Benjamin Admin
c2c8f7e424
Not scanner stubs — the scanners exist. The Silent Pass needs only their UNIFIED output. This adds the
small common DATA FORMAT (not a new module/framework) the user asked for, exactly the Requirement-
Source / MCAP / regulation-alias pattern: many inputs, one language.
Producer A / B / C -> normalize_signals (vocabulary: id + aliases) -> canonical IntakeSignal -> Silent Pass
- ProducedSignal {signal_id, source_type, confidence, evidence, provenance} = what ANY source emits
(website scanner, repo scanner, PDF parser, tender parser, API, the user).
- knowledge/onboarding/signal_vocabulary.yaml reduces producer dialects to a canonical signal: "SBOM
present" arrives as cyclonedx_found / spdx_found / sbom_uploaded / requires_sbom (tender) — all become
`sbom_file_found`. The Silent Pass cannot tell where it came from -> no per-scanner special logic, ever.
- Unknown signals pass through (a new producer stays visible). confidence/evidence/provenance flow to
the detected capability for the audit trail.
A tender that "requires SBOM" now produces the same effect as a repo that HAS one — fits Vision V2
(Requirement Source over Regulation). Endpoint (#58) then has its final shape: POST -> Producers ->
Normalizer -> Silent Pass -> Profile -> Delta -> Questions -> Roadmap. Non-runtime -> no deploy. mypy
--strict clean, 14 onboarding tests pass, check-loc 0.
73 lines
1.8 KiB
Python
73 lines
1.8 KiB
Python
"""Smart Onboarding Advisor — the onboarding runtime step (orchestration over existing engines).
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Turns (company + products + certifications + target) into inferred assumptions, the next best questions
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(<=5, each self-explaining), the capability delta, top measures, evidence requests and completeness —
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with NO sales interpretation and NO regulation picking. Orchestrator only: no new engine/registry/
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meta-model; certificate->capability hypotheses and target requirements are INJECTED.
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"""
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from __future__ import annotations
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from .engine import advisor_start, apply_answer
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from .hypotheses import (
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CapabilityHypothesis,
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inferred_hypotheses,
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resolve_for_certifications,
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)
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from .observations import (
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Observation,
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ObservationType,
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empirical_confidence,
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empirical_distribution,
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reviewed,
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)
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from .signals import (
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ProducedSignal,
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SignalVocabularyEntry,
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normalize_signals,
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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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AdvisorResult,
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InferredAssumption,
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OnboardingInput,
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RejectedAssumption,
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)
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__all__ = [
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"advisor_start",
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"apply_answer",
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"OnboardingInput",
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"AdvisorResult",
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"AdvisorQuestion",
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"AdvisorMeasure",
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"InferredAssumption",
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"RejectedAssumption",
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"CapabilityHypothesis",
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"inferred_hypotheses",
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"resolve_for_certifications",
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"Observation",
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"ObservationType",
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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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"ProducedSignal",
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"SignalVocabularyEntry",
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"normalize_signals",
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]
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