Keyword Cannibalization Audit

Keyword Cannibalization Audit

Audit a website for pages that compete with each other for the same query, and produce an actionable report. Findings are candidates to confirm — some overlap is legitimate; this skill surfaces the clusters and recommends a hierarchy.

When to use this skill

Use this skill when the user asks to:

• Audit or fix keyword cannibalization
• Find pages competing for the same keyword / query
• Decide which page should rank for a topic and what to do with the rest
• Review overlapping or competing content

Inputs to collect

1. Site URL or URL list — a live site root to crawl (auto-seeds from

/sitemap.xml), or a text file with --url-list.

1. Scope — pages to crawl (default 500, --max-pages).
2. Brand suffix — e.g. "| Acme Co" so brand words don't inflate title

similarity (--brand-suffix).

1. Overlap threshold — Jaccard similarity to flag competing pages (default

0.6, --overlap).

Workflow

Step 1: Crawl and capture target signals

Use scripts/extract_targets.py:

python3 scripts/extract_targets.py https://example.com --max-pages 500 --output target_inventory.json
# already crawled once (e.g. in a full SEO audit)? skip the crawl and reuse the shared cache:
# python3 scripts/fetch_pages.py https://example.com --output page_cache.json
# python3 scripts/extract_targets.py --from-cache page_cache.json --output target_inventory.json

For each page it records title, H1, meta description, noindex, word count, and the top body keywords by frequency.

Step 2: Run the audit checks

python3 scripts/audit_cannibalization.py target_inventory.json --brand-suffix "| Acme Co" --overlap 0.6 --output audit_report.json

Step 3: Evaluate the audit checks

Evaluate each check in references/audit-checks.md. Core checks:

| Check | Severity | |---|---| | Duplicate title target | High | | Overlapping target cluster (keyword signatures) | Medium | | Shared primary keyword (title bigram) | Low |

Step 4: Produce the report

Write a report following references/report-template.md: summary, duplicate title targets first, then overlapping clusters (largest first) each with a recommended hierarchy, and a prioritized action list.

Step 5: Recommend fixes

• For each cluster, name one primary page and a role for every other page:

differentiate to a distinct sub-query, merge, or 301/canonicalize into the primary.

• Frame as hierarchy, not deletion — supporting pages that target distinct

intents and link up to the primary are healthy.

• Always recommend confirming with Search Console query data before

consolidating: this audit infers competition from on-page signals.

• Related angles: meta-data-audit (duplicate titles) and

content-quality-audit (duplicate bodies).

• Base every grouping on observed signatures; never invent rankings.

Optional: export the report

As a Word document (.docx)

If the user wants the report as a .docx (for example, to share with stakeholders or attach to a ticket), save the Markdown report to a file and convert it:

python3 scripts/md_to_docx.py report.md --output report.docx

scripts/md_to_docx.py uses only the Python standard library (no pip install) and renders headings, tables, lists, links, bold/italic, and code blocks. Offer this whenever a user asks for a Word doc, a .docx, or a shareable/downloadable report.

As a CSV of findings (.csv)

If the user wants the raw findings as a spreadsheet (for filtering, sorting, or triage in Sheets or Excel), convert the audit's audit_report.json directly:

python3 scripts/findings_to_csv.py audit_report.json --output findings.csv

scripts/findings_to_csv.py is also standard-library only. It writes one row per finding, with check and severity columns prepended and list fields (e.g. the pages sharing a duplicate value) joined with ; . Unlike the .docx, which reformats the written report, the CSV is a direct dump of the structured findings — offer it whenever a user wants the data itself, a spreadsheet, or to slice findings by check or severity.

Resources

• scripts/md_to_docx.py — convert the Markdown report into a Word (.docx) document (standard library only)
• scripts/findings_to_csv.py — flatten the audit findings JSON into a CSV, one row per finding (standard library only)
• references/audit-checks.md — full definitions, thresholds, and rationale
• references/report-template.md — report output structure
• scripts/extract_targets.py — crawl and capture keyword-target signals
• scripts/audit_cannibalization.py — run cannibalization audit checks

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: JakeLabate
• Source: JakeLabate/Claude-SEO-Skills
• License: MIT
• Homepage: https://www.jakelabate.com/claude-seo-skills/

Install and usage instructions live in the source repository linked above.