# SEO Content

> Audit content quality and E-E-A-T signals — word count, heading hierarchy, readability (Flesch), keyword density, author/date/source signals, and GEO (LLM/AI-search) citability. Use when the user asks to improve content, rank for a keyword, make content cite-able by AI, or strengthen E-E-A-T.

- Skill: `freeautomation-tech/seo-content` (Agent Skill)
- Install (CLI): `npx skillmds@latest add freeautomation-tech/seo-content`
- Raw SKILL.md: https://api.skillmd.com/api/skills/freeautomation-tech/seo-content/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Marketing & Growth
- Author: FreeAutomation-Tech (https://skillmd.com/u/freeautomation-tech)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/freeautomation-tech/seo-content

---


# 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 citation phrases
- Numeric / data density
- Entity signals (author, dates, schema)
- Word-count floor for LLM citation

## Notes

- Inspired by [AgriciDaniel/claude-seo](https://github.com/AgriciDaniel/claude-seo)
  and [seranking/seo-skills](https://github.com/seranking/seo-skills) (both MIT).