SEO Geo

GEO / AI-Search Readiness

Evaluate how likely a page is to be cited by LLM answer engines, and give concrete fixes to improve LLM visibility.

Procedure

1. Fetch and parse the page, then run the GEO check:

   python -m seo_kit.content.geo "<url>"
   

   or from Python:

   from seo_kit.crawler.page_fetcher import fetch_page, parse_html
   from seo_kit.content.geo import run_geo_check
   
   _, html, _, _ = fetch_page("<url>")
   page = parse_html(html, "<url>")
   result = run_geo_check(page)
   print(result.to_dict())
   

2. Report the score and findings, then recommend improvements:

   • Question framing — add Q&A sections phrased as natural queries.
   • Verifiable claims — attribute statistics and cite sources.
   • Data density — use specific numbers, percentages, and dates.
   • Entity signals — author bylines, publish dates, schema markup.
   • Depth — expand past the LLM citation floor (~600+ words).

3. Offer to draft the rewrite (e.g., add an FAQ block, rewrite the intro as a direct answer, or insert sourced statistics).

Notes

• Scores 0-100 with findings per signal.
• Inspired by AgriciDaniel/claude-seo and seranking/seo-skills (both MIT).