Blog Analyzer: Quality Audit & Scoring
Scores blog posts on a 0-100 scale across 5 categories and provides prioritized improvement recommendations. Includes AI content detection analysis. Works with local files or published URLs.
Reference documents (paths from repo root):
skills/blog/references/quality-scoring.md: full scoring checklistskills/blog/references/eeat-signals.md: E-E-A-T evaluation criteriaskills/blog/references/ai-slop-detection.md: two-tier reflex methodology (v1.8.0)skills/blog/references/editorial-heuristics.md: ordinal 0-4 rubric, P0-P3 severity (v1.8.0, used with--rubric)skills/blog/references/cognitive-load.md: per-section concept density (v1.8.0, used with--cognitive-load)
Input Handling
- Local file: Read the file directly
- URL: Fetch with WebFetch, extract content
- Directory: Scan for blog files, audit all (batch mode)
- Flags:
--format json|table,--batch,--sort score,--rubric,--cognitive-load
Optional Modes (v1.8.0)
--rubric: in addition to the 100-point score, emit the ordinal 0-4 editorial-heuristics rubric with P0-P3 severity tags. Seeskills/blog/references/editorial-heuristics.md. The 100-point JSON schema is preserved; the rubric is added as a siblingrubricfield.--cognitive-load: runscripts/cognitive_load.pyagainst the post and embed the per-section load heatmap as a siblingcognitive_loadfield. Seeskills/blog/references/cognitive-load.md.
Both modes are additive. The default behavior (no flags) is unchanged from v1.7.1.
Scoring Process
Step 1: Content Extraction
Read the blog post and extract:
- Frontmatter (title, description, date, lastUpdated, author, tags)
- Heading structure (H1, H2, H3 with hierarchy)
- Paragraph count and word counts per paragraph
- Statistics (any number claims with or without sources)
- Images (count, alt text presence, format)
- Charts/SVGs (count, type diversity)
- Links (internal, external, broken)
- FAQ section presence
- Schema markup (types present)
- Meta tags (title, description, OG tags, twitter cards)
- Sentence lengths for burstiness analysis
- Vocabulary tokens for diversity scoring
Step 2: Score Each Category
Load references/quality-scoring.md for the full checklist. Score each:
Content Quality (30 points)
| Check | Points | Pass Criteria |
|---|---|---|
| Depth/comprehensiveness | 7 | Covers topic thoroughly, no major gaps |
| Readability (Flesch 60-70) | 7 | Flesch 60-70 ideal, 55-75 acceptable; Grade 7-8; Gunning Fog 7-8 |
| Originality/unique value markers | 5 | Original data, case studies, first-hand experience |
| Sentence & paragraph structure | 4 | Avg sentence 15-20 words, ≤25% over 20; paragraphs 40-80 words; H2 every 200-300 words |
| Engagement elements | 4 | Summary box, callouts, varied content blocks. Accepts: "TL;DR", "Key Takeaways", "The Bottom Line", "What You'll Learn", "At a Glance", "In Brief" |
| Grammar/anti-pattern | 3 | Passive voice ≤10%, AI trigger words ≤5/1K, transition words 20-30%, clean prose |
Readability Bands (apply per persona, or use default):
| Audience | Flesch Grade | Flesch Ease | Scoring Impact |
|---|---|---|---|
| Consumer | 6-8 | 60-80 | Full points if in range |
| Professional | 8-10 | 50-60 | Full points if in range |
| Technical | 10-12 | 30-50 | Full points if in range |
| Default (no persona) | 7-8 | 60-70 | Current scoring unchanged |
Content clarity is the #2 factor for AI citation probability (+32.83% score differential). Average US adult reads at 7th-8th grade level.
SEO Optimization (25 points)
| Check | Points | Pass Criteria |
|---|---|---|
| Heading hierarchy with keywords | 5 | H1 -> H2 -> H3, no skips, keyword in 2-3 headings |
| Title tag (40-60 chars, keyword, power word) | 4 | Front-loaded keyword, positive sentiment |
| Keyword placement/density | 4 | Natural integration, no stuffing, in first 100 words |
| Internal linking (3-10 contextual) | 4 | Descriptive anchor text, bidirectional |
| URL structure | 3 | Short, keyword-rich, no stop words, lowercase |
| Meta description (150-160 chars, stat) | 3 | Fact-dense, includes one statistic |
| External linking (tier 1-3) | 2 | 3-8 outbound links to authoritative sources |
E-E-A-T Signals (15 points)
| Check | Points | Pass Criteria |
|---|---|---|
| Author attribution (named, with bio) | 4 | Real name, credentials, not sales pitch |
| Source citations (tier 1-3, inline) | 4 | 8+ unique stats, zero fabricated |
| Trust indicators | 4 | Contact page, about page, editorial policy |
| Experience signals | 3 | "When we tested...", original photos/data |
When scoring source citations under E-E-A-T, evaluate whether each public statistic carries the FLOW evidence triple: year anchor in prose, inline citation with publisher and title, URL with retrieval date in the source block. Posts that cite tier 1-3 sources but lack retrieval dates score lower on this subcategory than posts that include the full triple. See skills/blog/references/flow-alignment.md for the standard.
Technical Elements (15 points)
| Check | Points | Pass Criteria |
|---|---|---|
| Schema markup (3+ types = bonus) | 4 | BlogPosting + FAQ + Person minimum |
| Image optimization | 3 | AVIF/WebP, descriptive alt text, lazy except LCP |
| Structured data elements | 2 | Tables, lists, comparison blocks |
| Page speed signals | 2 | LCP < 2.5s, no render-blocking JS |
| Mobile-friendliness | 2 | Responsive, tap targets 48px+ |
| OG/social meta tags | 2 | og:title, og:description, og:image, twitter:card |
AI Citation Readiness (15 points)
| Check | Points | Pass Criteria |
|---|---|---|
| Passage-level citability (120-180 words) | 4 | Self-contained sections with stat + source |
| Q&A formatted sections | 3 | 60-70% of H2s as questions, FAQ present |
| Entity clarity | 3 | Unambiguous topic entity, consistent terminology |
| Content structure for extraction | 3 | Answer-first, tables with thead, comparison formats |
| AI crawler accessibility | 2 | SSR/SSG, no JS-gated content |
Step 3: AI Content Detection
Analyze the post for AI-generated content risk:
Burstiness Score (sentence length variance):
- Calculate standard deviation of sentence lengths across the post
- Human writing: high variance (short punchy + long complex sentences)
- AI writing: low variance (consistently medium-length sentences)
- Score: 0-10 scale (10 = very human-like burstiness)
Known AI Phrase Detection: flag occurrences of these 17 phrases:
- "It's important to note"
- "In today's digital landscape"
- "Delve into"
- "Navigating the complexities"
- "Let's explore"
- "Furthermore"
- "In conclusion"
- "It is worth mentioning"
- "Embark on"
- "Cutting-edge"
- "Leverage" (as a verb, non-financial context)
- "Game-changer"
- "Revolutionize"
- "Streamline"
- "Harness the power"
- "Dive deep"
- "Unlock the potential"
- Em dashes (-) - count all instances, flag as AI writing pattern
Vocabulary Diversity (Type-Token Ratio):
- Calculate unique words / total words
- Human writing: TTR typically 0.4-0.6 for long-form
- AI writing: TTR often below 0.35 (repetitive vocabulary)
AI Content Risk Assessment:
- Flag if AI probability > 50% based on combined signals
- Provide specific passages that triggered the flag
- Recommend humanization: personal anecdotes, varied sentence rhythm, domain jargon
Step 4: Determine Rating
| Score | Rating | Action |
|---|---|---|
| 90-100 | Exceptional | Publish as-is, flagship content |
| 80-89 | Strong | Minor polish, ready for publication |
| 70-79 | Acceptable | Targeted improvements needed |
| 60-69 | Below Standard | Significant rework required |
| < 60 | Rewrite | Fundamental issues, start from outline |