name: programmatic-seo
description: >
Programmatic page generation at scale using template-based SEO and data
pipelines: keyword pattern mining, template architecture, and indexation for
100-100K+ pages. Use when building SEO pages at scale or scoping a programmatic
SEO build.
license: MIT + Commons Clause
metadata:
version: 1.1.0
author: borghei
category: marketing-growth
updated: 2026-06-15
tags: [seo, programmatic, templates, content-at-scale, data-driven-seo]
Programmatic SEO
Production-grade framework for building SEO page sets at scale. Covers the full lifecycle from keyword pattern discovery through template design, data pipeline construction, quality assurance, and post-launch optimization. Designed for deployments ranging from 50 to 100,000+ pages.
Core Capabilities
- Opportunity assessment & playbook selection — validate demand, rate data sources (Tier S-F), score the competitive moat, then pick from 14 page-set playbooks via the selection matrix and the weighted build-vs-skip decision matrix.
- Keyword pattern mining — extract repeating
[variable] structures, map head/torso/long-tail/zero-volume distribution, and classify search intent.
- Data pipeline design — source → extraction → transformation → enrichment → validation → publication, with per-record quality gates and per-data-type update cadence.
- Template architecture & quality control — 6-zone page structure, the 3-of-5 uniqueness rule, URL conventions, pre-publication QA, thin-content detection, and hub-and-spoke internal linking.
- Indexation & optimization — crawl-budget strategy, tiered indexation priority, IndexNow, phased launch sequence, post-launch metrics dashboard, and anti-pattern / penalty avoidance.
When to Use
Use this skill when:
- You have a repeating keyword pattern with 50+ variations
- You have (or can acquire) structured data to populate pages
- The search intent is consistent across variations
- Your domain has sufficient authority to compete
Do NOT use when:
- Each page requires unique editorial content (use content-creator instead)
- Total addressable pages < 30 (manual content is more effective)
- You lack a data source and would be generating thin placeholder content
- Your domain authority is below DR 20 and competitors are DR 60+
Clarify First
Before scoping the build, confirm these inputs. If any is unknown or vague, ASK — do not assume:
Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.
Quick Start
# Analyze keyword patterns for pSEO opportunities
python scripts/keyword_pattern_miner.py --keywords keywords.csv --json
# Score page templates for content quality and uniqueness
python scripts/template_scorer.py --template template.html --data sample_data.json
# Validate data quality for pSEO data pipeline
python scripts/data_validator.py --file data.csv --rules rules.json --json
References
Load the reference that matches the phase you are in — keep this file lean and pull detail on demand:
- references/strategy-and-playbooks.md — initial assessment (opportunity validation, data-source tiers, competitive moat), the 14 playbooks, playbook selection matrix, and the build-vs-skip decision matrix. Read when scoping an opportunity and choosing what to build.
- references/keyword-and-data.md — keyword pattern identification, volume distribution analysis, intent classification, and the full data pipeline (quality gates, update cadence). Read when mining keywords or designing the data feed.
- references/templates-and-quality.md — 6-zone page architecture, uniqueness requirements, URL structure, pre-publication QA checklist, thin-content detection, hub-and-spoke linking, and anti-patterns. Read when designing templates and QA gates.
- references/launch-and-optimization.md — crawl-budget management, indexation priority, IndexNow, phased launch sequence, post-launch metrics dashboard, troubleshooting table, output artifacts, and success criteria. Read when launching and monitoring the page set.
Scope & Limitations
In scope:
- Keyword pattern mining and volume distribution analysis
- Data pipeline design (source > extraction > transformation > validation > publication)
- Template architecture with uniqueness requirements
- Quality control frameworks including thin content detection
- Hub-and-spoke internal linking for pSEO page sets
- Phased indexation strategy and crawl budget management
- Post-launch optimization and monitoring dashboards
Out of scope:
- Individual editorial content creation (use Content Production)
- Data collection or web scraping implementation
- CMS or static site generator setup and configuration
- Server infrastructure for large-scale deployments
- Paid acquisition for pSEO pages
- Legal compliance for data usage rights
Known limitations:
- Google's 2026 helpful content system can deindex large page sets retroactively if quality drops below threshold
- Programmatic SEO at Tier F data (public/scraped) carries high penalty risk regardless of template quality
- Engagement metrics (bounce rate, time on page) now influence indexation decisions for pSEO pages
- AI content detection is improving — fully automated content generation without human oversight is increasingly risky
- Travel site case study: 50,000 city-swap pages had 98% deindexed within 3 months (per 2025 industry data)
Related Skills
- seo-audit -- Run after pSEO pages are live to diagnose indexation issues, thin content warnings, or ranking problems across the page set.
- schema-markup -- Add structured data to pSEO templates (Product, FAQ, LocalBusiness) for rich snippet eligibility at scale.
- site-architecture -- Plan hub-and-spoke structure and crawl budget management for large pSEO deployments (500+ pages).
- competitor-alternatives -- Use the Comparisons playbook when building "[X] vs [Y]" pages; competitor-alternatives has dedicated comparison page frameworks.
- content-creator -- Use when individual pages in the set need editorial-quality unique content beyond template generation.