seo-contentlisted

# Content & GEO Analyze content quality, E-E-A-T signals, and Generative Engine Optimization (GEO) readiness — how likely an LLM answer engine (AI Overview, ChatGPT, Perplexity, Gemini) is to cite the page. ## Procedure 1. **Fetch and parse the page**, then run the content audit: ```python from seo_kit.crawler.page_fetcher import fetch_page, parse_html from seo_kit.content.analyzer import run_content_audit _, html, _, _ = fetch_page("<url>") page = parse_html(html, "<url>") result = run_content_audit(page) print(result.to_dict()) ``` 2. **Run the GEO / AI-search readiness check:** ```bash python -m seo_kit.content.geo "<url>" ``` or from Python: ```python from seo_kit.content.geo import run_geo_check print(run_geo_check(page).to_dict()) ``` 3. **Summarize and propose rewrites:** - Weak heading hierarchy → outline new H2/H3 sections. - Thin content → expand with data-backed subsections. - Low readability → shorten sentences. - Weak E-E-A-T → add author byline, publish date, sources. - Low GEO score → add Q&A framing, statistics, and citations. ## What it checks (content) - Word count and depth - Heading hierarchy (skipped levels, first-heading-is-H1) - Flesch Reading Ease - Keyword density / stuffing detection - E-E-A-T signals (author, dates, canonical, structured data) - Long-word ratio ## What it checks (GEO) - Question-framed statements (LLM engines love Q&A structure) - Verifiable claims and cita