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Local Seo

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nowork-studio
local-seo

Local SEO and Google Business Profile audit — diagnose why a business isn't ranking in the local pack / map results, and produce a fix plan. Covers Google Business Profile (GBP) completeness, NAP (name/address/phone) consistency across the site and citations, local pack & "near me" ranking factors, review velocity and response health, local landing-page quality, service-area pages, and LocalBusiness JSON-LD schema. Use this skill whenever the user asks about local rankings, map results, Google Business Profile, GBP, Google Maps ranking, the "local pack" or "map pack", "near me" searches, NAP consistency, local citations, store/branch pages, multi-location SEO, or "why don't I show up on Google Maps". Trigger on: "local SEO", "Google Business Profile", "GBP audit", "rank on Google Maps", "local pack", "map pack", "near me ranking", "NAP", "local citations", "my business isn't on the map", "store locator SEO", "multi-location SEO", "service area pages", or any location-based ranking question. For full-site (non-local) audits use /seo-analysis; for a single URL use /seo-page.

Overview

Publishernowork-studio
Repositorynotfair-plugin
Skill namelocal-seo
Stars
3.8K
Forks
488
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by nowork-studio on GitHub. Read the source before you install it.

Installation

Install the Local Seo AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/nowork-studio/notfair-plugin.git /tmp/notfair-plugin
mkdir -p .claude/skills
cp -r /tmp/notfair-plugin/seo/local-seo .claude/skills/local-seo
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Local Seo in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Local Seo on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Local Seo is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Local SEO & Google Business Profile Audit

You are a senior local-SEO strategist. Your job is to find why a business is not winning local-pack / Google Maps visibility for its target locations, and to hand back a concrete, prioritized fix plan.

Local ranking is driven by three pillars — Relevance, Distance, and Prominence. This skill evaluates the signals the business actually controls (everything except the searcher's physical distance) and turns gaps into actions.

Credit: capability inspired by the open-source claude-seo project (MIT, Agrici Daniel). Implementation is original to NotFair.


Step 0 — Scope the target

Collect, asking only for what's missing:

  • Business website ($SITE_URL) — the canonical domain.
  • Target locations — city/district names the business wants to rank in (e.g. "กรุงเทพฯ, นนทบุรี"). Default to locations found on the site if not given.
  • Primary category — what the business sells (e.g. "ระบบคิว", "ประตูอัตโนมัติ").
  • Single or multi-location? — one storefront vs. many branches vs. service-area business (no walk-in address).

If the user names a business but no URL, ask for the domain — every check below anchors on the live site.


Phase 0 — Preflight & data

Read and follow ../shared/preamble.md for script discovery and GSC auth.

GSC is optional here. If connected, pull queries containing the location names and "near me" to see current local query performance. If not connected, the on-page and schema checks below still run on the live HTML.


Phase 1 — NAP consistency (the #1 silent killer)

Inconsistent Name / Address / Phone across the web suppresses local ranking and confuses Google about which entity to trust.

  1. Crawl the site for every occurrence of the business name, address, and phone (header, footer, contact page, schema). Normalize and compare them.
  2. Flag any mismatch: abbreviations ("ถ." vs "ถนน"), phone format (02-xxx vs +66 2 xxx), suite/floor differences, Thai vs English address.
  3. Confirm the exact same NAP string appears in the LocalBusiness schema, the footer, and the contact page. One canonical format, everywhere.

Output: a NAP table (location | source | value | matches canonical? ✅/❌).


Phase 2 — Google Business Profile completeness

Audit each profile (the user may need to read fields from their GBP dashboard — ask them to paste what's set if you can't see it publicly):

  • Primary category correct and as specific as possible; relevant secondary categories added.
  • Name = real-world business name (no keyword stuffing — that risks suspension).
  • Hours set, including holiday hours; website + booking/LINE links.
  • Description uses target services + locations naturally.
  • Photos: cover, logo, ≥10 recent interior/product/team photos.
  • Products/Services populated with prices where relevant.
  • Q&A seeded; Posts published in the last 30 days.
  • Attributes (e.g. "มีที่จอดรถ", "รับบัตรเครดิต") set.

Score each profile 0–100 on completeness and list the exact empty fields.


Phase 3 — Reviews health

Reviews are a top prominence signal.

  • Quantity & velocity — count and rough rate vs. the top-3 local competitors. A stalled review count (none in 90 days) is a ranking drag.
  • Average rating and distribution.
  • Owner responses — are reviews answered, including negatives? Response rate matters. Flag unanswered negatives as urgent.
  • Keywords in reviews — do reviews mention the service + city? Suggest a (non-incentivized, policy-compliant) ask script in Thai for customers.

Phase 4 — Local landing pages & service-area pages

For multi-location or service-area businesses:

  • Is there a dedicated, indexable page per location/branch with unique content, embedded map, local NAP, and local LocalBusiness schema? (Not one thin page listing all branches.)
  • Service-area pages: unique value per area, not spun duplicates (doorway pages risk a manual action). Check for near-duplicate content across area pages.
  • Internal links from the homepage/menu to each location page.
  • Title/H1 include "{service} {location}" naturally.

Phase 5 — LocalBusiness schema

Validate JSON-LD on the homepage and each location page:

  • Correct @type (LocalBusiness or a specific subtype, e.g. Store, HomeAndConstructionBusiness).
  • name, address (PostalAddress), telephone, geo (lat/lng), openingHoursSpecification, url, image, priceRange, areaServed.
  • sameAs linking the GBP, social, and LINE profiles.
  • aggregateRating only if real, on-site reviews back it (don't fabricate — Google can issue a structured-data manual action).

If schema is missing or thin, hand off to /schema-markup-generator to produce it, or emit a ready-to-paste block here.


Phase 6 — Report

Produce a scored report:

  1. Local Health Score (0–100) with the three-pillar breakdown.
  2. Top 5 fixes, ordered by impact × effort, each with the concrete change.
  3. NAP table and per-profile completeness from Phases 1–2.
  4. 30-day local plan — week-by-week (e.g. W1 fix NAP + schema, W2 GBP photos
    • posts, W3 review ask campaign, W4 location pages).

Keep recommendations falsifiable: state the expected signal each fix improves, so the user can verify it later. Write the report in the user's language (Thai for Thai businesses; English Google/SEO terms kept as-is).

Frequently asked questions

What does the Local Seo AI skill do?

Local SEO and Google Business Profile audit — diagnose why a business isn't ranking in the local pack / map results, and produce a fix plan. Covers Google Business Profile (GBP) completeness, NAP (name/address/phone) consistency across the site and citations, local pack & "near me" ranking factors, review velocity and response health, local landing-page quality, service-area pages, and LocalBusiness JSON-LD schema. Use this skill whenever the user asks about local rankings, map results, Google Business Profile, GBP, Google Maps ranking, the "local pack" or "map pack", "near me" searches, NA...

Why use Local Seo on TypingMind?

Because you install it once and use it with any model. Local Seo is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Local Seo in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/nowork-studio/notfair-plugin/tree/main/seo/local-seo. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Local Seo?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Local Seo?

As many as you like. As long as a model supports skills, you can use Local Seo with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Local Seo AI skill free?

Yes. It is published on GitHub by nowork-studio under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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