docs-gap-finderlisted

# docs-gap-finder — Content Gap Analysis Surfaces which documentation pages should be created next, based on real user signals (search misses, unanswered AI-chat questions, top external queries) instead of guesswork. Cross-references these signals against the live doc graph so already-covered topics are filtered out, then returns a prioritized list of the **top 7 pages to create**. ## Workflow 1. **Collect demand signals** — gather what users searched for but didn't find, questions the AI/support couldn't answer, and the most popular queries. If a docs analytics source is connected (e.g. a Docsbook workspace, PRO+), pull these directly; otherwise work from whatever search-log / support-ticket / question data the user can provide. Retain normalized text, frequency, and source signal type for each result. 2. **Gather the docs** — get the list of pages in scope and read their titles/headings. If a semantic/graph search tool over the markdown is available (self-hosted `markdown-lsp`, or a connected Docsbook workspace), prefer it — faster and cheaper than scanning files; otherwise read the files directly with `grep`/`find`. 3. **Cluster and score** — group near-duplicate queries into topic clusters. Compute priority score: `(failed_search × 3) + (ai_unanswered × 3) + (popular_search × 1)`. Failed searches and unanswered AI questions outweigh popularity — they confirm a gap. 4. **Cross-reference doc graph** — drop clusters already covered by an existing page (title/H1/H2 token o