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small fix, don't worry about it
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Tomas Dvorak
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# Core Web Vitals Thresholds (INP-era)
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Use field data first (CrUX/RUM), then lab data for diagnosis.
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## Primary Metrics
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| Metric | Good | Needs Improvement | Poor |
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|--------|------|-------------------|------|
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| LCP | <= 2.5s | > 2.5s and <= 4.0s | > 4.0s |
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| INP | <= 200ms | > 200ms and <= 500ms | > 500ms |
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| CLS | <= 0.10 | > 0.10 and <= 0.25 | > 0.25 |
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## Supporting Diagnostics
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Use these as diagnostic hints, not ranking stand-ins:
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| Metric | Suggested Target |
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|--------|------------------|
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| TTFB | <= 0.8s |
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| FCP | <= 1.8s |
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| Total Blocking Time (lab) | <= 200ms |
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| Main-thread long tasks | < 50ms each where possible |
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## Measurement Hierarchy
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1. Field data source priority:
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- CrUX (origin + URL level if available)
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- First-party RUM
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2. Lab data source priority:
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- PageSpeed Insights lab profile
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- Lighthouse (mobile + throttled) for reproducible debugging
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## Audit Instructions
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1. Compare mobile first, then desktop.
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2. Classify each page template by CWV risk:
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- Critical: poor in any primary metric on key template
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- High: needs improvement on key conversion template
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- Medium: issue on secondary templates
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3. Map root causes:
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- LCP: render-blocking CSS/JS, slow HTML, heavy hero image
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- INP: long JS tasks, expensive handlers, third-party script contention
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- CLS: unsized media, ad/embed shifts, late DOM injections
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4. Propose one quick win and one structural fix per failing metric.
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## Rules
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- Always use INP. Do not reference FID as a current optimization target.
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- Avoid over-claiming from synthetic tests alone.
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- If field data is unavailable, clearly label findings as provisional.
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# E-E-A-T Evaluation Framework
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Use this rubric to evaluate content quality for competitive queries.
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## Dimensions
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Score each dimension from 0 to 5:
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| Dimension | What to Check |
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|-----------|---------------|
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| Experience | First-hand evidence, practical examples, real usage context |
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| Expertise | Subject competence, technical accuracy, depth, specialist language used correctly |
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| Authoritativeness | Reputation signals, citations, recognized brand/person entities |
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| Trustworthiness | Accuracy, transparency, policy clarity, editorial rigor, safety disclosures |
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## Query Stakes Classification
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Classify intent before scoring:
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- YMYL-high: finance, health, legal, safety
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- Commercial-high: high-ticket buying decisions
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- Informational-competitive: dense SERP competition with strong entities
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- Informational-low: low-risk, basic educational intent
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Raise trust requirements for higher-stakes intents.
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## Evidence Checklist
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Review per template:
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1. Authorship clarity:
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- named author
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- credentials or role
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- updated timestamp
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2. Source quality:
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- primary sources cited
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- external corroboration
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- quote/context integrity
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3. Editorial quality:
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- original analysis (not paraphrased commodity text)
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- internal consistency across pages
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- clear claims with evidence
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4. Trust assets:
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- about page and editorial policy
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- contact and business identity
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- privacy and terms links
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5. User value:
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- intent match
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- completeness
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- actionable depth
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## Risk Flags
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Flag as high risk when you find:
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- anonymous or credential-free expert claims on high-stakes pages
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- copied or near-duplicate boilerplate across key landing pages
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- medical/financial/legal recommendations without sourcing
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- exaggerated claims without verifiable proof
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## Scoring Output Format
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Return:
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1. Overall content quality score (0-100)
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2. Per-dimension scores mapped from rubric
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3. Top 3 trust risks
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4. Fastest high-impact remediation actions
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## Remediation Patterns
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- Add expert-reviewed blocks with named reviewers on high-stakes pages.
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- Replace generic intros with first-hand evidence and data-backed claims.
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- Add citation discipline: primary sources first, then secondary context.
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- Standardize author bylines, update dates, and revision ownership.
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# Content Quality Gates
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Use these gates before recommending scale, templates, or programmatic rollout.
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## Thin Content Thresholds
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Apply minimum effective content targets by page type:
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| Page Type | Minimum Body Content | Minimum Unique Content | Notes |
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|-----------|----------------------|------------------------|-------|
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| Homepage | 500 words | 70% | Differentiate value proposition and audience |
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| Service page | 700 words | 70% | Include process, proof, and FAQs |
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| Location page | 600 words | 60% | Require city-specific proof and context |
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| Product category | 350 words | 65% | Add buyer guidance, not filler |
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| Product detail | 250 words | 80% | Unique specs, differentiators, and use cases |
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| Blog/article | 900 words | 75% | Include original insights and references |
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| Competitor comparison | 1000 words | 80% | Require side-by-side evidence |
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If intent is fully satisfied with less copy, allow exception only with strong evidence (high engagement and conversion outcomes).
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## Scaled Location Page Rules
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- Warning threshold: 30+ location pages.
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- Hard stop threshold: 50+ location pages.
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- Require at least 60% unique content per location page.
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- Require at least 3 localized proof elements per page:
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- local testimonials/case studies
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- location-specific service details
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- local team, office, or coverage details
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At hard stop, ask for explicit user justification before proceeding with large-scale rollout.
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## Programmatic SEO Gates
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Before recommending programmatic generation:
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1. Confirm variable set produces real informational differences.
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2. Confirm pages can include unique utility (data, tools, comparisons, or local proof).
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3. Reject if output would only swap tokens (city/product names) in near-identical copy.
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4. Define canonicalization and indexability strategy up front.
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## Duplication Risk Bands
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| Similarity Between Pages | Risk Level | Action |
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|--------------------------|------------|--------|
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| < 40% shared text | Low | Proceed |
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| 40% to 60% shared text | Medium | Add differentiators before publishing |
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| > 60% shared text | High | Block rollout until content model is redesigned |
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## Schema Content Policy
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- Do not recommend HowTo schema as a growth recommendation.
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- Limit FAQ schema recommendations to government and healthcare sites.
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- Ensure structured data mirrors visible page content.
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## Gate Outcome Labels
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Return one of:
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- `pass`: Meets thresholds
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- `pass_with_risk`: Can ship with documented constraints
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- `fail`: Block and redesign content strategy
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Always include concrete remediation steps for `pass_with_risk` and `fail`.
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# Schema Type Guidance
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Prefer JSON-LD. Validate syntax and eligibility before recommending implementation.
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## Recommended by Site Type
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| Site Type | Primary Schema | Secondary Schema |
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|-----------|----------------|------------------|
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| SaaS | `SoftwareApplication`, `Organization` | `Product`, `BreadcrumbList`, `WebSite` |
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| Local service | `LocalBusiness` (or subtype), `Organization` | `Service`, `Review`, `BreadcrumbList` |
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| E-commerce | `Product`, `Offer`, `AggregateRating` | `Organization`, `BreadcrumbList`, `ItemList` |
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| Publisher | `Article`/`NewsArticle`, `Person`, `Organization` | `BreadcrumbList`, `ImageObject` |
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| Agency | `Organization`, `Service` | `FAQPage` (restricted use), `BreadcrumbList` |
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## Important Eligibility Notes
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- Do not recommend `HowTo` as an SEO growth tactic.
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- Recommend `FAQPage` only for government and healthcare use cases.
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- Keep markup aligned with visible on-page content.
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- Prefer one coherent graph over fragmented duplicate blocks.
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## Global Baseline Types
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Implement these where applicable:
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- `Organization` with logo, URL, sameAs
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- `WebSite` with `SearchAction` if site search exists
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- `BreadcrumbList` on hierarchical pages
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## E-commerce Essentials
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For product pages, include:
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- `name`
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- `description`
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- `image`
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- `sku` or `gtin` when available
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- `brand`
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- `offers` (`price`, `priceCurrency`, `availability`, `url`)
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- `aggregateRating` only when genuine ratings exist
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## Publisher Essentials
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For article pages, include:
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- `headline`
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- `datePublished`
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- `dateModified`
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- `author`
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- `publisher`
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- `mainEntityOfPage`
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- `image`
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## Validation Workflow
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1. Detect current schema blocks and type coverage.
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2. Validate syntax and required fields.
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3. Check content parity (structured data must match rendered content).
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4. Remove conflicting or duplicate nodes.
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5. Prioritize schema by impact:
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- Critical: invalid structured data causing parsing failures
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- High: missing core schema for primary page type
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- Medium: missing enrichment fields
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- Low: optional graph enhancements
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