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>
2026-04-02 11:46:16 +02:00
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"""
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Placeholder Registrations Package
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Auto-imports all placeholder registrations to populate the global registry.
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"""
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# Import all registration modules to trigger auto-registration
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from . import nutrition_part_a
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2026-04-02 12:27:58 +02:00
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from . import nutrition_part_b
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feat: Placeholder Registry Part C - Nutrition Consistency & Balance
Registers 5 nutrition-related placeholders with complete metadata:
- macro_consistency_score: CV-based Makro-Konsistenz Score (0-100)
- energy_balance_7d: Energiebilanz (kcal/day avg, intake - TDEE)
- energy_deficit_surplus: Status (deficit/maintenance/surplus)
- intake_volatility: Klassifikation (stable/moderate/high)
- nutrition_days: Anzahl valider Ernährungstage (30d)
All placeholders with evidence-based tagging:
- 22 metadata fields per placeholder
- CODE_DERIVED: Technical fields, formulas from code inspection
- DRAFT_DERIVED: Semantic fields from canonical requirements
- MIXED: Calculation logic (TDEE model, thresholds, formulas)
- TO_VERIFY: Architecture layer decisions
Critical details documented:
- macro_consistency_score: CV formula + thresholds explicitly documented
- energy_balance_7d: TDEE model (weight_kg × 32.5), unit clarified (kcal/day avg)
- energy_deficit_surplus: Status thresholds (<-200, -200 to +200, >+200)
- intake_volatility: Category mapping from macro_consistency_score
- nutrition_days: Validation criteria (any entry = valid day)
Known limitations captured:
- TDEE model is simplified (no activity/age/gender adjustment)
- Thresholds are somewhat arbitrary (e.g., 200 kcal for deficit/surplus)
- High volatility not necessarily bad (context-dependent)
Registry now contains 14 placeholders total:
- Part A: 4 (kcal_avg, protein_avg, carb_avg, fat_avg)
- Part B: 5 (protein targets + adequacy)
- Part C: 5 (consistency + balance + meta)
Framework: PLACEHOLDER_REGISTRY_FRAMEWORK.md (verbindlich ab 2026-04-02)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-04-02 12:55:03 +02:00
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from . import nutrition_part_c
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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>
2026-04-02 11:46:16 +02:00
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feat: Placeholder Registry Part C - Nutrition Consistency & Balance
Registers 5 nutrition-related placeholders with complete metadata:
- macro_consistency_score: CV-based Makro-Konsistenz Score (0-100)
- energy_balance_7d: Energiebilanz (kcal/day avg, intake - TDEE)
- energy_deficit_surplus: Status (deficit/maintenance/surplus)
- intake_volatility: Klassifikation (stable/moderate/high)
- nutrition_days: Anzahl valider Ernährungstage (30d)
All placeholders with evidence-based tagging:
- 22 metadata fields per placeholder
- CODE_DERIVED: Technical fields, formulas from code inspection
- DRAFT_DERIVED: Semantic fields from canonical requirements
- MIXED: Calculation logic (TDEE model, thresholds, formulas)
- TO_VERIFY: Architecture layer decisions
Critical details documented:
- macro_consistency_score: CV formula + thresholds explicitly documented
- energy_balance_7d: TDEE model (weight_kg × 32.5), unit clarified (kcal/day avg)
- energy_deficit_surplus: Status thresholds (<-200, -200 to +200, >+200)
- intake_volatility: Category mapping from macro_consistency_score
- nutrition_days: Validation criteria (any entry = valid day)
Known limitations captured:
- TDEE model is simplified (no activity/age/gender adjustment)
- Thresholds are somewhat arbitrary (e.g., 200 kcal for deficit/surplus)
- High volatility not necessarily bad (context-dependent)
Registry now contains 14 placeholders total:
- Part A: 4 (kcal_avg, protein_avg, carb_avg, fat_avg)
- Part B: 5 (protein targets + adequacy)
- Part C: 5 (consistency + balance + meta)
Framework: PLACEHOLDER_REGISTRY_FRAMEWORK.md (verbindlich ab 2026-04-02)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-04-02 12:55:03 +02:00
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__all__ = ['nutrition_part_a', 'nutrition_part_b', 'nutrition_part_c']
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