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Free tier (rule engine):
- Title length: tier 30–60 = pass, <30 warn, >70 warn. Also detects
error-page titles (502 Bad Gateway etc.) so a misread of a downed
site doesn't silently produce a clean report.
- Meta description length: 50–160 = pass, otherwise warn.
- Twitter Card detection (card/title/description/image).
- Content volume / word count: 300+ = pass, 100–300 warn, <100 fail.
- Heading hierarchy: counts h1/h2/h3, warns if total <3.
- Internal vs external link count: warns if <5 internal links.
- Recommendations now include copy-pasteable code snippets for OG,
Twitter Card, canonical, lang, security.txt, and Organization
JSON-LD — matches roboauto's "AI Visibility Fixes" pattern.
Premium tier (LLM engines):
- New "AI Recognition" section in SECTIONS list.
- Claude system prompt instructs the model to honestly self-report
whether the target appears in its training data BEFORE any web
search, with a "recognition.training_data" finding. Priority 3
(informational), not a defect.
- Markdown report template reflows: AI Recognition slots in after
LLM Accessibility, pushing other sections up one number.
The non-Claude engines (openai/gemini/qwen/kimi/deepseek) use the
same SECTIONS list via shared prompt.ts, so the section is offered
to all paid engines — though prompt-side enforcement remains
Anthropic-only for now.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Copy file name to clipboardExpand all lines: lib/audit/claude-engine.ts
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@@ -58,6 +58,15 @@ For each finding:
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- detail: one sentence with the WHY plus the specific evidence (e.g. the actual H1 text, the actual robots.txt line)
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- priority: 1 (critical, breaks AEO entirely) to 5 (polish)
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For the "AI Recognition" section, BEFORE doing any web_fetch / web_search, answer honestly from your own training data:
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- Do you recognize this company / domain from training? (status: "pass" if yes with substantive recall, "warn" if vague, "fail" if not at all, "unknown" if uncertain)
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- check_key: "recognition.training_data"
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- title: "Familiar with <domain>" or "No prior knowledge of <domain>"
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- detail: One sentence. If you recognize it, name 1–2 concrete things you remember (what they do, who they serve, notable products). If you don't, say so plainly — do NOT speculate or paraphrase web search results.
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- priority: 3 (informational signal, not a defect)
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This section is a signal of training-data presence, not a quality judgment. Sites that don't appear in training can still be excellent — but the user should know whether they're invisible to the dominant AI surfaces.
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For score: weigh critical fails heavily. A site missing schema, blocking GPTBot in robots.txt, or hiding all content behind JS should score below 50. A clean, well-instrumented site should score 80+.
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For summary.pass/warn/fail/unknown: count each across all findings.
@@ -76,10 +85,12 @@ For markdown: produce the complete report in Markdown with these exact section h
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## 4. Schema / Structured Data Audit
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## 5. robots.txt and sitemap.xml Audit
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## 6. LLM / AI Crawler Accessibility
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## 7. Positioning Clarity
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## 8. Missing or Hard-to-Find Information
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## 9. Recommended Fixes
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## 10. Priority To-Do Checklist
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## 7. AI Recognition
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(One paragraph stating whether you recognize this site from training data, and what specifically you remember if so. No fluff.)
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## 8. Positioning Clarity
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## 9. Missing or Hard-to-Find Information
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## 10. Recommended Fixes
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## 11. Priority To-Do Checklist
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Use ✅ / ⚠️ / ❌ / ❓ status emojis on each bullet. Quote actual content from the site. Section 10 must be reusable checkboxes ("- [ ] **P1** Add JSON-LD Organization schema").
"Helps AI engines serve localized results and confirms content language.\n\n"+
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"```html\n<html lang=\"en\">\n```",
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},
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"homepage.load_time": {
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title: "Speed up homepage rendering",
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how: "AI crawlers commonly time out around 3s. Cache the HTML, ship less JS for the first paint, and pre-render the hero section server-side.",
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},
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"homepage.word_count": {
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title: "Add more substantive homepage content",
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how: "AI models need 300+ words of visible body text to summarize and recommend a site. Add a value-prop paragraph, a short FAQ, and a 'how it works' section.",
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},
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"homepage.heading_structure": {
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title: "Add structured headings",
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how: "Use h2 for each section and h3 for sub-points. AI uses these to outline and chunk the page.",
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},
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"homepage.internal_links": {
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title: "Add internal navigation links",
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how: "Top nav + footer with links to /pricing, /docs, /about, /contact gives AI crawlers an entry point to the rest of the site.",
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