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    programmatic seo factory

    1

    Builds pSEO pipelines anchored to Google's spam policies to prevent scaled content abuse penalties.

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    See it in action

    You say

    I have a CSV of 500 CRM tools. I want to build a "CRM vs [Competitor]" vertical. Here is my template and the data source. Help me design the pipeline and internal linking.

    Your agent does

    Pipeline designed.

    • Threshold: Min 4 unique feature comparisons per page.
    • Excluded: 42 pages with thin data noindexed.
    • Linking: Hub pages organized by 'Industry Focus' with unique aggregate insights.
    • Schema: Review markup bound only to tools with verified ratings.

    What you get

    Design data-binding logic that avoids 'scaled content abuse' triggers.Automate noindexing for thin or near-empty combinatorial pages.Generate valid JSON-LD schema tied only to visible on-page elements.Architect hub-and-spoke internal linking to eliminate orphan pages.

    About this skill

    The problem

    Programmatic SEO often triggers Google's scaled content abuse penalties. Generating thousands of pages via simple token substitution usually results in thin content, doorway pages, and manual actions that can tank an entire domain.

    What it does

    • Builds a template-to-data pipeline that forces differentiated content beyond simple string replacement.
    • Automates the exclusion or noindexing of thin combinatorial pages based on a justified content threshold.
    • Maps JSON-LD schema markup strictly to visible page content to avoid rich result penalties.
    • Designs internal linking architectures that prevent orphan pages and doorway patterns.
    • Performs entity deduplication to prevent near-identical URLs from competing for the same search intent.

    Why this beats prompting it yourself

    Most LLMs generate generic SEO advice that ignores the technical nuances of Google's spam policies. This skill anchors every architectural decision to specific Google Search Central documentation, ensuring your pSEO section is built to survive manual reviews and algorithmic updates.

    Use cases

    • Generating "Service in [City]" pages with unique local data and synthesized observations.
    • Building "Tool A vs Tool B" comparison engines without repetitive boilerplate sentences.
    • Creating directory-style hub pages that provide actual category-level value.
    • Binding complex Product or Review schema to data-driven page templates safely.

    Known limitations

    Does not perform live SEO audits or fact-check the accuracy of your underlying data source. It focuses on architecture and policy compliance rather than content creativity.

    How to install

    Drop the file into your AI Agent. Works with Claude, Cursor, ChatGPT, and 20+ more.

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