Adds corporate memory v1 (verification flywheel + contradiction detection + confidence scoring) and v1.5 (audience-based distribution + per-item privacy + admin curation). Server: GET /api/memory/bundle returns mandatory + ranked-approved items within a token budget; POST /api/memory/admin/mandate accepts an audience field gated against user_group_members; /api/memory/stats uses SQL aggregation. CLI: da sync writes received items to .claude/rules/km_*.md. Verification detector extracts knowledge candidates from session JSONL files. Auto-tagging via Haiku when ai: is configured. Adapted from the v9-era branch onto v13/v14 RBAC: _is_privileged_viewer + _effective_groups now query user_group_members JOIN user_groups; require_role(Role.KM_ADMIN) replaced with require_admin (km_admin collapsed into admin). Schema v15: knowledge_items context-engineering columns + knowledge_contradictions + session_extraction_state. Schema v16: verification_evidence. Cuts release v0.15.0 (also bundles #116 /me/debug page).
22 lines
926 B
JSON
22 lines
926 B
JSON
{
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"verifications": [
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{
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"detection_type": "correction",
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"title": "NPS uses rolling 90-day window, not quarterly",
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"content": "NPS is calculated using a rolling 90-day window, not quarterly snapshots.",
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"user_quote": "Not quite. We use a rolling 90-day NPS, not quarterly. The current score should be around 52.",
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"domain": "product",
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"entities": ["NPS"],
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"base_confidence": 0.90
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},
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{
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"detection_type": "unprompted_definition",
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"title": "MAU retention definition",
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"content": "MAU retention = percentage of users active in month N who were also active in month N-1.",
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"user_quote": "Also, FYI, our retention metric is defined as the percentage of users who are active in month N who were also active in month N-1. We call it MAU retention.",
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"domain": "product",
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"entities": ["MAU retention"],
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"base_confidence": 0.90
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}
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]
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}
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