- Introduced new fields for descriptions in training parameters, improving clarity for AI context in `training_sessions_recent_json`.
- Added a glossary placeholder `{{training_parameters_glossary_md}}` to provide a Markdown table of active training parameters, including names and descriptions.
- Updated the `placeholder_resolver.py` and `activity_metrics.py` to support the new glossary functionality.
- Enhanced the `AdminActivityAttributeProfilesPage` to allow input for descriptions in both German and English, ensuring better context for metrics.
- Revised tests to validate the inclusion of description fields in parameter schema merges and metrics handling.
- Added details for Issue #53 regarding the audit of activity placeholders between Layer 1 and Layer 2a in `CLAUDE.md` and `README.md`.
- Updated the `ACTIVITY_SESSION_METRICS_EAV_AGENT_GUIDE.md` to reflect the new registry checks and dynamic session metrics handling.
- Revised the `placeholder_resolver.py` and `activity_metrics.py` to clarify the registration of activity metrics and session insights, ensuring consistency in the handling of dynamic keys and metrics.
- Improved descriptions and semantic contracts in `activity_session_insights.py` to better outline the structure and limitations of session data.
- Updated the `get_activity_detail` function to include session metrics in the activity detail output, allowing for enriched data representation.
- Refactored the activity import logic to streamline the process of inserting and updating activity records, utilizing new helper functions for better maintainability.
- Improved the handling of duplicate activity entries by implementing a more robust identification mechanism.
- Enhanced the metadata for activity detail registration to reflect the inclusion of EAV metrics and updated source tables.
- Introduced Activity Session Metrics for enhanced tracking of session data.
- Updated backend to support new API endpoints for managing session metrics.
- Added new Pydantic models for activity metrics and replaced metrics functionality.
- Enhanced data layer to include session metrics in recent training session data.
- Updated documentation to reflect changes in session metrics handling.
- Adjusted the total number of placeholders from 116 to 114 across various documentation and code files to reflect the current state of the system.
- Enhanced TDEE calculation logic in `nutrition_metrics.py` to prioritize Mifflin–St Jeor BMR with PAL when demographic data is available, with a fallback to a weight-based estimate.
- Updated placeholder registrations to ensure consistency with the new metadata structure and improved data handling.
- Revised documentation to clarify the authoritative source of placeholder metadata and the implications of the changes on existing functionalities.
These updates improve the accuracy and consistency of the placeholder system and enhance the nutritional assessment capabilities within the application.
- Introduced functions to retrieve profile name, age, height, and gender for better placeholder resolution.
- Added functions for displaying current date and time period labels (last 7, 30, and 90 days).
- Updated PLACEHOLDER_MAP to utilize new functions for improved readability and maintainability.
- Enhanced placeholder registrations in __init__.py to include new modules for sleep, vital metrics, and profile time periods.
These changes enhance the flexibility and functionality of the placeholder system, allowing for more dynamic content generation.
- Added new functions for BMI and goal weight/body fat percentage retrieval in `body_metrics.py`.
- Introduced training frequency and inter-session gap calculations in `activity_metrics.py`.
- Updated placeholder registrations to include new metrics for nutrition and activity.
- Improved data handling in `placeholder_resolver.py` for better integration of new metrics.
- Enhanced documentation across modules to reflect the new functionalities.
These updates improve the accuracy and comprehensiveness of health and fitness assessments within the application.
Behebt letzte Inkonsistenzen im Export:
1. protein_g_per_kg:
- time_window: 'mixed' → '7d' (dominante Komponente)
- Kommentar angepasst: weight ist snapshot, aber protein (7d) ist primär
- known_limitations dokumentiert die Inkonsistenz weiterhin
2. protein_adequacy_28d:
- unit: 'score' → 'score (0-100)' (Konsistenz mit macro_consistency_score)
- Klarere Skalen-Angabe im Export
Finaler Export-Status: 14/14 Nutrition Placeholders konsistent
- Alle haben korrekte Category (Ernährung)
- Alle haben präzise Units
- Alle haben eindeutige Time Windows
- Alle haben korrekte Output Types
Abschlussarbeit für Ernährungs-Cluster.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
