seo-programmatic

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Programmatic SEO planning and analysis for pages generated at scale from data sources. Covers template engines, URL patterns, internal linking automation, thin content safeguards, and index bloat prevention. Use when user says "programmatic SEO", "pages at scale", "dynamic pages", "template pages", "generated pages", or "data-driven SEO".

AI & Automation 7,439 stars 1148 forks Updated 5 days ago MIT

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# Programmatic SEO Analysis & Planning Build and audit SEO pages generated at scale from structured data sources. Enforces quality gates to prevent thin content penalties and index bloat. ## Data Source Assessment Evaluate the data powering programmatic pages: - **CSV/JSON files**: Row count, column uniqueness, missing values - **API endpoints**: Response structure, data freshness, rate limits - **Database queries**: Record count, field completeness, update frequency - Data quality checks: - Each record must have enough unique attributes to generate distinct content - Flag duplicate or near-duplicate records (>80% field overlap) - Verify data freshness; stale data produces stale pages ## Template Engine Planning Design templates that produce unique, valuable pages: - **Variable injection points**: Title, H1, body sections, meta description, schema - **Content blocks**: Static (shared across pages) vs dynamic (unique per page) - **Conditional logic**: Show/hide sections based on data availability - **Supplementary content**: Related items, contextual tips, user-generated content - Template review checklist: - Each page must read as a standalone, valuable resource - No "mad-libs" patterns (just swapping city/product names in identical text) - Dynamic sections must add genuine information, not just keyword variations ## URL Pattern Strategy ### Common Patterns - `/tools/[tool-name]`: Tool/product directory pages - `/[city]/[service]`: Location + service pages ...

Details

Author
AgriciDaniel
Repository
AgriciDaniel/claude-seo
Created
3 months ago
Last Updated
5 days ago
Language
Python
License
MIT

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