blog-audit

name	blog-audit
description	Full-site blog health assessment scanning all blog files for quality scores, orphan pages, topic cannibalization, stale content, and AI citation readiness. Runs canonical batch analysis before site-wide checks. Produces per-post scores and a prioritized action queue. Use when user says "audit blog", "blog audit", "site audit", "blog health", "audit all posts", "check all blogs".
user-invokable	true
argument-hint	[directory]
license	MIT

Blog Audit: Full-Site Health Assessment

Performs a comprehensive blog health assessment across all posts in the project. Scans for quality scores, orphan pages, topic cannibalization, stale content, and AI citation readiness. Uses the canonical analyzer JSON as the score source and produces a prioritized action queue.

Audit Process

Step 1: Discover Blog Files

Scan the project for all blog content files:

• Recursively glob for .md, .mdx, .html, .astro, .svelte, .vue, .tsx, and .jsx in common blog directories and CMS export folders
• Common paths to check:
  • content/
  • posts/
  • blog/
  • src/content/
  • _posts/
  • pages/blog/
  • articles/
  • content/blog/**
  • CMS export folders explicitly provided by the user
  • src/pages/blog/
• Filter out hidden, vendor, generated, and secret-adjacent paths: .git/, dot-directories, node_modules/, vendor/, dist/, build/, .next/, coverage/, reports/, generated exports, README, CHANGELOG, LICENSE, config files, SKILL.md, package files, .env*, keys, and private notes
• Report: "Found N blog files in [directories]"

If no blog files are found in standard locations, ask for an allow-listed root or only search user-approved content directories. Do not scan the entire project root by default.

Step 2: Canonical Batch Analysis

Run canonical analyzer output first and use it as the source of per-post scores:

python3 scripts/analyze_blog.py <blog-root> --batch --format json

Process files in chunks, cap parallel follow-up work to a small fixed number, respect context limits, and aggregate deterministic JSON with file, score, categories, issues, and metadata. Layer the site-wide checks below on top of analyzer JSON, not separate scoring rubrics.

Content Quality Layer

• Score each post on the 30-point content quality scale
• Check paragraph length (target 40-80 words, hard limit 150)
• Check sentence length (target 15-20 words)
• Evaluate heading structure and question-format headings
• Assess readability using persona and content type: consumer content favors easier bands, professional content can be moderate, and technical content may be denser when clarity remains high

SEO Optimization Layer

• Check on-page SEO elements per post:
  • Title tag length (40-60 acceptable, 50-60 ideal, preview warning only)
  • Meta description is concise and page-specific. Statistics are optional and must be visible and sourced
  • H1 presence and uniqueness
  • Image alt text coverage
  • Internal and external link counts
  • URL slug quality

Schema Validation Layer

• Detect structured data across all posts
• Validate Article/BlogPosting, Person, Organization, and BreadcrumbList schema completeness
• If FAQPage exists, validate it as optional entity markup only, not a Google rich result
• Normalize dateModified, lastUpdated, updated, and lastmod, including timezone-normalized generated schema, then require freshness parity
• Flag missing or malformed schema

Link Health Layer

• Map internal links across all posts
• Build a directed link graph
• Detect orphan pages (zero inbound internal links)
• Detect dead-end pages (zero outbound internal links)
• Check for broken internal link targets
• Recommend bidirectional link opportunities

Freshness Check Layer

• Read lastUpdated or dateModified from each post's frontmatter
• Calculate days since last update
• Flag freshness by content type, source or statistic age, and GSC decay, not by a universal day count
• Categorize by refresh priority

AI Readiness Layer

• Score each post for AI citation readiness
• Check passage-level citability (120-180 word sections)
• Evaluate Q&A formatting and entity clarity
• Check for TL;DR boxes and citation capsules
• Check robots.txt, llms.txt, SSR/SSG output, JS-gated content, blocked assets, GPTBot, ClaudeBot, PerplexityBot, Googlebot, and Google-Extended policies

Step 2.5: Technical Crawl and Search Performance

Add site-wide technical checks before final recommendations:

• Validate sitemap coverage, robots.txt, noindex directives, canonical tags, redirects, HTTP status codes, hreflang, and internal canonical consistency
• Use blog-google when available for Core Web Vitals, GSC queries, URL Inspection, indexing status, and GA4 context
• Report skipped optional checks with reasons such as SKIPPED: credentials unavailable

Step 3: Topic Cannibalization Detection

Analyze across all posts for keyword competition:

1. Extract primary keyword/topic from each post:
   • Title text
   • H1 heading
   • Meta description
   • First paragraph
2. Normalize keywords with stopword handling, lemmatization, locale awareness, and intent modifiers
3. Cluster by intent using analyzer data, embeddings or explicit confidence, GSC query-to-URL data when available, and SERP overlap where available
4. Flag competing posts with one of these recommendations:
   • Merge: Combine two weak posts into one strong post
   • Redirect: 301 redirect the weaker post to the stronger one after preserving backlinks, validating a redirect map, and updating internal links
   • Differentiate: Adjust focus so posts target distinct intents

Step 4: Orphan Page Detection

Build and analyze the internal link graph:

1. Normalize URLs against site config and sitemap, including relative links, same-domain absolutes, trailing slashes, generated routes, anchors, and slug mappings
2. Build an adjacency map: { page -> [pages it links to] }
3. Build a reverse map: { page -> [pages linking to it] }
4. Identify orphan pages: posts with zero inbound internal links
5. Identify dead-end pages: posts with zero outbound internal links
6. For each orphan, recommend 2-3 existing posts that should link to it based on topic relevance

Step 5: Stale Content Detection

Audit content freshness across all posts:

1. Read frontmatter fields: lastUpdated, dateModified, date, updated
2. Calculate days since last update for each post
3. Categorize by refresh priority:
   • High: Volatile topic, stale sources or statistics, or GSC decay
   • Medium: Evergreen topic with aging examples, links, or screenshots
   • Low: Recently validated or stable reference content
4. Estimate refresh effort per post:
   • Light refresh: Update statistics, check links (1-2 hours)
   • Moderate refresh: Rewrite sections, add new data (3-4 hours)
   • Heavy refresh: Full rewrite recommended (5+ hours)

Step 6: Generate Site-Wide Report

Aggregate all results into a comprehensive report:

Summary Dashboard

## Blog Audit Report

**Audit Date:** [date]
**Total Posts:** N
**Average Score:** XX/100

### Health Overview
| Metric | Count |
|--------|-------|
| Posts Scoring 90+ (Excellent) | N |
| Posts Scoring 70-89 (Good) | N |
| Posts Scoring 50-69 (Needs Work) | N |
| Posts Scoring <50 (Poor) | N |
| Orphan Pages | N |
| Dead-End Pages | N |
| Cannibalization Issues | N |
| Stale or Decaying Content | N |

Per-Post Table

### Per-Post Scores
| Post | Score | Content | SEO | E-E-A-T | Technical | AI Citation | Issues |
|------|-------|---------|-----|---------|-----------|-------------|--------|
| [filename] | XX/100 | X/30 | X/25 | X/15 | X/15 | X/15 | [count] |

Prioritized Action Queue

### Prioritized Action Queue (Lowest Score First)
| Priority | Post | Score | Top Issue | Recommended Action |
|----------|------|-------|-----------|--------------------|
| 1 | [file] | XX | [issue] | [action] |
| 2 | [file] | XX | [issue] | [action] |

Cannibalization Report

### Topic Cannibalization
| Keyword | Competing Posts | Recommendation |
|---------|----------------|----------------|
| [keyword] | post-a.md, post-b.md | Merge / Redirect / Differentiate |

Orphan Pages

### Orphan Pages (No Inbound Links)
| Page | Inbound Links | Recommended Link Sources |
|------|---------------|--------------------------|
| [file] | 0 | post-a.md, post-b.md, post-c.md |

Stale Content

### Stale Content
| Post | Last Updated | Days Stale | Priority | Refresh Effort |
|------|-------------|------------|----------|----------------|
| [file] | [date] | [N] | High/Med/Low | Light/Moderate/Heavy |

Step 7: Save Report

Save timestamped Markdown and JSON exports under reports/, for example reports/blog-audit-YYYY-MM-DD.md and reports/blog-audit-YYYY-MM-DD.json. Do not overwrite a previous audit report.

After saving, inform the user:

• Report locations: [project-root]/reports/blog-audit-YYYY-MM-DD.md and [project-root]/reports/blog-audit-YYYY-MM-DD.json
• Summary of findings (total posts, average score, critical issues count)
• Suggest running /blog analyze <file> on the lowest-scoring post first
• Suggest running /blog flow optimize for AI-citation SEO checks on key posts

Cross-reference

For evidence-led audit prompts beyond this site-wide health pass, see /blog flow optimize (visibility, CTR, schema, extraction audits) and /blog flow win (dual-surface scorecard, conversion audit).