seo-programmatic

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Plan and audit programmatic SEO pages generated at scale from structured data. Use when designing templates, URL systems, internal linking, quality gates, and index-bloat safeguards for pages at scale.

AI & Automation 39,227 stars 6374 forks Updated today 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. ## When to Use - Use when the user wants programmatic SEO planning or review. - Use when designing templates, data-driven pages, or scalable URL systems. - Use when preventing thin content and index bloat across large page sets. ## 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 t...

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Author
sickn33
Repository
sickn33/antigravity-awesome-skills
Created
4 months ago
Last Updated
today
Language
Python
License
MIT

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