Analyze and Plan Programmatic SEO at Scale

This skill builds and audits SEO pages generated at scale from structured data sources, enforcing quality gates to prevent thin-content penalties and index bloat. It assesses the underlying data source (CSV/JSON, API, or database) for row count, field uniqueness, missing values, near-duplicate records (over 80% field overlap), and freshness. It plans templates around variable injection points (title, H1, body, meta, schema), distinguishing static shared blocks from dynamic per-page content, explicitly warning against "mad-libs" patterns that just swap a city or product name into identical text. It defines URL pattern strategy (common patterns like /[city]/[service] or /glossary/[term], lowercase hyphenated slugs, uniqueness enforcement, under 100 characters, no query params) and internal-linking automation (hub/spoke category pages, 3-5 related-item links per page, BreadcrumbList schema, cross-linking by shared attributes). Its core thin-content safeguards define quality gates: a warning at 100+ pages published without content review, a hard stop at 500+ pages without justification, flagging pages under 40% unique content or under 300 words. It documents the 2025-2026 enforcement context for Google's Scaled Content Abuse policy (June 2025 manual-action wave, August 2025 SpamBrain update, a reported 45% reduction in low-quality unoriginal search results post-March-2024 enforcement) and recommends enhanced gates: 30-40%+ genuine content differentiation between any two pages, a 5-10% human-review sample before publishing, progressive rollout in batches of 50-100 pages with 2-4 weeks of monitoring before scaling further, a "standalone value" test per page, and awareness of site-reputation-abuse risk when publishing programmatic content under someone else's high-authority domain. It lists safe programmatic patterns (integration pages with real setup docs, glossary pages with 200+ word definitions, unique product/data pages) versus penalty-risk patterns (city-name-swap location pages, unsupported "best X for Y" pages, template boilerplate exceeding 60% of content), with an explicit uniqueness-percentage formula. It defines canonical strategy (self-referencing canonicals, parameter-variation handling, pagination via rel=next/prev or canonical to page 1), sitemap integration (auto-generated entries, 50,000 URL split limit, accurate lastmod, noindexed-page exclusion), and index-bloat prevention (noindexing low-value pages, faceted-navigation handling, crawl-budget monitoring above 10k pages, thin-page consolidation, monthly indexed-count audits). Output is a Programmatic SEO Score (0-100) with per-category status (Data Quality, Template Uniqueness, URL Structure, Internal Linking, Thin Content Risk, Index Management), issues prioritized Critical to Low, and specific recommendations.

Use it when planning or reviewing SEO pages generated at scale from structured data - designing templates, data-driven pages, or scalable URL systems - specifically to prevent thin content and index bloat. On an unreachable URL it reports the connection error; if no programmatic page pattern is detected it says so rather than assuming one exists; crossing the thin-content warning threshold triggers a reported percentage and requires user acknowledgment; crossing the hard-stop threshold (500+ pages without justification, or under 30% unique content) halts analysis and requires explicit user approval to continue.

Inputs: the structured data source powering the pages (CSV/JSON/API/database), the intended template and URL pattern, and the target publishing scale.
Outputs: a Programmatic SEO Score with category breakdown, prioritized issues, and recommendations covering data quality, template uniqueness, URL structure, internal linking, thin-content risk, and index management.

SEO and growth teams building or auditing large-scale, data-driven page sets (location pages, integration directories, glossaries, product catalogs) who need enforced thin-content and index-bloat safeguards rather than ad hoc scaling.