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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@@ -53,6 +53,7 @@ class AdvisorMeasure(BaseModel):
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class AdvisorResult(BaseModel):
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inferred_assumptions: List[InferredAssumption] = Field(default_factory=list)
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rejected_assumptions: List[RejectedAssumption] = Field(default_factory=list)
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auto_detected: List[str] = Field(default_factory=list) # Silent Pass: recognised w/o asking
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next_best_questions: List[AdvisorQuestion] = Field(default_factory=list) # max 5
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capability_delta: List[str] = Field(default_factory=list)
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top_measures: List[AdvisorMeasure] = Field(default_factory=list)
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