Content Cannibalization

Content Cannibalization Detector & Resolver

You find cannibalization and recommend the specific fix. Cannibalization is the single most common cause of "we have lots of content but rankings are stuck" — and it's invisible without joining GSC query-level data with the site's page intents.

============================================================ === PRE-FLIGHT ===

• GSC data: from /gsc-pull skill (or direct connection).
• Page-to-intent map: for each URL, which target query is it written for? If unknown, derive from H1 + meta description.
• Crawl data: title, H1, canonical, noindex, content embedding (from /internal-link-graph if available).
• Authority signal: backlinks per page (from Ahrefs/Majestic if accessible; otherwise GSC referring domain proxy).
• Action capacity: how many redirects / consolidations / canonical edits can the team execute monthly? (Drives prioritization.)

Recovery:

• No backlink data: use internal PageRank from /internal-link-graph as authority proxy.
• No intent map: auto-derive via top GSC query per page (with a "MUST_VERIFY" flag on results).

============================================================ === PHASE 1: CANNIBALIZATION DETECTION ===

For each unique query in GSC (filter to impressions ≥ 50 in window):

def detect_cannibalization(gsc_rows, min_impressions=50, position_max=50):
    """
    Returns clusters where a single query has 2+ URLs both within
    position 1-50 with non-trivial impressions.
    """
    by_query = defaultdict(list)
    for row in gsc_rows:
        if row.impressions >= min_impressions and row.position <= position_max:
            by_query[row.query].append(row)
    
    clusters = {}
    for query, rows in by_query.items():
        if len(rows) >= 2:
            clusters[query] = sorted(rows, key=lambda r: -r.clicks)
    return clusters

Output cannibalization_clusters.csv:

VALIDATION: Detection produces non-zero clusters on any site with > 500 pages and > 100 ranking queries.

============================================================ === PHASE 2: ROOT-CAUSE CLASSIFICATION ===

For each cluster, classify the cannibalization type:

Detection per class:

• True duplicate: embedding cosine ≥ 0.95 across competing URLs.
• Intent overlap: SERP intent signal (Google AI Overview vs ten blue links vs shopping ads) suggests one canonical intent; multiple pages target different but Google merges.
• Template confusion: URL pattern suggests filter/variant (?color=, ?sort=, /category/page/2/).
• Variant leakage: URL has query string or session ID.

VALIDATION: Each cluster has a class label + confidence (0-1).

============================================================ === PHASE 3: RESOLUTION RECOMMENDATIONS ===

Per cluster, produce one of five resolutions:

A. Consolidate-and-301 (best for true duplicates):

• Identify "winner" (highest authority + best historical clicks + best target intent fit).
• All losers 301-redirect → winner.
• Merge unique content from losers into winner (don't lose value).
• Update internal links to point to winner.

B. Canonicalize (best for variant leakage):

• Set <link rel="canonical" href="https://example.com/canonical"> on variants.
• Don't 301 (preserve UX for filtered nav).

C. Differentiate-intent (best for intent overlap):

• Keep both pages. Rewrite each to target distinct intent.
• Page A → transactional ("buy X"); Page B → informational ("what is X").
• Update internal links to disambiguate.

D. Noindex-the-weaker (best for template / pagination):

• <meta name="robots" content="noindex,follow"> on weaker pages.
• Or use rel="next/prev" pagination semantics where applicable.

E. Update-internal-links-only (lightest touch, valid when both pages should stay live):

• Reroute internal links so the intended page gets all the link equity.
• Don't change URL structure.

Per cluster, output resolution_{cluster_id}.md with:

• Recommended resolution (A-E)
• Reasoning (which signals drove it)
• Exact code/redirects/canonical tags to add
• Internal link updates needed
• Expected click recovery (sum of cluster clicks × consolidation multiplier 1.2-1.8)

VALIDATION: Recommendation per cluster is concrete and tied to evidence.

============================================================ === PHASE 4: REDIRECT RULES & CANONICAL PATCHES ===

Generate platform-specific implementation:

Nginx:

location = /blog/old-url { return 301 /blog/winner-url; }

Apache (.htaccess):

RewriteRule ^blog/old-url$ /blog/winner-url [R=301,L]

Next.js (next.config.js):

async redirects() {
  return [
    { source: '/blog/old-url', destination: '/blog/winner-url', permanent: true },
    ...
  ]
}

Cloudflare Workers / Vercel vercel.json / Netlify _redirects:

/blog/old-url  /blog/winner-url  301

WordPress (Redirection plugin export):

source,target,type
/blog/old-url,/blog/winner-url,301

Canonical tag patches for "B. Canonicalize":

<link rel="canonical" href="https://example.com/canonical-url">

VALIDATION: Generated redirects parse correctly in their target stack.

============================================================ === PHASE 5: RECOVERY MODEL ===

Estimate post-fix click recovery:

Expected clicks per cluster after fix =
  (sum of impressions in cluster) × (CTR at winner's new expected position)

Position lift from consolidation:
  if total cluster clicks > 100 and lift ~ -1 to -3 positions (closer to top)
  use CTR-by-position curve (Google avg: pos 1=27%, pos 2=15%, pos 3=11%, pos 4=8%, pos 5=7%, etc.)

For each cluster:

• Current cluster total clicks/week
• Expected clicks/week after fix
• Net gain
• Confidence (high if true duplicate, medium for intent split, low for soft variants)

Aggregate: total expected click gain across all cannibalization fixes. Prioritize highest-gain × lowest-effort first.

VALIDATION: Recovery model uses real CTR-by-position curves, not made-up multipliers.

============================================================ === PHASE 6: ACTION QUEUE ===

Generate action_queue.md ordered by impact × inverse effort:

# Cannibalization Action Queue — {site}

## P0 — High impact, low effort
1. [Consolidate] "best CRM" cluster: 301 /blog/reviews + /pricing → /blog/best-crm. Expected: +180 clicks/week. Effort: 30 min. Risk: low.

## P1 — High impact, medium effort
2. [Differentiate] "API rate limits" cluster: rewrite /docs/api-limits to focus on technical reference vs /blog/api-rate-limits which keeps tutorial focus. Expected: +90 clicks/week. Effort: 4 hours.

## P2 — Polish
3. [Canonicalize] "/?utm_source=*" variants: add canonical pointing to clean URL. Expected: +5 clicks/week (de-duped indexing). Effort: 1 hour.

VALIDATION: Action queue has per-item expected gain + effort.

============================================================ === SELF-REVIEW ===

• Complete: Detection + classification + 5 resolution types + redirect patches + recovery model?
• Robust: Handles variant leakage with query strings? Avoids over-consolidating (some pairs SHOULD stay separate)?
• Clean: Recommendations are platform-specific (Nginx / Next.js / WordPress)?
• SEO-credible: Would an Ahrefs power-user accept the analysis as production-ready?

Common gap: recommending 301 on a page that's still gaining traffic. Always check trend before consolidating — growing-but-second-best may overtake.

============================================================ === LEARNINGS CAPTURE ===

~/.claude/skills/content-cannibalization/LEARNINGS.md.

============================================================ === STRICT RULES ===

• Never recommend a 301 without first checking that both pages have the same primary intent. Intent splits don't consolidate, they differentiate.
• Never propose a redirect that creates a chain (A→B→C). Always squash to A→C directly.
• Never assume cluster sizes > 2 = always bad. Sometimes Google legitimately surfaces multiple URLs (e.g., subdirectory + a sub-page).
• Always update internal links AFTER 301-ing. Otherwise the redirects hop forever and crawl budget burns.
• Always re-pull GSC after 4-8 weeks to verify recovery model was correct. Refine assumptions over time.