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. **Pull signals** — call `get_failed_searches`, `get_ai_unanswered`, and `get_popular_searches` in parallel for the target workspace and period. Retain normalized text, frequency, and source signal type for each result. 2. **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. 3. **Cross-reference doc graph** — call `get_doc_graph` and drop clusters already covered by an existing page (title/H1/H2 token overlap ≥ 0.6 with non-stub content). Mark partial matches as `expand_existing`. 4. **Produce report** — sort surviving clusters by score, take top `limit` (default 7), emit a markdown report with draft outlines per gap. 5. **Optionally open GitHub Issues** — if `open_issues: true`, create one issue per gap in the source repo with the draft outline and signal data. ## Guardrails - Requires PRO+ plan — `get_failed_searches` and `get_ai_unanswered` are PRO+ features. Exit early with a plan