Comprehensive SEO and GEO audit methodology covering technical SEO, on-page optimization, content quality, E-E-A-T signals, and AI citation readiness. Informed by Google's ranking pipeline architecture. Use for thorough content and site audits.
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resources/google-ranking-signals.yamlA comprehensive SEO audit methodology that evaluates content for both traditional search engine optimization and modern AI/LLM visibility (GEO). Informed by Google's multi-stage ranking pipeline.
Content passes through sequential evaluation gates. Understanding this pipeline informs what to audit and why.
Mustang → Topicality (T*) → NavBoost → Twiddlers
↓ ↓ ↓ ↓
Initial Query-match User clicks Final
scoring relevance (13 months) adjustments
Key Implications:
siteAuthority scoreSee resources/google-ranking-signals.yaml for complete signal reference.
Before auditing content, assess domain-level factors that cap page potential:
Domain Authority Check:
queriesForWhichOfficial)URL History Assessment:
Sandbox Awareness (New Domains/Pages):
| Factor | Status | Impact |
|---|---|---|
| hostAge | New (<6mo) / Established | Domain-level sandbox |
| documentHistory | New URL / Has history | Page-level demotion |
| Graduation signals | Social, backlinks, engagement | Exit sandbox |
If new domain: Set realistic expectations. Dual sandbox (host + document) requires consistent quality publishing, backlinks, and positive engagement over months.
Evaluate foundational technical elements:
Page-Level Technical:
titlematchScoreSite-Level Technical:
Entity & Trust Signals:
Evaluate content depth and value. Google's contentEffort signal measures ML-assessed effort invested.
Content Completeness:
| Factor | Check | Score | Signal |
|---|---|---|---|
| Topic coverage | Comprehensive vs. shallow | /10 | contentEffort |
| Unique value | Original insights vs. rehash | /10 | OriginalContentScore |
| Accuracy | Factual, verifiable | /10 | Trust signals |
| Freshness | Current data/sources | /10 | semanticDate |
| Depth | Expert-level detail | /10 | contentEffort |
Token Truncation Awareness: Google uses token limits—long documents may be truncated. Optimize for this:
Readability:
Freshness Audit: Google evaluates freshness through three distinct methods:
⚠️ Freshness spam detection: Changing timestamps without updating substance is detected via semanticDate comparison.
Assess keyword optimization. Affects Topicality (T*) stage scoring.
Primary Keyword:
Semantic Coverage:
webrefEntities signal)Entity Optimization:
Assess Experience, Expertise, Authority, Trust signals. These map directly to technical signals.
Experience Signals:
Expertise Signals: (→ contentEffort, OriginalContentScore)
Authority Signals: (→ siteAuthority, author attribute)
Trust Signals:
Disconnected Entity Check: Sites without verifiable ownership, author info, and contact transparency trigger algorithmic distrust—even with high content quality.
Assess content for LLM visibility:
Extractability:
Fact-Density:
Structure for AI:
Google's NavBoost uses 13 months of aggregated click behavior. Audit for "good clicks" potential.
Click Quality Indicators:
Engagement Factors:
| Signal | Status | Improvement |
|---|---|---|
| Expected dwell time | Low/Med/High | Match intent better |
| Bounce risk | Low/Med/High | Clearer value prop |
| Return visit potential | Low/Med/High | Brand building |
## SEO Audit Report
**Page:** [URL or filename]
**Date:** [Audit date]
**Overall Score:** X/100
**Domain Status:** [Established/New (sandbox considerations)]
### Executive Summary
[2-3 sentence overview including pipeline stage bottlenecks]
### Scores by Category
| Category | Score | Status | Key Signal |
|----------|-------|--------|------------|
| Domain/History | X/10 | [Status] | siteAuthority |
| Technical SEO | X/15 | [Status] | titlematchScore |
| Content Quality | X/25 | [Status] | contentEffort |
| Keyword Optimization | X/10 | [Status] | Topicality |
| E-E-A-T Signals | X/20 | [Status] | Trust signals |
| AI/GEO Readiness | X/10 | [Status] | Extractability |
| User Behavior | X/10 | [Status] | NavBoost |
### Pipeline Bottleneck Analysis
[Which pipeline stage is the primary blocker? Mustang quality? Topicality match? NavBoost signals?]
### Priority Issues (Fix First)
1. **[Issue]** - [Impact] - [Fix] - [Signal affected]
2. **[Issue]** - [Impact] - [Fix] - [Signal affected]
3. **[Issue]** - [Impact] - [Fix] - [Signal affected]
### Domain Context
[Sandbox status, authority inheritance, URL history]
### Technical SEO Findings
[Detailed findings with specific recommendations]
### Content Quality Findings
[contentEffort indicators, token optimization, freshness]
### Keyword Analysis
[Primary keyword performance, semantic gaps, entity coverage]
### E-E-A-T Assessment
[Specific signals present/missing, disconnected entity risk]
### AI Visibility Assessment
[GEO readiness score and improvements]
### User Behavior Optimization
[Click quality, dwell time, engagement improvements]
### Action Plan
**Immediate (This Week):**
- [ ] Action 1
- [ ] Action 2
**Short-term (This Month):**
- [ ] Action 1
- [ ] Action 2
**Ongoing (Sandbox Graduation):**
- [ ] Consistent quality publishing
- [ ] Social signal building
- [ ] Backlink acquisition
- [ ] User engagement optimization
90-100: Excellent - Minor optimizations only 70-89: Good - Some improvements needed 50-69: Needs Work - Significant gaps to address Below 50: Critical - Major overhaul required
New Domain Adjustment: Subtract 10-15 points for sandbox limitations. Focus on graduation signals.
For faster audits, focus on:
titlematchScore?contentEffort indicators present?Deliver top 5 issues with affected signals and fixes.
For detailed signal documentation, see:
resources/google-ranking-signals.yaml
Key signals to remember:
titlematchScore - Title relevance to querycontentEffort - ML-assessed content investmentOriginalContentScore - Uniqueness (0-512 scale)semanticDate - Actual freshness of facts/sourcessiteAuthority - Domain-level authority capchromeInTotal - Popularity via Chrome dataCreating algorithmic art using p5.js with seeded randomness and interactive parameter exploration. Use this when users request creating art using code, generative art, algorithmic art, flow fields, or particle systems. Create original algorithmic art rather than copying existing artists' work to avoid copyright violations.
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