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:

• skills/blog/references/quality-scoring.md - 5-category scoring (Content 30, SEO 25, E-E-A-T 15, Technical 15, AI Citation 15)
• skills/blog/references/eeat-signals.md - Experience, expertise, authority, trust markers
• skills/blog/references/internal-linking.md - Linking strategy and anchor text rules
• skills/blog/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 skills/blog/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?)
• Check schema presence and validity, prioritizing Article/BlogPosting, Person, Organization, and BreadcrumbList; FAQPage is optional entity markup only
• Check freshness signals (lastUpdated, dateModified)
• Assess self-promotion level
• Evaluate citation tier quality

1. 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.

1. Video embed check:

• Count existing YouTube embeds in the post
• If 0 embeds, flag: "No video embeds. Consider adding relevant high-quality YouTube embeds when they add useful context."
• If present, check: lazy loading? aria-labels? noscript fallback? VideoObject schema?

1. 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)

1. 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

1. Present audit summary with specific findings, AI detection results, video status, cannibalization status, and score
2. 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

1. Find images if post has fewer than 3:

• Prefer original screenshots, product visuals, diagrams, or data graphics when available
• For stock, use official provider APIs such as Openverse, Unsplash, Pexels, or Pixabay so license, creator, source URL, and download URL are captured
• Download approved assets locally, store attribution, and reject javascript:, data:, and file: URLs
• If blog-image is available, offer AI generation for missing or insufficient images and record the selected model ID

1. Plan charts if post has fewer than 2:

• Identify data suitable for visualization
• Select diverse chart types

Phase 3: Chart Generation (Built-In)

When the post needs more visual elements, invoke the blog-chart sub-skill:

1. Select chart type using the diversity rule (no repeated types per post)
2. Pass: chart type, title, data values, source, platform format
3. Embed the returned SVG directly within a <figure> wrapper
4. Target 2-4 charts per 2,000-word post

See skills/blog/references/visual-media.md for chart type selection and styling rules.

Phase 4: Content Rewrite

Apply changes in this order:

4a. Preserve What Works

• Keep the author's voice and unique perspective
• Preserve original insights and first-hand experience
• Keep existing quality images and charts
• Maintain internal links

4b. Fix Frontmatter

• Add lastUpdated: "YYYY-MM-DD" (today's date)
• Keep original date unchanged
• Fix meta description: fact-dense, 150-160 chars, includes 1 statistic
• Add coverImage + coverImageAlt + ogImage if missing
• Search Pixabay/Unsplash/Pexels for wide hero image (1200x630)
• Or generate custom SVG cover via blog-chart (text-on-gradient with key stat)
• Or generate custom AI image via blog-image sub-skill when available; record the model ID
• Verify tags/categories are appropriate

4c. Apply Answer-First Formatting

Every H2 section MUST open with a 40-60 word paragraph containing:

• At least one specific statistic with source attribution
• A direct answer to the heading's implicit question