local-business-area-page-enricher

Enforce local content requirements for town service area pages.

Updated Aug 27, 2026
One-click install
npx skills add https://github.com/redbananastudios/ai-library --skill local-business-area-page-enricher
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: local-business-area-page-enricher
Source: https://github.com/redbananastudios/ai-library/tree/main/skills/local-business-seo/skills/local-business-area-page-enricher
Command: npx skills add https://github.com/redbananastudios/ai-library --skill local-business-area-page-enricher

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

It helps writers and auditors produce area/town/city pages that pass a strict local-content recipe, avoiding templated, interchangeable SEO copy that fails helpful-content expectations.

Core Features & Use Cases

  • Local specificity enforcement: Requires concrete local signals such as real road names, outward + full postcodes, distance from base, property types, access/route details, indicative pricing, earned operational detail, and 4–6 town-specific FAQs.
  • Template detection via the “remove location name” test: Ensures the page remains clearly identifiable for the correct town even if the location name is removed.
  • Schema guidance for per-area Service JSON-LD: Instructs how to emit the appropriate service schema per area page (without duplicating LocalBusiness).
  • Audit-ready workflow: Provides an audit and scoring approach so under-scoring pages can be rewritten to meet the 900–1050 word target.

Quick Start

Use local-business-area-page-enricher to rewrite your /areas/<town>/ page so it includes all eight required local elements and passes the remove-location-name test.

Frequently Asked Questions about local-business-area-page-enricher

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I write local SEO content for town pages that avoids templated duplication?▼

To write local SEO content for town pages that avoids templated duplication, you must include real road names, full postcodes, distance-from-base context, property types, access routes, indicative pricing, and 4-6 town-specific FAQs. This ensures the page remains identifiable to the correct town even when the location name is removed.

What is the remove location name test for local business area pages?▼

The remove location name test for local business area pages is a validation method that checks if a page remains clearly attributable to the correct town after the location name is deleted. It ensures your content has enough genuine local signals like specific roads, postcodes, and operational details rather than interchangeable template text.

How do I add Service schema JSON-LD to individual local business area pages?▼

To add Service schema JSON-LD to individual local business area pages, you should emit the appropriate Service schema per area page without duplicating the main LocalBusiness schema. This targets the specific services offered in that town while keeping the core business entity separate.

What specific local elements are required for a helpful town service page?▼

A helpful town service page requires eight specific local elements: real road names, outward and full postcodes, distance from base, property types, route and access details, indicative pricing, earned on-the-ground operational detail, and 4-6 town-specific FAQs to satisfy helpful content expectations.

How many words should a local service area page have for SEO?▼

A local service area page should have between 900 and 1050 words for SEO. This strict word count constraint ensures enough space to include all required local details and pass the remove location name test without generating thin or templated content.

Can I audit existing area pages to check if they pass local SEO content requirements?▼

Yes, you can audit existing area pages to check if they pass local SEO content requirements by using a scoring approach that evaluates the presence of eight local elements and applies the remove location name test. Under-scoring pages can then be rewritten to meet the 900-1050 word target.