Analysis updated 2026-05-18
Load the wiki into Claude to draft local SEO deliverables like website copy or review responses.
Track your business or creator profile data alongside competitor research.
Use the wiki's playbooks for Google Business Profile and schema markup optimization.
Grow social-to-subscription conversion using the content creator marketing track.
| cemini23/seo-geo-b-m-wiki | 2arons/llm-cli | adzza/guardium-dns | |
|---|---|---|---|
| Stars | 11 | 11 | 11 |
| Language | Python | Python | Python |
| Setup difficulty | moderate | easy | moderate |
| Complexity | 2/5 | 2/5 | 3/5 |
| Audience | pm founder | developer | general |
Figures from each repo's GitHub metadata at analysis time.
Requires filling in a business intake config file before the wiki content becomes useful.
SEO-GEO-B-M-Wiki is a structured knowledge base for two types of operators: local brick-and-mortar businesses (restaurants, salons, auto shops, gyms, and similar service businesses) who want better visibility in local search results, and subscription-platform content creators who want to grow their audience and convert social followers into paying subscribers. The wiki is not a tool that automates anything on your behalf. It is a collection of curated, cross-linked documentation pages covering topics like local SEO foundations, Google Business Profile optimization, schema markup, GEO/AEO (the practice of being cited correctly by AI search engines), and social media playbooks. You read the pages, load them into an AI assistant like Claude, and use the AI to help you produce real deliverables, website copy, review responses, social captions, content calendars, and similar outputs. The repository uses a barbershop as the running example for the local business track and an image-based creator as the example for the content creator track, but the principles apply broadly. Setup involves cloning the repository, filling in a configuration file with your business details, such as name, location, social handles, competitor URLs, and goals, and optionally using an Obsidian vault for navigating the wiki locally. A lint script checks that the wiki structure is healthy. The wiki is organized into three layers: raw source material you feed in, such as articles, PDFs, and screenshots, structured wiki pages the AI helps you write and maintain, and a schema file that tells the AI how the wiki is organized. The full README is longer than what was shown.
A curated knowledge base you load into an AI assistant to help local businesses and content creators grow their visibility and subscribers.
Mainly Python. The stack also includes Python, Markdown, Obsidian.
Setup difficulty is rated moderate, with roughly 30min to a first successful run.
Mainly pm founder.
This repo across BitVibe Labs
Verify against the repo before relying on details.