SEO Audit

Full Website SEO Audit

Shared Data Cache

Step 0 -- Check shared data cache:

Before gathering, check .seo-cache/ for reusable context from related SEO skills. Reference: ../seo/references/shared-data-cache.md for schemas and dependency map.

Check these cache files when present:

• .seo-cache/site-meta.json for domain, business type, industry, and crawl context

• .seo-cache/audit-scores.json for prior full-audit priorities

• .seo-cache/pages/{url-slug}/page-analysis.json for page-level context when a URL is provided

• If found: parse and use clearly valid fields (note "Using cached [X] from [date]")

• If missing, corrupt, or irrelevant: continue with fresh evidence

• If the user says "refresh" or "re-run": ignore cache reads and overwrite on write

Process

1. Fetch homepage: use scripts/fetch_page.py to retrieve HTML
2. Detect business type: analyze homepage signals per seo orchestrator
3. Crawl site: follow internal links up to 500 pages, respect robots.txt
4. Delegate to subagents (if available, otherwise run inline sequentially):
   • seo-technical -- robots.txt, sitemaps, canonicals, Core Web Vitals, security headers
   • seo-content -- E-E-A-T, readability, thin content, AI citation readiness
   • seo-schema -- detection, validation, generation recommendations
   • seo-sitemap -- structure analysis, quality gates, missing pages
   • seo-performance -- LCP, INP, CLS measurements
   • seo-visual -- screenshots, mobile testing, above-fold analysis
   • seo-geo -- AI crawler access, llms.txt, citability, brand mention signals
   • seo-local -- GBP signals, NAP consistency, reviews, local schema, industry-specific local factors (spawn when Local Service industry detected: brick-and-mortar, SAB, or hybrid business type)
   • seo-maps -- Geo-grid rank tracking, GBP audit, review intelligence, competitor radius mapping (spawn when Local Service detected AND DataForSEO MCP available)
   • seo-google -- CWV field data (CrUX), URL indexation (GSC), organic traffic (GA4) (spawn when Google API credentials detected via python scripts/google_auth.py --check)
   • seo-backlinks -- Backlink profile data: DA/PA, referring domains, anchor text, toxic links (spawn when Moz or Bing API credentials detected via python scripts/backlinks_auth.py --check, or always include Common Crawl domain-level metrics)
   • seo-cluster -- Semantic clustering analysis (spawn when content strategy signals detected: blog, pillar pages, topic clusters)
   • seo-sxo -- Search experience analysis: page-type mismatch, user stories, persona scoring (always include in full audits)
   • seo-drift -- Drift analysis: compare against stored baseline (spawn when drift baseline exists for the URL via python scripts/drift_history.py <url>)
   • seo-ecommerce -- Product schema, marketplace intelligence (spawn when E-commerce industry detected)
5. Score -- aggregate into SEO Health Score (0-100)
6. Report -- generate prioritized action plan

Crawl Configuration

Max pages: 500
Respect robots.txt: Yes
Follow redirects: Yes (max 3 hops)
Timeout per page: 30 seconds
Concurrent requests: 5
Delay between requests: 1 second

Output Files

• FULL-AUDIT-REPORT.md: Comprehensive findings
• ACTION-PLAN.md: Prioritized recommendations (Critical > High > Medium > Low)
• screenshots/: Desktop + mobile captures (if Playwright available)
• PDF Report (recommended): Generate a professional A4 PDF using scripts/google_report.py --type full. This produces a white-cover enterprise report with TOC, executive summary, charts (Lighthouse gauges, query bars, index donut), metric cards, threshold tables, prioritized recommendations with effort estimates, and implementation roadmap. Always offer PDF generation after completing an audit.

Scoring Weights

Report Structure

Executive Summary

• Overall SEO Health Score (0-100)
• Business type detected
• Top 5 critical issues
• Top 5 quick wins

Technical SEO

• Crawlability issues
• Indexability problems
• Security concerns
• Core Web Vitals status

Content Quality

• E-E-A-T assessment
• Thin content pages
• Duplicate content issues
• Readability scores

On-Page SEO

• Title tag issues
• Meta description problems
• Heading structure
• Internal linking gaps

Schema & Structured Data

• Current implementation
• Validation errors
• Missing opportunities

Performance

• LCP, INP, CLS scores
• Resource optimization needs
• Third-party script impact

Images

• Missing alt text
• Oversized images
• Format recommendations

AI Search Readiness

• Citability score
• Structural improvements
• Authority signals

Priority Definitions

• Critical: Blocks indexing or causes penalties (fix immediately)
• High: Significantly impacts rankings (fix within 1 week)
• Medium: Optimization opportunity (fix within 1 month)
• Low: Nice to have (backlog)

DataForSEO Integration (Optional)

If DataForSEO MCP tools are available, spawn the seo-dataforseo agent alongside existing subagents to enrich the audit with live data: real SERP positions, backlink profiles with spam scores, on-page analysis (Lighthouse), business listings, and AI visibility checks (ChatGPT scraper, LLM mentions).

Google API Integration (Optional)

If Google API credentials are configured (python scripts/google_auth.py --check), spawn the seo-google agent to enrich the audit with real Google field data: CrUX Core Web Vitals (replaces lab-only estimates), GSC URL indexation status, search performance (clicks, impressions, CTR), and GA4 organic traffic trends. The Performance (CWV) category score benefits most from field data.

Error Handling

Write to shared data cache

After completing all work, write a concise JSON summary to .seo-cache/ when the workflow produced durable findings. Use the schemas and naming rules in ../seo/references/shared-data-cache.md; include at least cache_type, analyzed_at, source URL/domain, key findings, issues, recommendations, and tool limitations. Add .seo-cache/ to .gitignore if it is missing.