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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