Fix Keyword Cannibalization Using Google Search Console

    1

    Get every keyword cannibalization pair in your GSC export, scored HIGH/MED/LOW, with a merge, canonical or differentiate action per page.

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    Fix Keyword Cannibalization Using Google Search Console

    Example session with this skill installed

    "Check stridelab for cannibalization with gsc-query-page.csv, brand is stridelab."

    • Read your context and instructions
    • Compiled the fix keyword cannibalization

    → Rows read: 28 | queries: 16 | cannibalised queries: 7 (HIGH 1, MEDIUM 3, LOW 3)
    → [HIGH 13.2] "best running shoes for flat feet": 3 URLs, primary /blog/flat-feet-running-shoes holds 64% of 66 clicks (spread 7.4 pos)
    → /blog/replace-running-shoes <> /blog/shoe-lifespan: 2 shared queries, weaker-page overlap 86% → MERGE
    → /blog/clean-running-shoes <> /blog/clean-running-shoes/: → CANONICAL
    → /blog/trail-vs-road <> /shoes/trail: → DIFFERENTIATE (blog vs category, keep both)

    Connects securely to your tools. The creator never sees your data.

    About this skill

    The problem Two of your posts rank for the same keyword, Google keeps swapping them, and neither one breaks into the top 3. Finding these pairs means cross-referencing thousands of query and page rows by hand, so most sites never do it.

    What it does

    • Reads a Search Console query + page export offline (API, Search Analytics for Sheets, Looker Studio or BigQuery)
    • Scores every cannibalised query HIGH, MEDIUM or LOW by how evenly clicks are split, and names the URL that's winning
    • Catches technical duplicates (trailing slash, case, tracking parameters) that only need a canonical
    • Groups competing page pairs and suggests MERGE, DIFFERENTIATE, CANONICAL or MONITOR. It won't tell you to merge a blog post into a category page.
    • Writes a fix playbook and a re-check plan so you can prove the change worked

    Why this beats prompting it yourself An LLM guessing from page titles produces hypotheses. This runs a deterministic script on your real click and impression numbers, so the same export always gives the same answer, and every recommendation traces back to a row in your data.

    Use cases

    • Pruning and consolidating a blog with years of overlapping posts
    • Explaining "ranking flip-flops" to a client with evidence
    • Checking a content sprint didn't create competing articles

    Pairs with: Content Refresh Audit, SERP Intent Mapper, Topical Authority Map.

    How to use

    Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.

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