Audit: SEO & AEO Infrastructure

📡 SEO/AEO Infrastructure Audit

Date: 2026-02-12 Scope: Hydration Pipeline, Astro Components, and Schema Strategy. Verdict: STRUCTURALLY SOUND, DATA STARVED.

1. Executive Summary

The “Machine Readability” failure identified in the Legacy Review is NOT a content problem. It is a Pipeline Defect.

• The Pipeline (Hydration): Successfully injects deep forensic data (War Stories, Complexity) but ignores surface-level metadata (Title, Description).
• The Broadcaster (JSON-LD): Contains a critical typo (compentencies) and targets non-existent schema fields, rendering it partially blind.
• The Result: You have a “Ferrari Engine” (Forensic Content) inside a “Cardboard Box” (Generic Metadata).

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2. Infrastructure Analysis

A. The Hydration Gap (Critical)

Target: scripts/hydrate_content.py

The script is designed to inject new forensic schemas (scars, events), but it explicitly skips standard frontmatter fields.

• Evidence: The script has handlers for forensic_metrics, tags, reports, etc., but ZERO logic to update title or description from the Source JSON.
• Consequence: When you update notebook_dumps/c24.json with a killer description, it never reaches c24.mdx. The site continues to display the placeholder “Other project”.

B. Schema Drift (JSON-LD)

Target: src/js/jsonLD.ts

The standard Project schema generation is broken due to code-schema mismatch.

• Typo: Code attempts to read projectFrontmatter.compentencies (sic).
• Schema Mismatch: The content.config.ts defines additionalSkills and tags, but jsonLD.ts ignores tags completely.
• Impact: Google/Perplexity sees your projects as having Zero Keywords.

C. Social Graph (Open Graph)

Target: src/components/Seo/Seo.astro

• Status: HEALTHY.
• Logic: Correctly maps og:time, og:image, and twitter:card.
• Caveat: Because of the Hydration Gap (Point A), these healthy tags are being populated with generic data. Fixing A fixes C.

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3. AEO Assessment (Answer Engine Optimization)

Your “Forensic Architecture” is naturally AEO-friendly (high density of “How” and “Why”), but technical barriers prevent ingestion.

Factor	Status	Notes
Semantic Structure	🟢 STRONG	H2/H3 heirarchy in MDX is excellent for RAG.
Entity Graph	🔴 WEAK	JSON-LD is minimal. Missing connections (sameAs, knowsAbout).
Audio-Readiness	🟢 STRONG	audio_url and transcript fields exist in schema (ready for podcast ingestion).

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4. Strategic Recommendations

Phase 1: The “Metadata Bridge” (High ROI)

Modify hydrate_content.py to enforce title and description from the Source JSON.

• Why: Immediately fixes the “Dreamjob” SEO failure.
• Effort: Low (10 lines of Python).

Phase 2: Schema Hardening

Refactor jsonLD.ts to use the actual Zod schema fields.

• Fix: keywords = tags.join(", ")
• Why: Tells Google exactly what the project is (e.g., “Thermal Engineering”, “Crisis Management”).

Phase 3: The “Entity” Upgrade (Future)

Enhance Project schema to include about (linking to Concepts) and mentions (linking to Tools).