> Programmatic page generation at scale using template-based SEO and data 100-100K+ pages. Use when building SEO pages at scale or scoping a programmatic SEO build.
14k tokens
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the whole folder, loaded on every use
8
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ships runnable scripts
0
copies elsewhere
how many repositories repackaged it
447
stars on the repo
on the repository, not the skill itself
Install
one command, takes just this skill from the repository
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.
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:
[ ] Keyword pattern — the repeating [variable] structure with 50+ variations (drives keyword mining and template variables)
[ ] Structured data source — the dataset that populates pages and its quality tier (drives the data pipeline and the 3-of-5 uniqueness rule; thin-content risk)
[ ] Search intent — whether intent is consistent across all variations (drives playbook selection and template architecture)
[ ] Domain authority & scale — your DR vs competitors and target page count (drives the build-vs-skip decision and indexation strategy)
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
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.
How to use it
Copy the folder
Take borghei/programmatic-seo from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
Check the name does not clash
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.