What problem does it solve? Local business pages often rank in traditional search but are never cited inside AI answers from Google AI Overviews, ChatGPT, Perplexity, or Gemini. This Skill rewrites a page draft against the evidenced, page-controllable levers that lift AI-answer citation, then verifies the result with a linter. ## Core Features & Use Cases - Direct-answer-first restructuring: Converts essay-style sections into self-contained passage blocks whose opening sentences answer the buyer question outright, making them extractable by AI engines. - Evidence injection: Adds sourced statistics and real operator quotes pulled from the client's brand.yaml and SME interview notes, never fabricated facts. - Automated verification: Runs scripts/geo_page_linter.py before and after editing, plus readability_scorer.py and keyword_density.py, to confirm the draft passes. - Use Case: A plumber's service page ranks on page one but never appears in AI Overviews. Run this Skill on the draft to restructure its H2s, inject real pricing and response-time data, add an operator quote, and receive a GEO note listing the off-page entity gaps (GBP, NAP, reviews) still blocking citation. ## Quick Start Optimize my draft page.md for AI-answer citation using the geo-optimize levers and verify it with the linter.
