blog-rewrite

# Blog Rewriter: Optimize Existing Posts

Rewrites and optimizes existing blog posts for dual ranking: Google search
and AI citation platforms. Preserves the author's voice while applying the
6 pillars of optimization.

**Key references:**
- `references/quality-scoring.md` - 5-category scoring (Content 30, SEO 25, E-E-A-T 15, Technical 15, AI Citation 15)
- `references/eeat-signals.md` - Experience, expertise, authority, trust markers
- `references/internal-linking.md` - Linking strategy and anchor text rules
- `references/visual-media.md` - Image sourcing and chart styling
- `skills/blog/references/synthesis-contract.md` - 6 LAWs for re-citation hygiene during rewrite (v1.8.0; cross-skill ref lives in the orchestrator's references dir)
- `skills/blog/references/research-quality.md` - cross-source clustering for replacement-statistic research (v1.8.0)

## Cross-reference

For 21 evidence-led optimization prompts (AI-detector test, CTR audit, schema, PAA rewording, technical audit, ChatGPT visibility) directly applicable to rewrite work, see `/blog flow optimize`.

## Workflow

### Phase 1: Audit (Read-Only)

1. **Read the blog post** - Detect format (MDX, markdown, HTML)
2. **Run the quality checklist** against `references/quality-scoring.md`:
   - Count fabricated vs sourced statistics
   - Check answer-first formatting (H2 -> stat in first sentence?)
   - Count images and charts (type diversity?)
   - Measure paragraph lengths (any > 150 words?)
   - Check heading hierarchy (H1 -> H2 -> H3, no skips?)
   - Look for FAQ schema
   - Check freshness signals (lastUpdated, dateModified)
   - Assess self-promotion level
   - Evaluate citation tier quality
3. **AI content detection scan**:
   - **Burstiness score** - Measure sentence length variance across the post. Low
     variance (most sentences within 3-5 words of each other) is a strong AI signal.
     Calculate: standard deviation of sentence word counts. Target SD > 6.
   - **Known AI phrase scan** - Check for these high-frequency AI phrases:
     - "in today's digital landscape", "it's important to note", "dive into"
     - "game-changer", "navigate the landscape", "revolutionize", "seamlessly"
     - "cutting-edge", "harness the power of", "leverage" (as verb)
     - "delve", "crucial", "elevate", "foster", "landscape" (overused)
     - "multifaceted", "robust", "tapestry", "embark"
     - Full list in `agents/blog-writer.md`
   - **Vocabulary diversity** - Calculate Type-Token Ratio (TTR): unique words /
     total words. Low TTR (< 0.40) suggests AI-generated repetitive phrasing.
     Target TTR > 0.50 for natural prose.
   - **AI content percentage estimate** - Based on burstiness, phrase density, and
     TTR, estimate what percentage of the content reads as AI-generated (0-100%).
     Report as: "AI content estimate: ~X%"
   - **Second-order structural reflex scan** (v1.8.0) - The first-order checks above
     are vocabulary-level. The second-order pass catches what survives them: structural
     and rhythmic tics LLMs default to after the obvious words are replaced. Run against
     `skills/blog/references/ai-slop-detection.md`. Flag at minimum:
     - Question-cadence H2s above 70% of headings
     - Three or more "Here..." paragraph openers
     - Three-clause sentence rhythm above 50% in any 200-word window
     - More than 2 hedge words ("may," "often," "typically," "generally") in any 20-word span
     - Symmetric-list bloat (list-item word-count SD below 5)
     - More than 2 wrap-up rhetorical questions ("What does this mean for...?")
     - More than half of H2 openers starting with a transition word
     - "The key insight is..." or "What's important here is..." as sentence openers
     - Listicle pre-list intro above 250 words
     - Opening-word repetition: top three first-words above 25% share
     - Paragraph-shape SD below 25 (visual monotony)
     A draft is only "AI-detection clean" when both passes are clean. The two-namespace
     terminology (first-order/second-order for slop-detection vs Tier 1/2/3 for source
     authority) is intentional: see `skills/blog/references/ai-slop-detection.md` for
     why the labels diverged in v1.8.1.
4. **Video embed check**:
   - Count existing YouTube embeds in the post
   - If 0 embeds, flag: "No video embeds. YouTube has the strongest AI visibility correlation (0.737)"
   - If present, check: lazy loading? aria-labels? noscript fallback? VideoObject schema?
5. **Cannibalization check**:
   - Identify the post's primary keyword from title, H1, and first paragraph
   - Search the blog directory for other posts targeting the same keyword:
     - Grep headings and meta descriptions across all blog posts
     - Flag any posts with significant keyword overlap
   - If cannibalization found, report:
     - Which posts compete for the same keyword
     - Recommend: **merge** (combine into one stronger post) or **differentiate**
       (shift one post to a related but distinct keyword)
6. **Calculate current score** across 5 categories:
   - Score across 5 categories (Content Quality 30, SEO Optimization 25, E-E-A-T Signals 15, Technical Elements 15, AI Citation Readiness 15)
   - Total: 0-100
7. **Present audit summary** with specific findings, AI detection results, video status, cannibalization status, and score
8. **Enter plan mode** - Present section-by-section optimization plan

Wait for user approval before proceeding.

### Phase 2: Research

1. **Identify the blog's core topic** from existing content
2. **Find replacement statistics** for any fabricated/unsourced data:
   - Search: `[topic] study 2025 2026 data statistics`
   - Target tier 1-3 sources only
3. **Find images** if post has fewer than 3:
   - Pixabay: `site:pixabay.com [topic keywords]`
   - Unsplash: `site:unsplash.com [topic keywords]`
   - Verify each URL returns HTTP 200
   - If nanobanana-mcp is configured, offer AI generation for missing/insufficient images via `blog-image`
4. **Plan charts** if post has