Commit Graph

3 Commits

Author SHA1 Message Date
Benjamin Admin 978052b5a2 fix(onboarding): decouple partial/indicative signals from detected — partial no longer removes a question
Fix B of the pre-#59 semantic correction. The Silent Pass had only TWO effective states though the data
carries three: a `detected` mapping (a concrete artifact) AND a `partial` mapping (an indicative signal,
e.g. a CI pipeline -> secure-development-lifecycle) both flowed through capability_ids() and were fed to
the Advisor as already-present — so a weak indication silently removed a question, exactly the Welt-1/
Welt-2 transparency we want to keep.

Now three distinct states:
  - detected   -> reduces the delta immediately (auto_detected, not asked).   [unchanged]
  - partial    -> raises assumption strength but does NOT replace the question (surfaced as `indications`,
                  the capability stays in the delta and is still asked).
  - requirement-> describes a target, never the present state (already handled by Fix A's kind split).

Changes (data + thin wiring, no new architecture):
  - SilentIntakeResult.capability_ids() returns only relationship==detected; new indicative_capability_ids()
    returns the partial ones.
  - advisor_start() gains indicative_capabilities (NOT fed into the profile) and surfaces result.indications
    = indicative ∩ required − auto_detected.
  - AdvisorResult / AdvisorResponse gain `indications` (additive, contract-safe); the service passes the
    indicative ids through.

Tests: a partial CI signal is indicative-not-detected and does NOT shrink the delta; end-to-end it appears
in `indications`, not `auto_detected`, and the gap is still asked. 28 onboarding tests pass, mypy --strict
clean on the onboarding modules, demo runs, check-loc 0. Runtime effect -> deploy + smoke.
2026-06-28 16:02:35 +02:00
Benjamin Admin 9c33582412 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.
2026-06-28 14:34:27 +02:00
Benjamin Admin 3ba90f49cf feat: Smart Onboarding Advisor — make the knowledge usable in onboarding (ADR-012)
The user-named "right next runtime step": stop building knowledge, start using it automatically in
onboarding — no sales training, no regulation picking. compliance/onboarding/ is an ORCHESTRATOR (not
a new engine) wiring Company 2A -> RS-005 -> optimization -> completeness:

  advisor_start(input, cert_hypotheses, target_requirements, ...) -> AdvisorResult

From (company + products + certifications + target) it returns inferred_assumptions, rejected_
assumptions, next_best_questions (<=5, ranked by information_gain + leverage + unknown_high_risk +
evidence_missing, each self-explaining), capability_delta, top_measures, evidence_requests,
unsupported_domains, completeness_summary. apply_answer() updates the profile (delta shrinks).

Welt-1 throughout: certificates REDUCE questions but satisfy nothing automatically (verification_
required); relevance(evidence,target) keeps ISO 14001 out of the CRA result. Certificate->capability
hypotheses + target requirements are INJECTED (curated knowledge, outsourced; not in code).

All 7 acceptance criteria pass; mypy --strict clean. First app-caller wiring the engines into a
product flow — still no endpoint/persistence, so 0 runtime effect -> no deploy yet (deploys when
POST /onboarding/advisor-start + frontend are wired). check-loc 0.
2026-06-28 12:45:49 +02:00