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Knowledge gaps queue

An editorial backlog driven by real ticket and search demand.

A two-table signal model (canonical entity plus polymorphic signals) lets manual flags, ticket clusters, and failed searches all land in one queue. Resolving a gap cascades a "resolves" link to every ticket attached to its signals. Phase 2a–2c is shipping today.

7 total 6 shipping 1 planned

  • Two-table signal model

    Shipping

    knowledge_gaps is the canonical entity (open → drafting → resolved → dismissed). knowledge_gap_signals carries raw evidence with a polymorphic source key and signal-type-specific JSON payload.

  • Manual flagging

    Shipping

    Agents flag a ticket from the sidebar + menu. Flagged tickets show an amber "Flagged for documentation" pill linking to the queue. The action is idempotent: re-flagging an open ticket attaches a fresh signal to the existing gap.

  • Cluster detection

    Shipping

    Auto-detector groups closed-without-resolves-link tickets by category, device model, and channel. Confidence scales with cluster size; payload carries ticket IDs, sample titles, and facets.

  • Failed-search detection

    Shipping

    Doc-scoped searches log to search_query_log with no per-user attribution. The detector aggregates zero-result queries with two or more recurrences.

  • Editorial workflow

    Shipping

    Two-pane responsive layout at /documentation/gaps. One-click resolve cascades "resolves" links to every signal-attached ticket; dismiss for non-actionable gaps.

  • Coverage metric

    Shipping

    impact_score is a count of unique tickets the gap covers plus search occurrences. Concrete demand, not an abstract weighted score. The badge label adapts to whichever signal dominates ("5 tickets" vs "12 searches").

  • AI gap detection

    Planned

    Phase 3 of the docs/KB redesign. The schema is already set up so suggested_title, suggested_outline, and suggested_content can be added without a breaking migration.

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