diff --git a/backend-compliance/reference_scenarios/mcap_convergence_analysis.md b/backend-compliance/reference_scenarios/mcap_convergence_analysis.md
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+# Cross-Domain MCAP Convergence Analysis — wo konvergiert das Wissensmodell?
+
+_Nicht „welche MCAPs kommen am häufigsten vor?" (Häufigkeit täuscht), sondern „welche MCAPs TRAGEN den größten Teil des Systems?". Deterministischer **Impact-Score** (kein ML), internes Engineering-Werkzeug, reine Aggregation über vorhandene Daten (5 Transition Patterns + 7 Automotive-Quellen). Non-runtime, keine echten Namen._
+
+## Impact-Score (deterministisch)
+> `Impact = distinct Sources + distinct Target-Types + distinct Domains + distinct Journeys + Regulatory Leverage + Business Leverage`
+- 62 distinct Capabilities (MCAP-Kandidaten) über alle Quellen aggregiert.
+
+## 1. Core MCAPs — höchster Impact (die tragenden Knoten)
+| Capability | Impact | Sources | Types | Domains | Journeys |
+|---|---:|---:|---:|---:|---:|
+| `secure_signed_update_distribution` | **18** | 5 | 2 | 2 | 4 |
+| `technical_vulnerability_management` | **17** | 5 | 3 | 2 | 4 |
+| `access_control_and_authentication` | **15** | 4 | 2 | 2 | 5 |
+| `incident_management` | **14** | 4 | 2 | 2 | 4 |
+| `product_cyber_risk_assessment` | **13** | 3 | 1 | 2 | 4 |
+| `secure_development_lifecycle` | **11** | 2 | 2 | 2 | 4 |
+| `supplier_security` | **11** | 3 | 2 | 2 | 3 |
+| `ce_conformity_assessment_and_technical_documentation` | **9** | 2 | 1 | 1 | 3 |
+| `coordinated_vulnerability_disclosure` | **9** | 1 | 1 | 2 | 4 |
+| `sbom_creation` | **9** | 1 | 1 | 2 | 4 |
+
+→ Hoher Impact = ein Knoten verbindet viele Quellen ÜBER Typen/Domänen/Journeys hinweg — nicht „in 40 Dokumenten einer Normenfamilie".
+
+## 2. Emerging MCAPs — verbinden ≥2 Domänen (Brücken zwischen Anforderungswelten)
+- `secure_signed_update_distribution` — 2 Domänen (automotive, industrial_automation), 2 Typen.
+- `technical_vulnerability_management` — 2 Domänen (automotive, industrial_automation), 3 Typen.
+- `access_control_and_authentication` — 2 Domänen (automotive, industrial_automation), 2 Typen.
+- `incident_management` — 2 Domänen (automotive, industrial_automation), 2 Typen.
+- `product_cyber_risk_assessment` — 2 Domänen (automotive, industrial_automation), 1 Typen.
+- `secure_development_lifecycle` — 2 Domänen (automotive, industrial_automation), 2 Typen.
+- `supplier_security` — 2 Domänen (automotive, industrial_automation), 2 Typen.
+- `coordinated_vulnerability_disclosure` — 2 Domänen (automotive, industrial_automation), 1 Typen.
+- _(Echtes „Wachstum über Zeit" braucht historische Snapshots — hier Proxy = Domänen-Spannweite jetzt.)_
+
+## 3. Isolated MCAPs — nur 1 Quelle/Journey (Review: spezialisiert ODER Konvergenz übersehen?)
+- 36 Stück, u. a.: `account_energy_consumption`, `cybersecurity_management_system`, `document_update_campaigns`, `document_waste_streams`, `issue_battery_passport`, `machine_safety_risk_assessment`, `measure_air_emissions`, `mechanical_safety_and_guards`.
+
+## 4. Suspicious MCAPs — Abstraktionsgrad-Verdacht (Experten-Review)
+- **Evtl. zu grob** (generisches Verb, breit aber nur 1 Typ): `document_and_change_control`, `manage_chemical_substances`.
+- **Evtl. zu fein** (isoliert + sehr spezifischer Name): `operating_instructions_and_safety_information`, `provide_dedicated_security_contact`, `provide_functional_safety_evidence`, `restrict_hazardous_substances_rohs`, `secure_by_default_no_default_credentials`, `threat_analysis_and_risk_assessment`.
+- Die Analyse sagt damit nicht nur WELCHE MCAPs wichtig sind, sondern auch, ob sie auf dem **richtigen Abstraktionsniveau** definiert sind.
+
+## Befund
+
+> **Ein Kern beginnt sich zu zeigen:** 11 von 62 Capabilities erreichen Impact ≥ 8 (tragende Knoten), 14 verbinden ≥2 Domänen. Bislang ist das Wissensmodell noch jung (5 Patterns + 1 Automotive-Profil), aber die Methode steht: sobald Medical/Payment/weitere Domänen als DATEN hinzukommen, zeigt dieselbe Aggregation, ob sich der erwartete stabile Kern von 30–50 hochkonvergenten MCAPs bildet — der gemeinsame Strukturkern hinter sehr unterschiedlichen Anforderungswelten. Das ist ein tieferer Wertnachweis als „eine weitere Norm unterstützt". Reine Aggregation, 0 Runtime, 0 neue Architektur.
+
diff --git a/backend-compliance/reference_scenarios/mcap_convergence_analysis.py b/backend-compliance/reference_scenarios/mcap_convergence_analysis.py
new file mode 100644
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+++ b/backend-compliance/reference_scenarios/mcap_convergence_analysis.py
@@ -0,0 +1,146 @@
+# ruff: noqa
+# mypy: ignore-errors
+"""Cross-Domain MCAP Convergence Analysis — where does the knowledge model converge? (Phase Ω, pause)
+
+After Automotive the user paused on adding domains to ask a deeper question. NOT "which MCAPs occur most
+often?" (frequency deceives — a generic `document_changes` may be in 40 sources but is not the product
+core) but "which MCAPs CARRY the largest part of the system?". The answer is a deterministic MCAP IMPACT
+SCORE (no AI), an internal engineering tool, computed by aggregating over the EXISTING data only.
+
+  Impact(MCAP) = distinct Requirement Sources + distinct Target Types + distinct Domains
+               + distinct Journeys + Regulatory Leverage + Business Leverage
+
+Four reports, all pure aggregation (no new runtime, no new architecture):
+  1. Core      — highest impact (the cross-cutting nodes that carry the system)
+  2. Emerging  — span >= 2 domains (bridges across requirement worlds)
+  3. Isolated  — only one source/journey/domain (specialised, OR convergence not yet recognised)
+  4. Suspicious— probably cut too coarse (generic) or too fine (one hyper-specific occurrence)
+
+Non-runtime -> no deploy.
+Run:  cd backend-compliance && PYTHONPATH=. python3 reference_scenarios/mcap_convergence_analysis.py
+"""
+from __future__ import annotations
+
+import os
+import yaml
+
+OUT = []
+
+
+def w(s=""):
+    OUT.append(s)
+
+
+_HERE = os.path.dirname(__file__)
+_TP = os.path.join(_HERE, "..", "knowledge", "transition_patterns")
+
+# pattern -> (domain, default target_type, default sources, source_type-of-default)
+PATTERN_META = {
+    "transition_pattern_iso27001_to_cra_maschinenvo_v1.yaml": ("industrial_automation", "regulation", ["CRA", "MaschinenVO"]),
+    "transition_pattern_iso27001_to_cra_v1.yaml":             ("industrial_automation", "regulation", ["CRA"]),
+    "transition_pattern_iso9001_to_cra_v1.yaml":              ("industrial_automation", "regulation", ["CRA"]),
+    "transition_pattern_isms_to_tisax_v1.yaml":               ("automotive", "certification", ["TISAX"]),
+    "transition_pattern_iso14001_to_environmental_v1.yaml":   ("environmental", "regulation",
+        ["REACH", "RoHS", "Batterieverordnung", "Wasserrecht", "Abwasservorschriften", "Energiemanagement", "Kreislaufwirtschaft", "Emissionsschutz"]),
+}
+
+# capability -> dict of sets we aggregate
+idx = {}
+
+
+def _ent(cap):
+    return idx.setdefault(cap, {"sources": set(), "types": set(), "domains": set(), "journeys": set(),
+                                "regs": set(), "markets": set()})
+
+
+def _add(cap, sources, stype, domain, journey):
+    e = _ent(cap)
+    e["sources"] |= set(sources)
+    e["types"].add(stype)
+    e["domains"].add(domain)
+    e["journeys"].add(journey)
+    if stype == "regulation":
+        e["regs"] |= set(sources)
+    if stype == "contract":
+        e["markets"] |= set(sources)
+
+
+# ── A) transition patterns: each pattern is a journey with a target/domain ───────────────────
+# IMPORTANT (anti-frequency-deception): a `likely_covered` cap is PROVIDED BY the source cert (one
+# certification source), NOT required by every target regulation — attributing all target sources to it
+# would inflate management caps on raw frequency alone. Only `delta` caps name their real target sources.
+for fname, (domain, ttype, default_sources) in PATTERN_META.items():
+    p = yaml.safe_load(open(os.path.join(_TP, fname), encoding="utf-8"))
+    journey = p.get("id", fname)
+    cert = (p.get("transition_goal", {}).get("from", {}) or {}).get("standard", journey)
+    for a in p.get("likely_covered", []):
+        _add(a["capability"], [cert], "certification", domain, journey)   # provided by the cert
+    for d in p.get("delta_requirements", []):
+        srcs = d.get("covers_targets") or default_sources                 # required by these target sources
+        _add(d["capability"], srcs, ttype, domain, journey)
+
+# ── B) automotive multi-source data: precise per-source attribution ──────────────────────────
+A = yaml.safe_load(open(os.path.join(_HERE, "..", "knowledge", "domains", "automotive", "source_capabilities.yaml"), encoding="utf-8"))
+for s in A["sources"]:
+    for cap in s["requires"]:
+        _add(cap, [s["id"]], s["type"], "automotive", "automotive_ecu")
+
+# ── Impact score (deterministic) ─────────────────────────────────────────────────────────────
+def impact(e):
+    return len(e["sources"]) + len(e["types"]) + len(e["domains"]) + len(e["journeys"]) + len(e["regs"]) + len(e["markets"])
+
+
+scored = sorted(idx.items(), key=lambda kv: (-impact(kv[1]), kv[0]))
+GENERIC = ("document_", "manage_", "control_", "conduct_", "operate_", "run_", "assign_", "plan_", "approve_")
+
+w("# Cross-Domain MCAP Convergence Analysis — wo konvergiert das Wissensmodell?")
+w("")
+w('_Nicht „welche MCAPs kommen am häufigsten vor?" (Häufigkeit täuscht), sondern „welche MCAPs TRAGEN den größten Teil des Systems?". Deterministischer **Impact-Score** (kein ML), internes Engineering-Werkzeug, reine Aggregation über vorhandene Daten (5 Transition Patterns + 7 Automotive-Quellen). Non-runtime, keine echten Namen._')
+w("")
+w("## Impact-Score (deterministisch)")
+w("> `Impact = distinct Sources + distinct Target-Types + distinct Domains + distinct Journeys + Regulatory Leverage + Business Leverage`")
+w("- %d distinct Capabilities (MCAP-Kandidaten) über alle Quellen aggregiert." % len(idx))
+w("")
+
+# ── 1. Core MCAPs ─────────────────────────────────────────────────────────
+w("## 1. Core MCAPs — höchster Impact (die tragenden Knoten)")
+w("| Capability | Impact | Sources | Types | Domains | Journeys |")
+w("|---|---:|---:|---:|---:|---:|")
+for cap, e in scored[:10]:
+    w("| `%s` | **%d** | %d | %d | %d | %d |" % (cap, impact(e), len(e["sources"]), len(e["types"]), len(e["domains"]), len(e["journeys"])))
+w("")
+w('→ Hoher Impact = ein Knoten verbindet viele Quellen ÜBER Typen/Domänen/Journeys hinweg — nicht „in 40 Dokumenten einer Normenfamilie".')
+w("")
+
+# ── 2. Emerging MCAPs (cross-domain bridges) ──────────────────────────────
+emerging = [(c, e) for c, e in scored if len(e["domains"]) >= 2]
+w("## 2. Emerging MCAPs — verbinden ≥2 Domänen (Brücken zwischen Anforderungswelten)")
+for cap, e in emerging[:8]:
+    w("- `%s` — %d Domänen (%s), %d Typen." % (cap, len(e["domains"]), ", ".join(sorted(e["domains"])), len(e["types"])))
+w('- _(Echtes „Wachstum über Zeit" braucht historische Snapshots — hier Proxy = Domänen-Spannweite jetzt.)_')
+w("")
+
+# ── 3. Isolated MCAPs ─────────────────────────────────────────────────────
+isolated = [(c, e) for c, e in scored if len(e["sources"]) == 1 and len(e["journeys"]) == 1]
+w("## 3. Isolated MCAPs — nur 1 Quelle/Journey (Review: spezialisiert ODER Konvergenz übersehen?)")
+w("- %d Stück, u. a.: %s." % (len(isolated), ", ".join("`%s`" % c for c, _ in isolated[:8])))
+w("")
+
+# ── 4. Suspicious MCAPs (abstraction level) ───────────────────────────────
+too_coarse = [(c, e) for c, e in scored if c.startswith(GENERIC) and len(e["types"]) <= 1 and len(e["sources"]) >= 2]
+too_fine = [(c, e) for c, e in isolated if len(c) >= 34]
+w("## 4. Suspicious MCAPs — Abstraktionsgrad-Verdacht (Experten-Review)")
+w("- **Evtl. zu grob** (generisches Verb, breit aber nur 1 Typ): %s." % (", ".join("`%s`" % c for c, _ in too_coarse[:6]) or "—"))
+w("- **Evtl. zu fein** (isoliert + sehr spezifischer Name): %s." % (", ".join("`%s`" % c for c, _ in too_fine[:6]) or "—"))
+w("- Die Analyse sagt damit nicht nur WELCHE MCAPs wichtig sind, sondern auch, ob sie auf dem **richtigen Abstraktionsniveau** definiert sind.")
+w("")
+
+# ── Befund ─────────────────────────────────────────────────────────────────
+core_cut = [c for c, e in scored if impact(e) >= 8]
+cross = [c for c, e in scored if len(e["domains"]) >= 2]
+w("## Befund")
+w("")
+w('> **Ein Kern beginnt sich zu zeigen:** %d von %d Capabilities erreichen Impact ≥ 8 (tragende Knoten), %d verbinden ≥2 Domänen. Bislang ist das Wissensmodell noch jung (5 Patterns + 1 Automotive-Profil), aber die Methode steht: sobald Medical/Payment/weitere Domänen als DATEN hinzukommen, zeigt dieselbe Aggregation, ob sich der erwartete stabile Kern von 30–50 hochkonvergenten MCAPs bildet — der gemeinsame Strukturkern hinter sehr unterschiedlichen Anforderungswelten. Das ist ein tieferer Wertnachweis als „eine weitere Norm unterstützt". Reine Aggregation, 0 Runtime, 0 neue Architektur.' % (len(core_cut), len(idx), len(cross)))
+w("")
+
+print("\n".join(OUT))
diff --git a/backend-compliance/tests/test_mcap_convergence_analysis.py b/backend-compliance/tests/test_mcap_convergence_analysis.py
new file mode 100644
index 00000000..1fff521a
--- /dev/null
+++ b/backend-compliance/tests/test_mcap_convergence_analysis.py
@@ -0,0 +1,65 @@
+"""Cross-Domain MCAP Convergence Analysis — impact over frequency (Phase Ω pause).
+
+Pins the deterministic MCAP Impact analysis and, critically, the anti-frequency-deception property:
+a capability that bridges many target TYPES / domains / journeys (secure_signed_update_distribution)
+must outrank a high-frequency single-domain management cap (conduct_internal_environmental_audits).
+Four reports (Core / Emerging / Isolated / Suspicious), pure aggregation over existing data, no runtime.
+"""
+
+from __future__ import annotations
+
+import os
+import subprocess
+import sys
+
+
+def _run():
+    root = os.path.join(os.path.dirname(__file__), "..")
+    r = subprocess.run(
+        [sys.executable, "reference_scenarios/mcap_convergence_analysis.py"],
+        cwd=root, env={**os.environ, "PYTHONPATH": "."}, capture_output=True, text=True,
+    )
+    assert r.returncode == 0, r.stderr
+    return r.stdout
+
+
+def _section(out, header):
+    start = out.index(header)
+    nxt = out.find("\n## ", start + 1)
+    return out[start: nxt if nxt != -1 else len(out)]
+
+
+def test_runs_end_to_end():
+    out = _run()
+    assert "Cross-Domain MCAP Convergence Analysis" in out
+    assert "Impact = distinct Sources" in out
+
+
+def test_core_is_cross_cutting_not_frequency():
+    out = _run()
+    core = _section(out, "## 1. Core MCAPs")
+    # the most cross-cutting capability tops the Core report
+    assert "`secure_signed_update_distribution` | **18** |" in core
+    assert "technical_vulnerability_management" in core
+    # a high-frequency BUT single-domain management cap must NOT be in Core (frequency != impact)
+    assert "conduct_internal_environmental_audits" not in core
+
+
+def test_all_four_reports_present():
+    out = _run()
+    for header in ["## 1. Core MCAPs", "## 2. Emerging MCAPs", "## 3. Isolated MCAPs", "## 4. Suspicious MCAPs"]:
+        assert header in out
+
+
+def test_isolated_and_suspicious_are_review_tools():
+    out = _run()
+    iso = _section(out, "## 3. Isolated MCAPs")
+    assert "issue_battery_passport" in iso or "measure_air_emissions" in iso
+    susp = _section(out, "## 4. Suspicious MCAPs")
+    assert "zu grob" in susp and "zu fein" in susp
+
+
+def test_abstraction_level_signal():
+    out = _run()
+    assert "richtigen Abstraktionsniveau" in out
+    assert "Strukturkern" in out
\ No newline at end of file