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:
references/quality-scoring.md-- full scoring checklistreferences/eeat-signals.md-- E-E-A-T evaluation criteria
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
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 | TL;DR box, callouts, varied content blocks |
| Grammar/anti-pattern | 3 | Passive voice ≤10%, AI trigger words ≤5/1K, transition words 20-30%, clean prose |
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 |
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"
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 |
Step 5: Generate Report
Default output format (Markdown):
## Blog Quality Report: [Title]
**Score: [X]/100** -- [Rating]
### Score Breakdown
| Category | Score | Max | Notes |
|----------|-------|-----|-------|
| Content Quality | X | 30 | [1-line summary] |
| SEO Optimization | X | 25 | [1-line summary] |
| E-E-A-T Signals | X | 15 | [1-line summary] |
| Technical Elements | X | 15 | [1-line summary] |
| AI Citation Readiness | X | 15 | [1-line summary] |
| **Total** | **X** | **100** | |
### AI Content Risk
- **Burstiness score**: [X]/10 ([human-like / moderate / flat])
- **AI phrases detected**: [N] ([list phrases found])
- **Vocabulary diversity (TTR)**: [X] ([high / acceptable / low])
- **AI probability**: [X]% -- [No concern / Review recommended / High risk]
- **Flagged passages**: [quote specific flat or formulaic sections, if any]
### Issues Found
#### Critical (Must Fix)
- [ ] [Issue with specific location and fix]
#### High Priority
- [ ] [Issue with specific location and fix]
#### Medium Priority
- [ ] [Issue with specific location and fix]
#### Low Priority
- [ ] [Issue with specific location and fix]
### Quick Stats
- Word count: [N]
- Paragraphs: [N] (X over 150 words)
- H2 sections: [N] (X as questions, X with answer-first formatting)
- Statistics: [N] sourced / [N] unsourced
- Images: [N] (X with alt text, formats: ...)
- Charts: [N] (types: ...)
- Internal links: [N]
- External links: [N] (tier breakdown: ...)
- Schema types: [list]
- OG/social tags: [present/missing]
### Recommended Actions
1. [Most impactful fix -- Critical items first]
2. [Second most impactful]
3. [Third]
Run `/blog rewrite <file>` to apply these optimizations automatically.
Export Formats
Default: Markdown Report
Standard detailed report as shown above.
JSON Export (--format json)
Machine-readable output for integration with CI/CD or dashboards:
{
"file": "post.md",
"title": "...",
"score": 78,
"rating": "Acceptable",
"categories": {
"content_quality": { "score": 22, "max": 30 },
"seo_optimization": { "score": 18, "max": 25 },
"eeat_signals": { "score": 12, "max": 15 },
"technical_elements": { "score": 13, "max": 15 },
"ai_citation_readiness": { "score": 13, "max": 15 }
},
"ai_detection": {
"burstiness": 6.2,
"ai_phrases_found": ["Furthermore", "Let's explore"],
"ttr": 0.44,
"ai_probability": 32
},
"issues": {
"critical": [],
"high": [],
"medium": [],
"low": []
}
}
Table Export (--format table)
Compact summary for quick review:
File | Score | Rating | Content | SEO | EEAT | Tech | AI-Ready | AI Risk
post.md | 78 | Acceptable | 22/30 | 18/25 | 12/15 | 13/15 | 13/15 | 32%
Batch Mode
When given a directory or --batch flag, scan for blog files and produce a
summary table. Use --sort score to order by score (ascending by default).
## Blog Audit Summary: [N] Posts Analyzed
| File | Score | Rating | Content | SEO | EEAT | Tech | AI-Ready | AI Risk | Top Issue |
|------|-------|--------|---------|-----|------|------|----------|---------|-----------|
| post-1.md | 85 | Strong | 26/30 | 20/25 | 13/15 | 14/15 | 12/15 | 18% | Missing OG tags |
| post-2.md | 42 | Rewrite | 10/30 | 8/25 | 5/15 | 9/15 | 10/15 | 71% | 12 fabricated stats |
| post-3.md | 71 | Acceptable | 20/30 | 16/25 | 10/15 | 12/15 | 13/15 | 25% | No answer-first |
### Priority Queue (Lowest Scoring First)
1. post-2.md (42) -- Full rewrite needed, high AI content risk
2. post-3.md (71) -- Answer-first formatting + stats needed
3. post-1.md (85) -- Add OG tags, minor polish
Run `/blog rewrite <file>` on each, starting from lowest score.