feat: Silent Knowledge Pass — recognise before asking (Phase 0, before the endpoint)
Not the endpoint yet — the bigger knowledge lever first. The Advisor can say "I need 5 answers" but does not yet decide what it can find out by ITSELF. The Silent Knowledge Pass runs in front of the Advisor and, from signals existing scanners/parsers already produce (website, repository, documents, product data), deterministically derives capabilities the company demonstrably HAS + product facts that drive scope — so every recognised item shrinks the delta and removes a question. compliance/onboarding/silent_intake.py: silent_intake(signals, signal_map) -> detected_capabilities (+ evidence already in hand) + product_facts. The signal->conclusion map is curated DATA (knowledge/onboarding/intake_signal_map.yaml), signals are injected (scanners are upstream). Pure, deterministic, no LLM. advisor_start gains detected_capabilities (folded into the profile at HIGH confidence -> covered, not asked) and an auto_detected result + headline. The experience flips from a question wall to "we already recognised 4 capabilities, 2 product facts and have 4 pieces of evidence in hand — only these few remain". Order now: Silent Pass -> #58 endpoint/frontend -> #59 empirical loop. NOT new architecture, just an orchestration step in front. Non-runtime (no app caller) -> no deploy. 15 onboarding tests pass, mypy --strict clean, check-loc 0.
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@@ -21,6 +21,14 @@ from .observations import (
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empirical_distribution,
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reviewed,
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)
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from .silent_intake import (
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DetectedCapability,
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IntakeSignal,
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ProductFact,
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SignalMapping,
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SilentIntakeResult,
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silent_intake,
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)
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from .schemas import (
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AdvisorMeasure,
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AdvisorQuestion,
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@@ -47,4 +55,10 @@ __all__ = [
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"empirical_distribution",
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"empirical_confidence",
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"reviewed",
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"silent_intake",
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"IntakeSignal",
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"SignalMapping",
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"DetectedCapability",
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"ProductFact",
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"SilentIntakeResult",
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]
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