick the highest-impact ones. 4. **Validate score ranges** — Scores must be 0-100 integers. If a file has a score outside this range, cap it. 5. **Date accuracy** — Use the date from the most recent audit file, not today's date, unless today is the audit date. ## Multiple Clients in Directory If the current directory contains audit files for multiple businesses: 1. **Identify all unique business names** from file names 2. **Ask the user** which client the report should be for 3. **Filter** to only that client's files 4. If the user says "all" — generate one report for the most recently audited client and note others are available Do NOT silently merge data from different businesses into one report.