Rewrite and optimize existing blog posts for Google rankings (December 2025 Core Update, E-E-A-T) and AI citations (GEO/AEO). Full rewrite for both Google rankings AND AI citations. For AI-citation-only audit (no Google work), use seo-geo instead. Replaces fabricated statistics with sourced data, applies answer-first formatting, adds Pixabay/Unsplash images, generates built-in SVG charts, injects FAQ schema, performs AI content detection, adds citation capsules and information gain markers, and updates freshness signals. Works with any blog format (MDX, markdown, HTML). Use when user says "rewrite blog", "optimize blog", "update blog", "improve blog", "fix blog", "refresh blog post", "blog optimization".
Installer avec Codex ou Claude Copiez ce prompt, collez-le dans Codex, Claude ou un autre assistant, puis laissez-le vérifier la page du skill et l'installer pour vous.
Une commande directe contourne le prompt de vérification. Examinez la source avant de l'exécuter.
Rewrite and optimize existing blog posts for Google rankings (December 2025 Core Update, E-E-A-T) and AI citations (GEO/AEO). Full rewrite for both Google rankings AND AI citations. For AI-citation-only audit (no Google work), use seo-geo instead. Replaces fabricated statistics with sourced data, applies answer-first formatting, adds Pixabay/Unsplash images, generates built-in SVG charts, injects FAQ schema, performs AI content detection, adds citation capsules and information gain markers, and updates freshness signals. Works with any blog format (MDX, markdown, HTML). Use when user says "rewrite blog", "optimize blog", "update blog", "improve blog", "fix blog", "refresh blog post", "blog optimization".
user-invokable
true
argument-hint
<file-path>
license
MIT
id
blog-rewrite
domain
content
invoked_by
["blog-writer"]
depends_on
[]
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/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)
Read the blog post - Detect format (MDX, markdown, HTML)
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?)
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)
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
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.
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?
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)
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
Present audit summary with specific findings, AI detection results, video status, cannibalization status, and score
Enter plan mode - Present section-by-section optimization plan
Wait for user approval before proceeding.
Phase 2: Research
Identify the blog's core topic from existing content
Find replacement statistics for any fabricated/unsourced data:
Search: [topic] study 2025 2026 data statistics
Target tier 1-3 sources only
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 seo-image-gen
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:
Select chart type using the diversity rule (no repeated types per post)
Pass: chart type, title, data values, source, platform format
Embed the returned SVG directly within a <figure> wrapper
Target 2-4 charts per 2,000-word post
See 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 seo-image-gen sub-skill (if nanobanana-mcp configured)
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
4d. Replace Fabricated Statistics
Search for patterns: "X% of...", "X out of Y...", unsourced claims
Replace with real data from tier 1-3 sources
Always include inline attribution: ([Source Name](url), year)
4e. Improve Headings
Convert statement headings to questions where natural (60-70% target)
Keep 2-3 statement headings for variety
Ensure keyword appears in 2-3 headings naturally
4f. Fix Paragraph Length
Split any paragraph > 150 words
Target 40-80 words per paragraph
Ensure each paragraph starts with its most important sentence
4g. Add Visual Elements
Embed new images after H2 headings, spaced evenly
Embed charts within relevant sections
If nanobanana-mcp configured: generate custom images for sections lacking good stock matches (invoke seo-image-gen sub-skill via Task)
Adapt embed format to detected platform (MDX vs markdown vs HTML)
4h. Add Video Embeds
If the post lacks YouTube video embeds:
Search 2-3 relevant videos using quality criteria from references/video-embeds.md
Embed using platform-appropriate format (srcdoc lazy loading)
Place: 1 after introduction, 1-2 in mid-article sections
Include noscript fallback for AI crawlers
4i. Add/Improve FAQ
If no FAQ exists, add one (3-5 questions)
If FAQ exists, ensure answers are 40-60 words with statistics
Add FAQ schema markup appropriate to platform
4j. Reduce Self-Promotion
Max 1 brand mention (author bio context only)
Remove "At [Company], we..." patterns
Convert promotional sections to educational content
4k. Citation Capsule Injection
For each H2 section, generate (or improve existing) a citation capsule:
40-60 word self-contained passage per H2
Contains: one specific claim + one data point + source attribution
Written in a declarative style so an AI system could extract and quote it directly
Placed naturally within the section body, not as a separate callout
Example:
According to a 2026 Gartner study, 58% of enterprise buyers now consult AI
assistants before contacting a vendor ([Gartner](https://www.gartner.com), 2026).
This shift means B2B content must answer specific questions concisely enough
for AI systems to extract and cite in their responses.
Capsules map to the "AI Citation Readiness" category (15 points) in
references/quality-scoring.md.
4l. Anti-AI-Detection Patterns
Apply these transformations to reduce AI-detectable writing patterns:
Eliminate em dashes - Replace every em dash (-) with a comma, hyphen (-),
colon, or period. Split sentences if needed. Em dashes are an AI writing tell.
Replace flagged phrases - Swap every detected AI phrase (from the scan in
Phase 1 step 3) with a natural alternative. Examples:
"it's important to note" -> "worth noting" or "keep in mind"
"in today's digital landscape" -> "right now" or "in [specific year]"
"leverage" -> "use", "apply", "take advantage of"
"delve" -> "look at", "explore", "dig into"
"robust" -> "strong", "solid", "reliable"
"crucial" -> "key", "essential", "critical" (or restructure the sentence)
Vary sentence length deliberately - After rewriting, scan each paragraph.
Inject short punchy sentences (5-10 words) between longer ones (18-25 words).
Target: no more than 3 consecutive sentences within 5 words of each other's length.
Inject rhetorical questions - Add at least one rhetorical question every
200-300 words to break up declarative monotony.
Use contractions naturally - Replace formal constructions with contractions
where they sound natural: "it is" -> "it's", "we have" -> "we've",
"do not" -> "don't", "is not" -> "isn't".
Include hedging language - Sprinkle first-person hedges that signal real
experience: "in our experience", "we've found that", "from what we've seen",
"this tends to", "it depends on".
4m. Summary Box (Key Takeaways)
If the post lacks a summary box, add one immediately after the introduction:
> **Key Takeaways**> - [Core finding with statistic and source]> - [Second key insight or recommendation]> - [Third actionable takeaway]> (3-5 bullets, 40-60 words combined. Self-contained - reader gets> the core value without reading the full article.)
Default label is "Key Takeaways", but this is configurable per persona or
brand voice (e.g., "The Bottom Line", "Quick Summary", "What You Need to Know").
If an existing TL;DR box is present, convert it to the bullet-point Key
Takeaways format. Verify it meets the 40-60 word requirement and contains
at least one statistic with source attribution.
4n. Information Gain Marker Injection
Review the post for original value and tag it:
[ORIGINAL DATA] - Any proprietary data, survey results, experiments, or
case study metrics the author collected first-hand
[UNIQUE INSIGHT] - Novel analysis, contrarian perspectives backed by data
If the post lacks original value markers:
Ask the author for first-hand data or experience to include
At minimum, add analytical insights that connect existing research in new ways
Target: at least 2-3 markers per post
Use HTML comments (<!-- [ORIGINAL DATA] -->) or visible callouts depending
on the post's style.
Phase 5: Verification
After rewriting, verify all quality gates pass:
Core Quality Gates
Every H2 opens with a statistic + source
No paragraph exceeds 150 words
Zero fabricated statistics
Heading hierarchy is clean
FAQ section present with schema
Images have descriptive alt text
Cover image present in frontmatter (coverImage + ogImage)
If MDX: build the project to verify no compilation errors
New Element Verification
TL;DR box present after introduction (40-60 words, contains statistic)
At least 2-3 information gain markers present
Citation capsules in major H2 sections (40-60 words, self-contained)
Internal linking zones marked or actual links present (5-10 per 2,000 words)
No AI-detectable phrases remain from banned list
Burstiness and Naturalness Check
Sentence length variance: SD > 6 (mix of short and long sentences)
Contractions used naturally throughout
Rhetorical questions present (1 per 200-300 words)
AI content estimate reduced from audit baseline
Score improved across all 5 categories vs Phase 1 audit
YouTube video embeds present with lazy loading, aria-labels, and noscript fallback
Phase 6: Summary
## Blog Optimization Complete: [Title]
### Score Change
- Before: [X]/100 ([Rating])
- Content Quality: [X]/30
- SEO Optimization: [X]/25
- E-E-A-T Signals: [X]/15
- Technical Elements: [X]/15
- AI Citation Readiness: [X]/15
- After: [Y]/100 ([Rating])
- Content Quality: [Y]/30
- SEO Optimization: [Y]/25
- E-E-A-T Signals: [Y]/15
- Technical Elements: [Y]/15
- AI Citation Readiness: [Y]/15
### AI Detection
- Before: ~[X]% AI-detected content
- After: ~[Y]% AI-detected content
- Phrases replaced: [N]
- Burstiness improved: [before SD] -> [after SD]
### Cannibalization
- [Status: none found / flagged N posts / resolved]
### Changes Made
- [X] statistics replaced with sourced data
- [X] SVG charts added (types: ...)
- [X] images added from Pixabay/Unsplash
- Answer-first formatting applied to [N] H2 sections
- FAQ schema injected with [N] questions
- TL;DR box: [added/updated]
- Information gain markers: [N] ([types])
- Citation capsules: [N] across H2 sections
- AI phrases replaced: [N]
- lastUpdated set to [date]
- Self-promotion reduced to [N] mentions
### Visual Elements
- Charts: [count] ([types])
- Images: [count]
- YouTube videos: [count] ([titles])
### Ready for
- `/blog analyze <file>` to verify final score
- Publishing / deploying
Phase 5.5: Delivery Contract Enforcement (v1.9.0)
Before presenting the rewritten draft, run the 5-gate delivery contract per skills/blog/references/blog-delivery-contract.md. The contract applies to rewrites the same way it applies to new posts: the user is never the first reviewer.
Steps:
Hero check: if the existing post already has a hero image referenced and still on disk, keep it. If the rewrite changed the topic substantially OR the hero is missing, regenerate via python scripts/generate_hero.py --topic "<new title>" --tags "<tags>" --out <folder>.
Re-render: run python scripts/blog_render.py --md <slug>.md --out-dir <folder> to refresh the .html and .pdf from the updated .md.
Reviewer dispatch: dispatch the blog-reviewer agent against the rendered .html. Threshold: score 90/100 or higher AND zero P0 issues.
Iterate on failure: maximum 3 iterations. After the 3rd failure, STOP and present the diagnostic from <folder>/preflight-report.json.
Rewrites have a higher implicit threshold because the existing draft was presumably already published. Re-presenting something worse than the original is not acceptable. If the rewritten score is lower than the original score, that itself is a P0 condition.
Update Mode
When invoked as /blog update <file>, focus on freshness:
Update statistics to latest available data (2025-2026)
Add new developments since last update
Refresh images if older than 1 year
Update lastUpdated in frontmatter
Preserve the existing structure - minimize rewrites
Target: at least 30% content change to register as "fresh" for AI crawlers
Identifies outdated content, scores decay/freshness, prioritizes refresh work, and produces update plans with GEO and republishing guidance.
Quick Start
Find content on [domain] that needs refreshing
Which of my blog posts have lost the most traffic?
Refresh this article for [current year]: [URL/content]
Update this content to outrank [competitor URL]: [your URL]
Create a content refresh strategy for [domain/topic]
Skill Contract
Expected output: a scored diagnosis, prioritized repair plan, and a short handoff summary ready for memory/audits/.
Reads: candidate URLs/content, traffic and ranking history, publish/update dates, and competitor examples.
Writes: a user-facing refresh plan (and optional refreshed content) plus a reusable summary that can be stored under memory/audits/.
Promotes: blocking defects, repeated weaknesses, fix priorities, and pending decisions to memory/open-loops.md.
Done when: decay drivers are identified with evidence; a refresh plan lists specific updates with a republish-date strategy; a Changes Made block and handoff summary are produced.
Primary next skill: use the Next Best Skill below when the repair path is clear.
Use ~~analytics, ~~search console, and ~~SEO tool when connected; otherwise ask for traffic data, ranking history, publish dates, candidate URLs, and competitor examples. See CONNECTORS.md.
Instructions
Label every metric Measured (tool/export), User-provided, or Estimated (model inference); never present an estimate as measured; if a required metric is unavailable, mark it N/A — do not invent it.
When a user requests content refresh help:
CORE-EEAT Quick Score — Estimate all 8 dimensions, prioritize red/yellow areas, and hand off to seo-content for full scoring when needed.
Identify Refresh Candidates — Use age, dated claims, declining traffic, lost rankings, broken links, SERP shifts, and missing topics.
Analyze Page-Level Decay — Compare 6-month-old vs current performance, keyword deltas, SERP intent, competitor updates, and the why-refresh rationale.
Define Updates Needed — Capture outdated elements, competitor/PAA gaps, SEO updates, GEO updates, links, images, sources, and dates.
Create Refresh Plan — Specify title, structure, new sections, refreshed statistics, internal/external links, images, and validation requirements.
Write Refresh Content — Draft updated intro, replacement sections, refreshed facts, FAQ answers, and Changes Made notes.
Optimize for GEO — Add 40-60 word definitions, quotable statements, Q&A, dated citations, and standalone factual statements.
Set Republishing Strategy — Use published-date update for 50%+ new content, last-updated date for 20-50%, original date for <20%; update schema, sitemap lastmod, cache, Search Console, and 4-6 week monitoring.
Create Refresh Report — Summarize completed changes, expected outcomes, owners, next review date, and open loops.
A page is decayed enough that a rewrite may beat a refresh (e.g., outdated premise, intent shift, or >50% of content stale) — state the finding and ask: (1) refresh in place, or (2) rewrite as new content via seo-content-writer.
Continue silently (never stop for):
Missing analytics/ranking history — score decay from on-page signals (dated claims, broken links, stale stats), label findings Estimated, and proceed.
A request to "refresh" content that is actually net-new (no existing URL) — note the mismatch once and route to seo-content-writer rather than fabricating a prior version.
Which republish-date treatment to apply — follow the Step 8 thresholds without asking.
Example
User: "Refresh my blog post about 'best cloud hosting providers'"
Output: CORE-EEAT quick score flags weak Referenceability, Experience, and Trust; recommends pricing refresh, broken-link fixes, author credential additions, affiliate disclosure, and a Changes Made block ready for republish.
Prioritize by ROI/search demand, make substantive improvements instead of date-only edits, add stronger evidence than competitors, track post-publish rankings/traffic, and treat every refresh as a GEO citation opportunity.
Ask to save results; if yes, write a dated summary to memory/audits/blog-rewrite/YYYY-MM-DD-<topic>.md. Hand off veto-level risks to the auditor gate before any hot-cache marker.
Gate check recommended: Run seo-content on refreshed content before republishing.
Reference Materials
Content Decay Signals — Decay indicators, lifecycle stages, and refresh triggers by content type
