feat(completeness): Regulatory Completeness Engine — auditable coverage, not confidence

Phase A½. The move from feature to product development: for every assessment, answer "how sure are
we that this answer is COMPLETE?" — different from confidence. The product never claims full coverage;
it makes its own knowledge state transparent and auditable. Shows what we do NOT know and why.

- compliance/completeness/: assess_completeness(identified, corpus_status, uncertain, assumptions,
  assessed_obligations) -> CompletenessReport. Separates IDENTIFIED from ASSESSED (validated corpus
  AND determined applicability) and justifies every gap. Two kinds of open: corpus gap (future_corpus)
  and applicability uncertainty (query_required + deciding question, e.g. Data Act / generates_usage_data).
- The metric is COUNTS, never a single percentage: "Identifiziert N · bewertet M · offen K ·
  Unsicherheiten U · Begründung ja" + an honest audit statement.
- ADR-007: auditable honesty; phase order A factory -> A½ Completeness -> B new domains; the
  transparency selling point. Deterministic, no LLM; corpus status + obligation count injected.
- reference suite: "Regulatory Completeness" section runs an industrial-dishwasher assessment
  (assessed CRA/MaschinenVO; open EMV/Environmental=future_corpus, Data Act=query_required) and notes
  Environmental flips open->validated automatically once the corpus lands.

11 completeness tests (54 with adjacent modules), mypy --strict clean (15 files), check-loc 0.
Product code with no app caller + ADR/reference = non-runtime -> no deploy (ADR-001). Freeze-safe.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

backend-compliance/compliance/completeness/__init__.py

@@ -0,0 +1,24 @@
+"""Regulatory Completeness — auditable knowledge coverage, not confidence.
+
+An internal quality machine: for an assessment it reports identified vs assessed regulations and
+justifies every open or excluded domain (corpus gap -> future_corpus; applicability uncertain ->
+query_required). The metric is counts, never a single percentage. The product never claims full
+coverage — it makes its own knowledge state transparent and auditable. Deterministic, no LLM, no
+new corpus/meta-model class (freeze v1.0).
+"""
+
+from __future__ import annotations
+
+from .engine import assess_completeness
+from .schemas import (
+    Assumption, CompletenessReport, CorpusStatus, DomainCoverage, Exclusion,
+)
+
+__all__ = [
+    "assess_completeness",
+    "CompletenessReport",
+    "CorpusStatus",
+    "DomainCoverage",
+    "Exclusion",
+    "Assumption",
+]