Template-based page generation at scale for SEO. Data-driven content, internal linking architecture, and automated optimization for large-scale content operations. Use when the user asks about programmatic SEO, pSEO, scaled content, template pages, or automated page generation.
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Generate thousands of targeted pages from data templates at scale.
git clone https://github.com/thatrebeccarae/claude-marketing.git && cp -r claude-marketing/skills/programmatic-seo ~/.claude/skills/
Creating large numbers of search-optimized pages from structured data + templates. Instead of writing each page manually, you define a template and populate it with data variations.
Examples of successful pSEO:
| Pattern | Template | Data Source | Pages |
|---|---|---|---|
| Integration pages | "[Your product] + [Partner]" | Partner/integration list | 100-5,000 |
| Location pages | "[Service] in [City/State]" | Location database | 500-50,000 |
| Comparison pages | "[Your product] vs [Competitor]" | Competitor list | 20-200 |
| Glossary/definitions | "What is [Term]?" | Industry term database | 200-2,000 |
| Template/example pages | "[Type] template" or "[Type] examples" | Template library | 100-1,000 |
| Tool pages | "[Metric] calculator" | Calculator variations | 50-500 |
| Stats pages | "[Topic] statistics [Year]" | Data sources | 50-500 |
[H1: Dynamic title with primary keyword]
[Intro paragraph: 2-3 sentences with entity-specific context]
[Key data section: structured data unique to this entity]
[Comparison or context section: how this entity relates to others]
[FAQ section: 3-5 entity-specific questions]
[Related pages: internal links to related entities]
[CTA: conversion action relevant to search intent]
Every programmatic page MUST have:
Hub page: "[Category] Guide" (high authority)
└── Spoke 1: "[Entity A] in [Category]"
└── Spoke 2: "[Entity B] in [Category]"
└── Spoke 3: "[Entity C] in [Category]"
(Each spoke links back to hub and to 3-5 related spokes)
| Approach | Best For | Stack |
|---|---|---|
| Static Site Generation (SSG) | <10,000 pages, infrequent data updates | Next.js, Astro, Hugo |
| Incremental Static Regeneration (ISR) | 10,000-100,000 pages, periodic updates | Next.js |
| Server-Side Rendering (SSR) | Dynamic data, real-time pricing | Next.js, Nuxt |
| Database-driven CMS | Non-technical team needs to manage | WordPress + custom post types |
noindex on thin pages until they have sufficient contentまだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
A/B and multivariate testing methodology. Design experiments, calculate sample sizes, determine statistical significance, avoid common pitfalls, and interpret results. Platform-agnostic framework applicable to landing pages, emails, ads, pricing, and product features. Use when the user asks about A/B testing, split testing, experiment design, statistical significance, or conversion experiments.
日本語の概要は準備中です。原文の説明を表示しています。
Google and Meta paid media account structure evaluation. Audits campaign/ad set architecture against conversion volume minimums, budget thresholds, and targeting overlap. Identifies over-segmentation, under-segmentation, budget fragmentation, and structural anti-patterns blocking algorithmic learning. Provides consolidation roadmaps with migration plans. Use when inheriting accounts, quarterly health checks, or before scaling budgets.
日本語の概要は準備中です。原文の説明を表示しています。
Answer Engine Optimization (AEO) and Generative Engine Optimization (GEO) specialist. Optimize content and websites to appear in AI-generated answers from ChatGPT, Perplexity, Claude, Google AI Overviews, and other LLM-powered search experiences. Use when the user asks about AI search optimization, AEO, GEO, AI Overviews, appearing in AI answers, LLM citations, or optimizing for generative search.
日本語の概要は準備中です。原文の説明を表示しています。
Extract brand identity from a website URL — voice, colors, typography, imagery, values, and target audience — into a structured brand-profile.json. The profile feeds downstream skills (seo-content-writer, email-composer, frontend-design, pro-deck-builder, cross-platform-audit) for brand-consistent output. Use when the user says brand DNA, brand profile, extract brand, analyze brand, brand voice, brand identity, brand colors, or brand style guide.
日本語の概要は準備中です。原文の説明を表示しています。
Develop brand voice, tone matrices, messaging frameworks, and brand book documentation. Use when the user asks about brand voice, tone of voice, brand guidelines, messaging framework, or brand consistency.
日本語の概要は準備中です。原文の説明を表示しています。
Braze customer engagement platform expertise. Audit campaigns, Canvases, segments, messaging channels, and data architecture. Use when the user asks about Braze, customer engagement, push notifications, in-app messaging, cross-channel orchestration, or lifecycle marketing at scale.
日本語の概要は準備中です。原文の説明を表示しています。