/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
• Review paragraph and sentence pacing in context; lengths are descriptive,

not universal pass/fail thresholds

• 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 whether important sections are self-contained and evidence-backed
• Evaluate purpose fit and entity clarity; question headings and FAQs are optional
• Check whether summaries and structured formats help the intended reader
• 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 an