Building programmatic SEO at scale with AI coding assistants

Programmatic SEO (pSEO) is the practice of generating hundreds or thousands of pages from structured data. Think of how Zapier has a page for every integration, Yelp has a page for every business in every city, or how comparison sites have pages for every "[product] vs [product]" combination.

When done well, pSEO captures long-tail search traffic that would be impossible to target with manually written pages.

When done poorly, it produces thin, repetitive content that Google penalizes.The difference between the two comes down to execution: data quality, content uniqueness, technical SEO foundations, and quality controls. These are complex to get right, especially at scale.

I've been building pSEO implementations for a while and noticed I was repeating the same patterns, making the same checks, and solving the same problems across projects. So I packaged everything into a set of reusable skills that AI coding assistants can follow.

What are these skills?

The pSEO Skills repository contains 12 modular skills that guide AI coding assistants through every phase of a pSEO implementation:

Discovery and planning

• pseo-discovery: Analyzes your codebase and business to identify what pages you should build and whether you have the data to support them
• pseo-audit: Checks if your codebase is ready for pSEO at scale

Data architecture

• pseo-data: Designs the structured data layer that powers all pages, including memory-safe patterns for handling thousands of records
• pseo-scale: Database-backed architecture for 10K-100K+ pages

Page generation

• pseo-templates: Builds page templates that produce unique, intent-matched content
• pseo-metadata: Implements dynamic titles, descriptions, canonical URLs, and social tags
• pseo-schema: Adds JSON-LD structured data for rich results

Site structure

• pseo-linking: Creates hub-spoke architecture, breadcrumbs, related pages, sitemaps, and redirects

Optimization

• pseo-performance: Optimizes build times, Core Web Vitals, and memory usage
• pseo-llm-visibility: Optimizes for citation in AI-generated answers (ChatGPT, Perplexity, Google AI Overviews)

Quality control

• pseo-quality-guard: Validates against thin content, duplicates, cannibalization, and Google's scaled content abuse patterns
• pseo-orchestrate: Coordinates all skills in the correct dependency order

Why build this as AI assistant skills?

pSEO implementations involve a lot of interconnected decisions. The data model affects the templates. The templates affect the metadata. The linking structure affects the schema. A mistake in one area cascades to others.

Traditional documentation helps, but you still need to manually apply it to your specific codebase. AI coding assistants can actually implement the patterns directly, adapting the code examples to your framework, your data sources, and your existing architecture.

By encoding the methodology as skills, the assistant knows not just what to do, but when to do it, what depends on what, and what quality checks to run before moving on.

What's different about this approach?

Google 2025 compliance built in. The skills incorporate the latest algorithm updates, including the scaled content abuse policies from March 2025, the E-E-A-T requirements, and the December 2025 "Needs Met" enforcement. Every template includes the signals Google now expects.

LLM visibility as a first-class concern. Traditional SEO optimizes for the 10 blue links. But AI-generated answers are now a significant discovery channel, and they work differently. The skills include optimization for llms.txt, AI crawler configuration, content chunking for extraction, and entity optimization.

Memory-safe patterns for scale. Most pSEO tutorials work fine for 100 pages but break at 10,000. The skills use a two-tier data model that separates lightweight index data (safe to hold in memory) from heavy page content (loaded one at a time). This prevents out-of-memory crashes during builds.

Quality gates, not just quality checks. Instead of generating 50,000 pages and then finding out 40,000 are thin, the skills include data sufficiency scoring that gates page generation before it happens. Pages without enough data never get built.

Scalable similarity detection. Checking for duplicate content across thousands of pages using pairwise comparison is O(n²) and impractical. The skills use MinHash/LSH and SimHash for near-linear comparison at scale.

How to use them

The skills work with any AI coding assistant that supports custom instructions:

Claude Code: Copy to .claude/skills/

Cursor: Copy to .cursor/rules/ as .mdc files

GitHub Copilot: Concatenate into .github/copilot-instructions.md

Windsurf: Copy to .windsurf/rules/

Then ask the assistant to run the pipeline: "Run pseo-orchestrate with full pipeline" or run individual skills as needed.

The pipeline

The skills follow a dependency order:

Discovery → Audit → Data → Templates + Linking → Metadata + Schema → Performance + LLM Visibility → Quality Guard

Discovery figures out what to build. Audit checks if the codebase is ready. Data builds the foundation everything else depends on. Templates and linking can run in parallel. Metadata and schema can run in parallel. Performance and LLM visibility can run in parallel. Quality guard validates the whole thing.

For projects targeting 10K+ pages, pseo-scale runs after audit to set up database infrastructure before the data layer is built.

What the skills don't do

These skills don't write your content or create your data. They assume you already have (or can source) structured data worth building pages from. If you don't have that, the discovery skill will tell you.

They also don't deploy your site, set up CI/CD, or configure hosting. They focus on the SEO and code architecture.

Open source

The full skill set is available at github.com/lisbeth718/pseo-skills under the MIT license. Use it, adapt it, or build on it.

If you've implemented pSEO before, you'll recognize the patterns. If you haven't, the skills encode the hard-won lessons so you don't have to learn them the painful way.