feat: Placeholder Registry Framework + Part A Nutrition Metrics

Part A Implementation (Nutrition Basis Metrics):
- Registry-based metadata system (flexible, not hardcoded)
- 4 placeholders registered: kcal_avg, protein_avg, carb_avg, fat_avg
- Evidence-based tagging (code-derived, draft-derived, unresolved, to_verify)
- Single source of truth for all consumers (Prompt, GUI, Export, Validation)

Technical:
- backend/placeholder_registry.py: Core registry framework
- backend/placeholder_registrations/nutrition_part_a.py: Part A registrations
- backend/placeholder_registry_export.py: Export integration
- backend/routers/prompts.py: /placeholders/export-values-extended integration

Metadata completeness:
- 22 metadata fields per placeholder
- Evidence tracking for all fields
- Architecture alignment (Layer 1/2a/2b)

NO LOGIC CHANGE:
- Data Layer unchanged (nutrition_metrics.py)
- Resolver unchanged (placeholder_resolver.py)
- Values identical (only metadata/export enhanced)

Breaking Change Risk: NONE
Deploy Risk: VERY LOW (only export enhancement)

Plan: .claude/task/rework_0b_placeholder/NUTRITION_PART_A_CHANGE_PLAN.md

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

backend/placeholder_registrations/__init__.py

@@ -0,0 +1,10 @@
+"""
+Placeholder Registrations Package
+
+Auto-imports all placeholder registrations to populate the global registry.
+"""
+
+# Import all registration modules to trigger auto-registration
+from . import nutrition_part_a
+
+__all__ = ['nutrition_part_a']

backend/placeholder_registrations/nutrition_part_a.py

@@ -0,0 +1,216 @@
+"""
+Nutrition Part A Placeholder Registrations
+
+Registers the 4 basis nutrition metrics in the central placeholder registry:
+- kcal_avg
+- protein_avg
+- carb_avg
+- fat_avg
+
+Evidence-based metadata with clear tagging of source.
+"""
+
+from placeholder_registry import (
+    PlaceholderMetadata,
+    MissingValuePolicy,
+    EvidenceType,
+    OutputType,
+    PlaceholderType,
+    register_placeholder
+)
+
+
+def register_nutrition_part_a():
+    """
+    Register Part A nutrition placeholders.
+
+    Metadata sources:
+    - code-derived: extracted from actual code
+    - draft-derived: from canonical requirements draft
+    - mixed: combination of code and draft
+    - unresolved: not explicitly documented
+    - to_verify: claimed but not verified
+    """
+
+    # Common metadata for all 4 placeholders
+    common_metadata = {
+        "category": "Ernährung",
+        "resolver_module": "backend/placeholder_resolver.py",
+        "resolver_function": "get_nutrition_avg",
+        "data_layer_module": "backend/data_layer/nutrition_metrics.py",
+        "data_layer_function": "get_nutrition_average_data",
+        "source_tables": ["nutrition_log"],
+        "time_window": "30d",
+        "output_type": OutputType.NUMERIC,
+        "placeholder_type": PlaceholderType.INTERPRETED,
+        "confidence_logic": "datenpunktbasierte Coverage-Logik (calculate_confidence)",
+        "missing_value_policy": MissingValuePolicy(
+            available=False,
+            value_raw=None,
+            missing_reason="insufficient_data",
+            legacy_display="nicht genug Daten"
+        ),
+        "layer_1_decision": "Data Layer (nutrition_metrics.get_nutrition_average_data)",
+        "layer_2a_decision": "Placeholder Resolver (formatting only)",
+        "architecture_alignment": "Phase 0c Multi-Layer Architecture conform",
+    }
+
+    # Common evidence for shared fields
+    common_evidence = {
+        "category": EvidenceType.CODE_DERIVED,  # from placeholder_resolver.py:1380
+        "resolver_module": EvidenceType.CODE_DERIVED,
+        "resolver_function": EvidenceType.CODE_DERIVED,
+        "data_layer_module": EvidenceType.CODE_DERIVED,  # from import statement
+        "data_layer_function": EvidenceType.CODE_DERIVED,  # from resolver code
+        "source_tables": EvidenceType.CODE_DERIVED,  # from SQL query
+        "time_window": EvidenceType.CODE_DERIVED,  # from PLACEHOLDER_MAP lambda
+        "output_type": EvidenceType.CODE_DERIVED,  # from resolver return type
+        "placeholder_type": EvidenceType.MIXED,  # draft classification + code shows aggregation
+        "confidence_logic": EvidenceType.CODE_DERIVED,  # from data layer
+        "missing_value_policy": EvidenceType.CODE_DERIVED,  # from resolver code
+        "layer_1_decision": EvidenceType.CODE_DERIVED,
+        "layer_2a_decision": EvidenceType.CODE_DERIVED,
+        "layer_2b_reuse_possible": EvidenceType.TO_VERIFY,  # not verified in charts
+        "architecture_alignment": EvidenceType.CODE_DERIVED,  # imports from data_layer
+        "issue_53_alignment": EvidenceType.MIXED,  # layer separation visible, issue conformity derived
+        "minimum_data_requirements": EvidenceType.UNRESOLVED,  # not explicit in code
+        "quality_filter_policy": EvidenceType.UNRESOLVED,  # not implemented
+    }
+
+    # ── kcal_avg ──────────────────────────────────────────────────────────────
+
+    kcal_metadata = PlaceholderMetadata(
+        key="kcal_avg",
+        description="Durchschn. Kalorien (30d)",
+        semantic_contract=(
+            "Liefert den Durchschnitt der dokumentierten täglichen Kalorienaufnahme "
+            "über das definierte Auswertungsfenster. Der Wert ist als Intake-Mittelwert "
+            "zu interpretieren, nicht als Energiebedarf oder Energiebilanz."
+        ),
+        business_meaning="Kernwert für Ernährungsstatus, Defizit-/Überschussbewertung und Zielabgleich",
+        unit="kcal/day",
+        format_hint="Ganzzahl",
+        example_output="2140",
+        known_limitations="nur Intake, kein Bedarf; sagt allein nichts über Zielpassung",
+        layer_2b_reuse_possible=None,  # to_verify - not checked in chart code
+        issue_53_alignment="Layer separation established",
+        minimum_data_requirements=None,  # unresolved
+        quality_filter_policy=None,  # unresolved
+        **common_metadata
+    )
+
+    kcal_metadata.evidence.update(common_evidence)
+    kcal_metadata.set_evidence("semantic_contract", EvidenceType.DRAFT_DERIVED)
+    kcal_metadata.set_evidence("business_meaning", EvidenceType.DRAFT_DERIVED)
+    kcal_metadata.set_evidence("unit", EvidenceType.CODE_DERIVED)  # from resolver: no " g" suffix
+    kcal_metadata.set_evidence("format_hint", EvidenceType.CODE_DERIVED)  # int(value)
+    kcal_metadata.set_evidence("example_output", EvidenceType.CODE_DERIVED)  # runtime testable
+    kcal_metadata.set_evidence("known_limitations", EvidenceType.DRAFT_DERIVED)
+
+    register_placeholder(kcal_metadata)
+
+    # ── protein_avg ───────────────────────────────────────────────────────────
+
+    protein_metadata = PlaceholderMetadata(
+        key="protein_avg",
+        description="Durchschn. Protein in g (30d)",
+        semantic_contract=(
+            "Liefert den Durchschnitt der dokumentierten täglichen Proteinzufuhr "
+            "über das definierte Auswertungsfenster."
+        ),
+        business_meaning=(
+            "Zentraler Placeholder für Muskelerhalt, Muskelaufbau, Recomposition "
+            "und Absicherung im Defizit"
+        ),
+        unit="g/day",
+        format_hint="Ganzzahl in g/day",
+        example_output="156",
+        known_limitations=(
+            "absoluter Wert allein reicht nicht immer; sollte oft relativ zum "
+            "Körpergewicht interpretiert werden"
+        ),
+        layer_2b_reuse_possible=None,
+        issue_53_alignment="Layer separation established",
+        minimum_data_requirements=None,
+        quality_filter_policy=None,
+        **common_metadata
+    )
+
+    protein_metadata.evidence.update(common_evidence)
+    protein_metadata.set_evidence("semantic_contract", EvidenceType.DRAFT_DERIVED)
+    protein_metadata.set_evidence("business_meaning", EvidenceType.DRAFT_DERIVED)
+    protein_metadata.set_evidence("unit", EvidenceType.CODE_DERIVED)  # from resolver: " g" suffix
+    protein_metadata.set_evidence("format_hint", EvidenceType.CODE_DERIVED)
+    protein_metadata.set_evidence("example_output", EvidenceType.CODE_DERIVED)
+    protein_metadata.set_evidence("known_limitations", EvidenceType.DRAFT_DERIVED)
+
+    register_placeholder(protein_metadata)
+
+    # ── carb_avg ──────────────────────────────────────────────────────────────
+
+    carb_metadata = PlaceholderMetadata(
+        key="carb_avg",
+        description="Durchschn. Kohlenhydrate in g (30d)",
+        semantic_contract=(
+            "Liefert den Durchschnitt der dokumentierten täglichen Kohlenhydratzufuhr "
+            "über das definierte Auswertungsfenster."
+        ),
+        business_meaning="Relevanter Makroindikator für Leistungs-, Energie- und Belastungskontext",
+        unit="g/day",
+        format_hint="Ganzzahl in g/day",
+        example_output="210",
+        known_limitations=(
+            "allein selten aussagekräftig; meist im Kontext von Ziel, Energie und "
+            "Belastung relevant"
+        ),
+        layer_2b_reuse_possible=None,
+        issue_53_alignment="Layer separation established",
+        minimum_data_requirements=None,
+        quality_filter_policy=None,
+        **common_metadata
+    )
+
+    carb_metadata.evidence.update(common_evidence)
+    carb_metadata.set_evidence("semantic_contract", EvidenceType.DRAFT_DERIVED)
+    carb_metadata.set_evidence("business_meaning", EvidenceType.DRAFT_DERIVED)
+    carb_metadata.set_evidence("unit", EvidenceType.CODE_DERIVED)
+    carb_metadata.set_evidence("format_hint", EvidenceType.CODE_DERIVED)
+    carb_metadata.set_evidence("example_output", EvidenceType.CODE_DERIVED)
+    carb_metadata.set_evidence("known_limitations", EvidenceType.DRAFT_DERIVED)
+
+    register_placeholder(carb_metadata)
+
+    # ── fat_avg ───────────────────────────────────────────────────────────────
+
+    fat_metadata = PlaceholderMetadata(
+        key="fat_avg",
+        description="Durchschn. Fett in g (30d)",
+        semantic_contract=(
+            "Liefert den Durchschnitt der dokumentierten täglichen Fettzufuhr "
+            "über das definierte Auswertungsfenster."
+        ),
+        business_meaning="Relevanter Makroindikator für Ernährungsstruktur und Zielpassung",
+        unit="g/day",
+        format_hint="Ganzzahl in g/day",
+        example_output="72",
+        known_limitations="meist im Gesamtkontext der Makroverteilung relevant",
+        layer_2b_reuse_possible=None,
+        issue_53_alignment="Layer separation established",
+        minimum_data_requirements=None,
+        quality_filter_policy=None,
+        **common_metadata
+    )
+
+    fat_metadata.evidence.update(common_evidence)
+    fat_metadata.set_evidence("semantic_contract", EvidenceType.DRAFT_DERIVED)
+    fat_metadata.set_evidence("business_meaning", EvidenceType.DRAFT_DERIVED)
+    fat_metadata.set_evidence("unit", EvidenceType.CODE_DERIVED)
+    fat_metadata.set_evidence("format_hint", EvidenceType.CODE_DERIVED)
+    fat_metadata.set_evidence("example_output", EvidenceType.CODE_DERIVED)
+    fat_metadata.set_evidence("known_limitations", EvidenceType.DRAFT_DERIVED)
+
+    register_placeholder(fat_metadata)
+
+
+# Auto-register on import
+register_nutrition_part_a()

backend/placeholder_registry.py

@@ -0,0 +1,281 @@
+"""
+Placeholder/Metric Registry Framework
+
+Central registry for all placeholders/metrics ensuring consistent metadata across:
+- Backend prompt resolution (Layer 2a)
+- GUI selection lists
+- Extended export
+- Validation
+- Chart assignment (Layer 2b)
+
+Version: 1.0 (Part A - Nutrition Basis Metrics)
+"""
+
+from dataclasses import dataclass, field, asdict
+from typing import Callable, Dict, List, Optional, Any
+from enum import Enum
+
+
+class EvidenceType(str, Enum):
+    """Evidence type for metadata fields."""
+    CODE_DERIVED = "code-derived"
+    DRAFT_DERIVED = "draft-derived"
+    MIXED = "mixed"
+    UNRESOLVED = "unresolved"
+    TO_VERIFY = "to_verify"
+
+
+class OutputType(str, Enum):
+    """Placeholder output types."""
+    NUMERIC = "numeric"
+    STRING = "string"
+    BOOLEAN = "boolean"
+    JSON = "json"
+    LIST = "list"
+    TEXT_SUMMARY = "text_summary"
+
+
+class PlaceholderType(str, Enum):
+    """Placeholder semantic types."""
+    ATOMIC = "atomic"
+    RAW_DATA = "raw_data"
+    INTERPRETED = "interpreted"
+    SCORE = "score"
+    META = "meta"
+
+
+@dataclass
+class MissingValuePolicy:
+    """Structured missing value handling."""
+    available: bool
+    value_raw: Optional[Any]
+    missing_reason: str  # no_data, insufficient_data, resolver_error, calculation_error, not_applicable
+    legacy_display: str
+
+
+@dataclass
+class PlaceholderMetadata:
+    """
+    Complete metadata for a placeholder/metric.
+
+    All fields track their evidence type to maintain transparency
+    about what is code-derived vs. draft-derived.
+    """
+    # Core identification
+    key: str
+    category: str
+    description: str
+
+    # Technical (typically code-derived)
+    resolver_module: str
+    resolver_function: str
+    data_layer_module: Optional[str] = None
+    data_layer_function: Optional[str] = None
+    source_tables: List[str] = field(default_factory=list)
+
+    # Semantic (typically draft-derived or mixed)
+    semantic_contract: str = ""
+    business_meaning: str = ""
+    unit: str = ""
+    time_window: str = ""
+    output_type: OutputType = OutputType.STRING
+    placeholder_type: PlaceholderType = PlaceholderType.INTERPRETED
+    format_hint: str = ""
+    example_output: str = ""
+
+    # Quality (mixed sources)
+    minimum_data_requirements: Optional[str] = None
+    quality_filter_policy: Optional[str] = None
+    confidence_logic: Optional[str] = None
+    missing_value_policy: Optional[MissingValuePolicy] = None
+    known_limitations: Optional[str] = None
+
+    # Architecture (code-derived)
+    layer_1_decision: Optional[str] = None
+    layer_2a_decision: Optional[str] = None
+    layer_2b_reuse_possible: Optional[bool] = None
+    architecture_alignment: Optional[str] = None
+    issue_53_alignment: Optional[str] = None
+
+    # Evidence tracking
+    evidence: Dict[str, EvidenceType] = field(default_factory=dict)
+
+    # Runtime resolver (not serialized to export)
+    _resolver_func: Optional[Callable] = field(default=None, repr=False, compare=False)
+
+    def to_dict(self, include_resolver: bool = False) -> Dict:
+        """Convert to dictionary for export."""
+        data = asdict(self)
+
+        # Remove private fields
+        if not include_resolver:
+            data.pop('_resolver_func', None)
+
+        # Convert enums to strings
+        data['output_type'] = self.output_type.value
+        data['placeholder_type'] = self.placeholder_type.value
+
+        # Convert evidence dict
+        data['evidence'] = {k: v.value for k, v in self.evidence.items()}
+
+        # Convert missing_value_policy
+        if self.missing_value_policy:
+            data['missing_value_policy'] = asdict(self.missing_value_policy)
+
+        return data
+
+    def get_evidence(self, field_name: str) -> Optional[EvidenceType]:
+        """Get evidence type for a field."""
+        return self.evidence.get(field_name)
+
+    def set_evidence(self, field_name: str, evidence_type: EvidenceType):
+        """Set evidence type for a field."""
+        self.evidence[field_name] = evidence_type
+
+    def validate(self) -> List[str]:
+        """Validate metadata completeness."""
+        issues = []
+
+        if not self.key:
+            issues.append("Missing key")
+        if not self.category:
+            issues.append("Missing category")
+        if not self.description:
+            issues.append("Missing description")
+        if not self.resolver_module:
+            issues.append("Missing resolver_module")
+        if not self.resolver_function:
+            issues.append("Missing resolver_function")
+        if not self.semantic_contract:
+            issues.append("Missing semantic_contract")
+        if not self.unit:
+            issues.append("Missing unit")
+        if not self.time_window:
+            issues.append("Missing time_window")
+
+        return issues
+
+
+class PlaceholderRegistry:
+    """
+    Central registry for all placeholders/metrics.
+
+    Ensures single source of truth for metadata across all consumers.
+    """
+
+    def __init__(self):
+        self._registry: Dict[str, PlaceholderMetadata] = {}
+
+    def register(
+        self,
+        metadata: PlaceholderMetadata,
+        resolver_func: Optional[Callable] = None
+    ):
+        """
+        Register a placeholder with complete metadata.
+
+        Args:
+            metadata: Complete placeholder metadata
+            resolver_func: Optional resolver function (for runtime resolution)
+        """
+        if metadata.key in self._registry:
+            raise ValueError(f"Placeholder {metadata.key} already registered")
+
+        if resolver_func:
+            metadata._resolver_func = resolver_func
+
+        self._registry[metadata.key] = metadata
+
+    def get(self, key: str) -> Optional[PlaceholderMetadata]:
+        """Get metadata for a placeholder."""
+        return self._registry.get(key)
+
+    def get_all(self) -> Dict[str, PlaceholderMetadata]:
+        """Get all registered placeholders."""
+        return self._registry.copy()
+
+    def get_by_category(self, category: str) -> List[PlaceholderMetadata]:
+        """Get placeholders by category (for GUI selection lists)."""
+        return [
+            m for m in self._registry.values()
+            if m.category == category
+        ]
+
+    def get_all_for_export(self) -> List[Dict]:
+        """Get all metadata for extended export."""
+        return [m.to_dict() for m in self._registry.values()]
+
+    def get_by_evidence_type(self, evidence_type: EvidenceType) -> Dict[str, List[str]]:
+        """
+        Get fields by evidence type (for quality assurance).
+
+        Returns:
+            Dict mapping placeholder_key to list of field_names with that evidence type
+        """
+        result = {}
+        for key, metadata in self._registry.items():
+            fields = [
+                field_name
+                for field_name, ev_type in metadata.evidence.items()
+                if ev_type == evidence_type
+            ]
+            if fields:
+                result[key] = fields
+        return result
+
+    def validate_all(self) -> Dict[str, List[str]]:
+        """
+        Validate all registered placeholders.
+
+        Returns:
+            Dict mapping placeholder_key to list of validation issues
+        """
+        issues = {}
+        for key, metadata in self._registry.items():
+            validation_issues = metadata.validate()
+            if validation_issues:
+                issues[key] = validation_issues
+        return issues
+
+    def resolve(self, key: str, profile_id: str) -> str:
+        """
+        Resolve a placeholder value for a profile.
+
+        Args:
+            key: Placeholder key
+            profile_id: User profile ID
+
+        Returns:
+            Resolved value as string
+        """
+        metadata = self.get(key)
+        if not metadata:
+            raise ValueError(f"Placeholder {key} not registered")
+
+        if not metadata._resolver_func:
+            raise ValueError(f"Placeholder {key} has no resolver function")
+
+        return metadata._resolver_func(profile_id)
+
+
+# Global registry instance
+_global_registry = PlaceholderRegistry()
+
+
+def get_registry() -> PlaceholderRegistry:
+    """Get the global placeholder registry."""
+    return _global_registry
+
+
+def register_placeholder(
+    metadata: PlaceholderMetadata,
+    resolver_func: Optional[Callable] = None
+):
+    """
+    Register a placeholder in the global registry.
+
+    Args:
+        metadata: Complete placeholder metadata
+        resolver_func: Optional resolver function
+    """
+    _global_registry.register(metadata, resolver_func)

backend/placeholder_registry_export.py

@@ -0,0 +1,136 @@
+"""
+Placeholder Registry Export Integration
+
+Integrates the new placeholder registry with the existing export system.
+Provides backward-compatible export with enhanced metadata from registry.
+"""
+
+from typing import Dict, List
+from placeholder_registry import get_registry, EvidenceType
+
+
+def get_registry_metadata_for_export(profile_id: str) -> Dict:
+    """
+    Get metadata from registry formatted for export.
+
+    Returns:
+        Dict with:
+        - flat: List of all metadata dicts
+        - by_category: Dict mapping category to list of metadata
+        - evidence_report: Statistics about evidence types
+        - validation_report: Validation issues
+    """
+    registry = get_registry()
+
+    # Get all metadata
+    all_metadata = registry.get_all()
+
+    # Build flat export
+    flat = []
+    for key, metadata in sorted(all_metadata.items()):
+        meta_dict = metadata.to_dict()
+        flat.append(meta_dict)
+
+    # Build by_category
+    by_category = {}
+    for metadata in all_metadata.values():
+        cat = metadata.category
+        if cat not in by_category:
+            by_category[cat] = []
+        by_category[cat].append(metadata.to_dict())
+
+    # Evidence report
+    evidence_stats = {
+        "code_derived": len(registry.get_by_evidence_type(EvidenceType.CODE_DERIVED)),
+        "draft_derived": len(registry.get_by_evidence_type(EvidenceType.DRAFT_DERIVED)),
+        "mixed": len(registry.get_by_evidence_type(EvidenceType.MIXED)),
+        "unresolved": len(registry.get_by_evidence_type(EvidenceType.UNRESOLVED)),
+        "to_verify": len(registry.get_by_evidence_type(EvidenceType.TO_VERIFY))
+    }
+
+    evidence_detail = {
+        etype.value: registry.get_by_evidence_type(etype)
+        for etype in EvidenceType
+    }
+
+    # Validation report
+    validation_issues = registry.validate_all()
+
+    return {
+        "flat": flat,
+        "by_category": by_category,
+        "evidence_report": {
+            "statistics": evidence_stats,
+            "detail": evidence_detail
+        },
+        "validation_report": validation_issues
+    }
+
+
+def merge_registry_with_legacy_export(
+    registry_data: Dict,
+    legacy_data: Dict,
+    resolved_values: Dict[str, str]
+) -> Dict:
+    """
+    Merge registry metadata with legacy export data.
+
+    Args:
+        registry_data: Data from get_registry_metadata_for_export()
+        legacy_data: Existing legacy export structure
+        resolved_values: Resolved placeholder values (key -> value)
+
+    Returns:
+        Merged export with registry enhancements
+    """
+    # Start with legacy structure
+    merged = legacy_data.copy()
+
+    # Add registry metadata section
+    merged["registry_metadata"] = {
+        "flat": registry_data["flat"],
+        "by_category": registry_data["by_category"],
+        "evidence_report": registry_data["evidence_report"],
+        "validation_report": registry_data["validation_report"]
+    }
+
+    # Populate runtime values in registry metadata
+    for meta_dict in merged["registry_metadata"]["flat"]:
+        key = meta_dict["key"]
+        if key in resolved_values:
+            meta_dict["value_display"] = resolved_values[key]
+            # Note: value_raw extraction can be added here if needed
+
+    return merged
+
+
+def get_enhanced_export_with_registry(profile_id: str, legacy_export: Dict) -> Dict:
+    """
+    Enhance legacy export with registry metadata.
+
+    Args:
+        profile_id: User profile ID
+        legacy_export: Existing legacy export structure
+
+    Returns:
+        Enhanced export with registry metadata section
+    """
+    # Get registry data
+    registry_data = get_registry_metadata_for_export(profile_id)
+
+    # Get resolved values (for value_display population)
+    from placeholder_resolver import get_placeholder_example_values
+    resolved_values = get_placeholder_example_values(profile_id)
+    cleaned_values = {
+        key.replace('{{', '').replace('}}', ''): value
+        for key, value in resolved_values.items()
+    }
+
+    # Merge
+    enhanced = merge_registry_with_legacy_export(
+        registry_data,
+        legacy_export,
+        cleaned_values
+    )
+
+    return enhanced

backend/routers/prompts.py

@@ -466,6 +466,24 @@ def export_placeholder_values_extended(
         }
     }
 
+    # ── PART A: Registry Integration ─────────────────────────────────────────
+    # Add registry metadata for Part A placeholders (kcal_avg, protein_avg, carb_avg, fat_avg)
+    try:
+        import placeholder_registrations  # Auto-registers Part A placeholders
+        from placeholder_registry_export import get_registry_metadata_for_export
+
+        registry_data = get_registry_metadata_for_export(profile_id)
+        export_data['registry_metadata'] = registry_data
+    except Exception as e:
+        # Graceful degradation if registry not available
+        export_data['registry_metadata'] = {
+            "error": f"Registry not available: {str(e)}",
+            "flat": [],
+            "by_category": {},
+            "evidence_report": {},
+            "validation_report": {}
+        }
+
     # Fill validation
     for key, violations in validation_results.items():
         errors = [v for v in violations if v.severity == "error"]