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    Search Console Quick Win Cannibalization Auditor

    1

    Audit GSC exports for keyword cannibalization and high-potential SEO quick wins using deterministic Python analysis.

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    Search Console Quick Win Cannibalization Auditor

    Search Console Quick Win Cannibalization Auditor

    Example session with this skill installed

    I've run the audit on my GSC exports for example.com. Here is the audit.json output. Summarize the high-priority cannibalization candidates and top 3 quick wins.

    • Read your context and instructions
    • Compiled the search console quick

    I have analyzed the GSC audit results for example.com. The data covers 90 days of performance data with 1,240 query-page pairings processed.

    Search Console Audit Summary

    CategoryFindingDetails
    Quick Win"headless cms review"Pos: 6.2, Gap: 450 clicks, CTR: 1.2% (Ref: 4.5%)
    Quick Win"best static site generators"Pos: 11.4, Gap: 310 clicks, CTR: 0.8% (Ref: 2.1%)
    Quick Win"jamstack deployment guide"Pos: 4.8, Gap: 280 clicks, CTR: 3.1% (Ref: 6.8%)
    Cannibalization"api authentication"URL A (45% share, Pos 3) vs URL B (38% share, Pos 5)
    Cannibalization"webhooks vs polling"URL C (30% share, Pos 12) vs URL D (28% share, Pos 14)

    High Priority Cannibalization: "api authentication"

    The query "api authentication" shows two competing URLs with high impression share. example.com/blog/api-auth and example.com/docs/authentication are both ranking in the top 5. This is a high-priority candidate for consolidation as they likely serve the same search intent.

    Next steps
    1.

    Validate Search Intent: Check if "api authentication" users prefer the tutorial (blog) or the technical spec (docs).
    2.

    Review Internal Linking: Ensure internal anchor text isn't confusing search engines by pointing both terms to different pages.
    3.

    Optimize Quick Win 1: Refresh the meta title for "headless cms review" to improve the CTR, as it is significantly below the 4.5% benchmark.

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

    About this skill

    Search Console data is often too noisy to act on without manual filtering. This skill automates the detection of SEO "quick wins" and keyword cannibalization by processing Google Search Console (GSC) exports through deterministic Python scripts. It moves beyond eyeballing spreadsheets by calculating click gaps and identifying URLs competing for the same search intent.

    What it does

    • Calculate click gaps by comparing observed CTR against configurable position-based reference benchmarks.
    • Identify quick wins for queries and pages ranking between positions 4 and 20 with high impression volume but low CTR.
    • Detect cannibalization by flagging instances where multiple URLs capture significant impression share for the same query.
    • Prioritize review using high-priority flags for competing URLs that sit within five positions of each other.
    • Export multi-format reports including Markdown, HTML, JSON, and CSV for integration into other SEO workflows.

    How it works

    1. Prepare exports from GSC including Top Queries, Top Pages, and the paired Query x Page report.
    2. Execute the auditor via the provided Python script, passing the CSV paths and property metadata as arguments.
    3. Review findings in the generated audit.md or quick_wins.csv files to see prioritized opportunities.
    4. Validate intent using the model's interpretation of the script's output to decide on redirects, canonicals, or content updates.

    Frameworks & tools

    The core logic uses Python 3.10+ for offline data processing. It requires no third-party dependencies or network calls, ensuring client data stays local.

    Why this beats prompting it yourself

    LLMs frequently hallucinate arithmetic or lose track of thresholds when processing large CSV files. This skill uses a deterministic script for calculations and uses the model only for strategic interpretation, ensuring accuracy in click-gap estimates and cannibalization flagging.

    Use cases

    • Identify low-hanging fruit by finding high-impression keywords stuck on the second page of search results.
    • Resolve keyword conflict by detecting URLs that split traffic and dilute ranking signals for specific queries.
    • Audit new clients quickly by generating a baseline performance report from their historical GSC data.
    • Prioritize content updates based on calculated traffic potential rather than subjective sorting.

    Known limitations

    Requires English GSC headers for CSV parsing. Cannibalization detection requires the specific paired query-page export. The tool does not crawl live pages or inspect SERP features.

    Find more skills like this on the Claude Skills Marketplace, the Codex Skills Marketplace and the Gemini Skills Marketplace, or browse Claude Skills for Research & Analysis.

    Requires English GSC headers. Cannibalization detection needs the paired query-page export. Does not crawl live sites or SERPs. Python 3.10+ required.

    How to use

    1. 1

      Connect your agent to the Agensi MCP

      Copy the prompt and paste it into Claude, ChatGPT, Cursor or any agent that supports MCP. It sets up the connection for you. Manual setup

    2. 2

      Sign in to Agensi

      Your agent shows a sign-in link. Sign in and click Allow.

    3. 3

      Ask for what you need

      Your agent loads this skill and uses it on your task. It always asks before buying a skill.

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    Software
    Python

    Permissions

    Terminal / Shell
    Read Files
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    Listed23 days ago

    What's inside

    Frequently Asked Questions

    This skill solves the 'noise' problem in GSC data by using deterministic Python scripts to calculate exact click gaps and identify specific URLs competing for the same search intent, which is much more accurate than manual spreadsheet filtering or asking an LLM to guess at numbers.

    You will need the standard 'Top Queries,' 'Top Pages,' and the paired 'Pages/Queries' report exported from your GSC property in CSV format. The skill requires these specific exports to accurately map cannibalization and calculate traffic potential.

    The purchase includes the core Python auditing script, a pre-configured AI agent profile to interpret the results, and documentation on how to execute the analysis and read the priority flags.

    Yes, it is designed for any agent capable of executing Python 3.10+ code. It processes data locally within the agent's sandbox environment, meaning your sensitive GSC data never leaves your environment or makes external API calls.

    The script currently looks for standard English GSC headers (e.g., 'Clicks', 'Impressions', 'Position'). If your GSC interface is in another language, you will need to map your headers to these English equivalents for the parser to function.

    No, this is a deterministic local tool. It analyzes your historical GSC data to find 'quick wins' and 'cannibalization,' but it does not require a live internet connection to crawl your website or check real-time SERPs.

    New in Research & Analysis