- Introduced `filter_rematch_slot_indices` to exclude preserved slots from rematching, improving the accuracy of slot assignments.
- Added `_slot_priority_for_rematch` to prioritize existing slot assignments during rematching, enhancing the robustness of the rematch process.
- Updated `_run_roadmap_rematch_loop` to utilize the new filtering and prioritization logic, ensuring better handling of rematch scenarios.
- Enhanced tests in `test_planning_path_rematch.py` to validate the new filtering behavior and ensure correct exercise restoration when not rejected.
- Bumped version to reflect the new features and improvements.
- Updated `PROJECT_STATUS.md` to reflect the addition of the Planning AI Progression Graph and its context in the roadmap.
- Enhanced `DOMAIN_MODEL.md` with details on the new `planning_catalog_context` features, allowing trainers to manage curriculum stages and context.
- Added tests in `test_planning_catalog_context.py` to validate the separation of LLM highlights from fix hints during QA processes.
- Updated `HANDOVER.md` and `PLANNING_KI_ROADMAP.md` to reflect the latest app version and improvements in the planning context.
- Enhanced frontend components to support the new planning catalog context, including updates to `ExerciseProgressionPathBuilder` and `ProgressionGraphEditor`.
- Bumped version to 0.8.233 to reflect the new features and improvements.
- Introduced `planning_catalog_context` to `ProgressionPathSuggestRequest` for improved handling of catalog-related data during path suggestions.
- Implemented `_resolve_planning_catalog_context` to retrieve and validate the planning catalog context, enhancing the robustness of the suggestion process.
- Updated `_build_path_target_profile` to incorporate catalog context, enriching target profiles with relevant planning data.
- Enhanced frontend components in `ProgressionGraphEditor` to manage and display planning catalog context, including new selection options for focus areas, style directions, training types, and target groups.
- Added utility functions for parsing and transforming planning catalog context data for API interactions.
- Bumped version to 0.8.233 to reflect the new features and improvements.
- Introduced `_roadmap_step_passes_post_match_gate` to validate steps after matching, ensuring only relevant steps proceed.
- Enhanced `_enrich_roadmap_unfilled_gap_offers` to generate AI gap offers for unfilled roadmap slots, improving exercise suggestions.
- Updated `suggest_progression_path` to incorporate new gap offer logic and streamline the handling of roadmap steps.
- Refined frontend logic in `applyMatchStepsToSlots` to better manage step assignments and improve clarity in slot handling.
- Bumped version to 0.8.231 to reflect the new features and improvements.
- Updated `max_rematch_rounds` in `ProgressionPathSuggestRequest` to allow for a maximum of 3 rounds, improving flexibility in rematch processes.
- Introduced `_track_rejected` function to track rejected exercises by major step index, enhancing the rematch logic to account for previously rejected exercises.
- Enhanced `_run_roadmap_rematch_loop` to utilize the new rejection tracking, ensuring better handling of off-topic steps during rematching.
- Improved `detect_off_topic_steps` to incorporate refined scoring and reasoning for stage fit, enhancing the accuracy of off-topic detection.
- Updated `refine_stage_spec_artifact` to merge stage exclusion phrases more effectively, improving the clarity of anti-pattern handling.
- Bumped version to 0.8.228 to reflect the new features and improvements.
- Introduced `auto_refine_stage_spec` to `ProgressionPathSuggestRequest`, enabling optional refinement of stage specifications during the rematch process.
- Updated `_run_roadmap_rematch_loop` to incorporate stage specification refinements, logging changes for better tracking of adjustments made during rematching.
- Enhanced `suggest_progression_path` to include refine logs in the output, providing clearer insights into the refinement process.
- Added utility functions for formatting refine log entries, improving the display of refinement actions in the frontend components.
- Updated frontend components to display refine logs, enhancing user feedback on stage specification adjustments during progression analysis.
- Bumped version to 0.8.227 to reflect the new features and improvements.
- Added support for displaying optimization hints and rematch logs in the Progression Findings Panel, improving user feedback on potential enhancements.
- Introduced new utility functions for formatting rematch log entries and resolving hint slot indices, enhancing clarity in displayed information.
- Updated the Progression Graph Editor to handle rematch actions and display relevant match summaries, ensuring comprehensive insights during progression analysis.
- Enhanced the utility functions to support the new features, ensuring robust handling of optimization actions and rematch logic.
- Updated `suggest_progression_path` to ensure unique gap fill offers are collected and added based on their IDs, improving the relevance of suggestions.
- Refined the logic for setting `slot_status` and handling `gap_offer` and `proposal_key` in steps, enhancing clarity in progression path management.
- Improved the `collectGapOffersFromApiResponse` function to consolidate gap offers from various sources, ensuring comprehensive offer retrieval.
- Enhanced the handling of unfilled slots in `applyMatchStepsToSlots`, ensuring proper assignment of proposals and gap offers.
- Added tests to validate the new logic for gap fill offers and slot assignments, ensuring robustness in path suggestion features.
- Added `_safe_tsquery_fragment` to sanitize learning goal input for SQL queries, improving query safety.
- Introduced `_fetch_learning_goal_library_candidate_ids` to retrieve exercise IDs matching learning goals, enhancing exercise relevance in roadmap suggestions.
- Enhanced `_match_roadmap_slot` to utilize learning goal candidates, improving the accuracy of supplemental exercise selection.
- Implemented `_pick_roadmap_rank_fallback` to provide a fallback mechanism for selecting the best exercise when strict matching fails, ensuring better exercise retrieval.
- Updated tests to validate the new learning goal retrieval and fallback logic, ensuring robustness in exercise selection processes.
- Introduced new functions for handling exercise visibility and retrieval based on progression graph context, including `fetch_exercise_rows_by_ids_for_graph`.
- Updated `_load_supplemental_exercise_rows` to incorporate graph visibility rules, improving the accuracy of exercise retrieval.
- Enhanced `_run_path_step_retrieval` to utilize preloaded supplemental exercise rows, optimizing performance and clarity in path step processing.
- Added `exercise_title_equivalent_to_stage_goal` function to improve title matching against learning goals, enhancing exercise relevance.
- Updated tests to validate new retrieval logic and title equivalence functionality, ensuring robustness in exercise selection processes.
- Added `preserve_slot_assignments` and `retrieval_boost_exercise_ids` to `ProgressionPathSuggestRequest` for improved handling of exercise suggestions.
- Refactored `_supplemental_exercise_ids_from_body` to incorporate retrieval boost exercise IDs, ensuring they are prioritized over slot assignments.
- Updated `_build_steps_roadmap_first` to conditionally preserve slot assignments based on the new flag.
- Enhanced tests to validate the new retrieval boost logic and its integration with existing slot assignment handling.
- Introduced `slot_assignments` to `ProgressionPathSuggestRequest` for improved handling of existing slot assignments in path building.
- Implemented `_slot_assignments_by_major_index` and `_path_step_from_slot_assignment` functions to facilitate the integration of slot assignments into the path generation process.
- Updated `_build_steps_roadmap_first` to utilize slot assignments, enhancing the accuracy of path steps based on existing exercise slots.
- Enhanced `detect_path_gaps` to skip empty slots, preventing unnecessary errors during gap detection.
- Added tests to validate the new slot assignment handling and ensure robustness in path generation logic.
- Added `tzdata` installation in the Dockerfile to support time zone handling in Linux environments.
- Increased `PIP_DEFAULT_TIMEOUT` and added retry logic for pip installations to enhance reliability during dependency installation.
- Updated `requirements.txt` to conditionally include `tzdata` for Windows platforms, ensuring compatibility across different operating systems.
- Updated `suggest_progression_path` to include AI-generated gap fill offers when exercises are missing, improving the relevance of suggested paths.
- Introduced a match summary to provide insights on library matches and gap fill offers, enhancing user feedback in the `ProgressionGraphEditor`.
- Refined the `pick_best_path_hit` function to ensure proper handling of roadmap stage matches based on primary topics.
- Added tests to validate the new gap fill offer logic and match summary functionality, ensuring robustness in path suggestion features.
- Introduced `resolve_path_primary_topic` function to enhance the determination of primary topics from goal queries and semantic briefs, improving exercise relevance.
- Updated `_match_roadmap_slot` and `detect_off_topic_steps` functions to utilize the new primary topic resolution logic, ensuring accurate topic identification.
- Enhanced tests to validate the functionality of primary topic resolution and its impact on exercise selection and off-topic detection.
- Improved handling of primary topics in the `ExerciseProgressionPathBuilder` and related components for better integration with the overall path-building process.
- Improved off-topic step handling by incorporating roadmap major step indices for better indexing and detection.
- Refactored `collect_gap_fill_specs` to streamline the insertion logic for off-topic steps, ensuring correct placement based on major step indices.
- Introduced `_normalize_roadmap_steps_coverage` function to standardize roadmap steps coverage, enhancing the handling of missing slots.
- Added `prune_stripped_after_rematch` function to clean up stripped off-topic steps after rematching, improving the overall rematching process.
- Updated tests to validate new rematching and off-topic handling features, ensuring robustness against edge cases.
- Incremented application version to reflect these updates.
- Refactored `ExerciseProgressionGraphPanel` to support a create dialog for new progression graphs, improving user experience.
- Integrated `ProgressionGraphListCard` for better visualization of existing graphs and streamlined management.
- Updated `ProgressionGraphEditor` to handle start/target analysis and improved draft hydration with AI suggestions.
- Added utility functions for managing structured responses from AI, enhancing the planning process.
- Incremented application version to reflect these updates.
- Introduced skills catalog management in the `ProgressionGraphEditor`, allowing for improved context in AI suggestions.
- Updated the loading mechanism to fetch both focus areas and skills catalog concurrently, enhancing performance.
- Implemented `ensureQuickCreateDraftFromAiSuggestion` utility to streamline the creation of drafts from AI suggestions.
- Enhanced slot management by integrating AI context into the gap fill preparation process, improving user experience.
- Incremented application version to reflect these updates.
- Implemented functions to resolve neighboring steps based on major indices and build AI context for unfilled roadmap stages.
- Enhanced `try_suggest_ai_stage_step` to generate AI proposals for empty roadmap stages, improving user experience in gap filling.
- Updated `build_gap_fill_offer` to utilize major step neighbors for better context in offers related to unfilled slots.
- Added tests to ensure correct functionality of AI suggestion handling in the context of roadmap gaps.
- Incremented application version to reflect these updates.
- Implemented `_build_evaluate_empty_slot_gap_specs` function to generate gap offer specifications for unfilled roadmap slots in evaluate-only mode.
- Enhanced `ProgressionFindingsPanel` to display AI offers for empty slots and gaps, improving user interaction and clarity.
- Updated `ProgressionGraphEditor` and `ProgressionSlotCard` components to support new functionalities for managing slots and offers.
- Refactored utility functions in `progressionGraphDraft.js` to streamline slot management and offer handling.
- Incremented application version to reflect these updates.
- Introduced EvaluateStepPayload class to facilitate evaluation of exercise steps with optional attributes for AI proposals and roadmap details.
- Added SlotContentEntry and SlotExerciseContent classes to manage exercise content within the progression graph planning artifact.
- Updated GraphPlanningRoadmapArtifact to include new slot contents and last findings attributes for improved data handling.
- Enhanced Exercise Progression Graph Panel with links to the new Slot Editor for streamlined editing of progression graphs.
- Incremented application version to reflect these updates.
- Updated `build_gap_fill_goal_text` to include expected skills in the generated text, improving clarity for users.
- Enhanced `_roadmap_gap_snapshot_for_spec` to incorporate skill expectations from the progression stage, enriching the roadmap context.
- Modified `_annotate_roadmap_step` to append skill expectations to the step reasons, providing additional insights.
- Updated tests to verify the inclusion of expected skills in the gap fill goal text.
- Incremented application version to 0.8.215 to reflect these changes.
- Added `stage_learning_goal_override` and `gap_trainer_supplements` parameters to `build_progression_path_gap_planning_context`, allowing for customized learning goals and additional trainer notes.
- Updated `gapOfferContextDisplayLines` to include trainer supplements in the context display.
- Enhanced `ExerciseProgressionPathBuilder` to utilize new parameters for improved gap fill offer handling.
- Incremented application version to 0.8.214 to reflect these changes.
- Added `context_preview` to the `build_gap_fill_offer` function, providing a structured overview of the roadmap snapshot.
- Introduced `gapOfferContextDisplayLines` utility to format context information for UI display, improving clarity for users.
- Updated `ExerciseProgressionPathBuilder` and related components to utilize the new context preview, enhancing the user experience.
- Incremented application version to 0.8.213 to reflect these changes.
- Introduced `build_progression_gap_snapshot` function to create a compact roadmap context for gap exercises, integrating start situation, target state, and stage specifications.
- Updated `build_gap_fill_goal_text` to include roadmap snapshot details, enhancing the context for AI-generated exercises.
- Enhanced `ProgressionPathSuggestRequest` and related components to support new structured inputs for start/target analysis, improving user experience and AI suggestions.
- Incremented application version to 0.8.212 to reflect these changes.
- Added `planning_context` to the `suggestExerciseAi` endpoint, enabling structured planning context for new exercise creation.
- Updated relevant components and backend logic to handle the new planning context, enhancing the AI's exercise suggestion capabilities.
- Incremented application version to 0.8.208 to reflect these changes.
- Introduced a new environment variable `CLUB_FEATURE_ENFORCE` to control club feature access, allowing values of 1, true, or yes for activation.
- Updated the backend logic to check for club feature enforcement, raising HTTP exceptions when access is denied without an active club context.
- Enhanced the admin rights router with a new endpoint to check the enforcement status of club features.
- Incremented application version to 0.8.202 to reflect these changes.
- Updated the Membership RBAC Decisions document to reflect the latest implementation status and roadmap, including new features and enhancements.
- Incremented application version to 0.8.200 and updated database schema version to 20260606083.
- Added a new API endpoint to clear capability grants for club roles, improving admin rights management.
- Enhanced the Admin Rights page in the frontend to display enforcement status and feature consumption details for capabilities.
- Improved the user interface for better clarity on rights and capabilities management.
- Introduced the `consume_club_feature_with_usage` function to standardize feature consumption across endpoints, improving code reusability and clarity.
- Implemented `merge_feature_usage_into_response` to embed feature usage data in API responses, streamlining frontend integration.
- Updated various backend routers to utilize the new consumption logic, ensuring consistent feature usage tracking during AI-related actions.
- Enhanced tests to validate the new consumption and logging behavior.
- Incremented application version to 0.8.199 and updated module version for 'club_features' to 1.6.0 to reflect these changes.
- Replaced the admin club feature exemptions router with a new admin rights router to streamline capability management.
- Added new API endpoints for managing admin rights, including capability grants and quota bypass for portal roles and profiles.
- Updated the frontend to include navigation and lazy loading for the new Admin Rights page.
- Incremented application version to 0.8.197 to reflect these changes and enhancements.
- Incremented application version to 0.8.192 and database schema version to 20260606081.
- Updated club module versions for 'clubs' and 'club_creation_requests' to reflect recent changes.
- Implemented logic to mark approved club creation requests as 'superseded' when the associated club is deleted.
- Refactored frontend components to clear session storage for coach-related keys upon logout and during login checks.
- Enhanced onboarding page to accurately display the status of club creation requests based on their validity.
- Introduced endpoints for managing club creation requests, including fetching, creating, and withdrawing requests.
- Updated the onboarding page to allow users to submit new club creation requests and view their existing requests.
- Enhanced the admin interface with navigation and routing for club creation requests management.
- Incremented version to 0.8.191 to reflect these new features and updates in the application.
- Refactored the logout function in AuthContext to handle asynchronous logout operations, improving session management.
- Updated the FeatureUsageBadge component to display error messages when feature data retrieval fails, enhancing user feedback.
- Replaced lazy loading of OnboardingPage with lazyWithRetry for improved loading reliability.
- Adjusted the EntitlementsContext to determine club ID using utility functions for better governance form handling.
- Revised the status in the Capability Catalog to reflect partial implementation (M3).
- Added a new reference to `MEMBERSHIP_RBAC_DECISIONS_2026-06.md` in both the Capability Catalog and Club Membership documentation.
- Enhanced the Club Membership documentation with details on product decisions and onboarding phases.
- Implemented middleware in the backend to restrict access for unverified users and those pending club membership.
- Updated versioning in `version.py` to reflect changes in account lifecycle management.
- Introduced `email_verified` and `account_state` attributes in the `TenantContext` to improve user state management.
- Updated the `resolve_tenant_context` function to dynamically fetch `email_verified` status from the database and determine `account_state` based on user roles and memberships.
- Implemented `assert_min_account_state` checks across various endpoints to enforce access control based on user account status.
- Incremented version to 1.1.0 in version.py to reflect these enhancements in tenant context management and access control.
- Introduced a new admin user content management endpoint for superadmins, allowing for moderation of user-generated content.
- Updated the backend to include new API functions for retrieving, patching, and deleting user content items.
- Enhanced the frontend with a new Admin User Content page and navigation link for easy access to user content management.
- Updated access layer documentation to reflect the new endpoint and its exempt status.
- Incremented version to 0.8.191 and updated changelog to document these additions in admin functionality.
- Incremented version to 0.8.184, reflecting the implementation of Phase C2 features.
- Added support for displaying variant lists and suggested variant names in exercise suggestions.
- Enhanced the ExercisePickerModal to allow selection of exercise variants and improved handling of variant IDs.
- Updated backend logic to enrich planning hits with variant metadata, ensuring accurate exercise variant selection.
- Documented changes in the changelog to highlight the new capabilities in planning AI functionality.
- Introduced the `exercise_enrichment_admin` API for batch exercise enrichment, allowing superadmins to filter candidates, preview, and apply skills.
- Updated the access layer documentation to include the new endpoint and its exempt status.
- Enhanced the frontend with a new admin page for exercise enrichment and updated navigation to include this feature.
- Incremented version to 0.8.179 and updated changelog to reflect these additions and improvements.
- Updated APP_VERSION to 0.8.166 and modified BUILD_DATE to reflect recent changes.
- Enhanced AI exercise creation process with a new quick create feature, allowing users to generate exercises based on search input.
- Introduced a rich text editor for editing AI-generated drafts, improving user experience in exercise creation.
- Updated ExercisePickerModal and related components to support the new quick create functionality, including error handling and input validation.
- Added new utility functions for parsing search queries and building exercise payloads from drafts.
- Updated the quick create process to include a preview feature for AI-generated exercises, allowing users to review goals, execution, preparation, and trainer notes.
- Introduced new constants for instruction fields and refactored the payload building function to utilize the preview data.
- Improved error handling to ensure at least one of the goal or execution fields is populated.
- Deprecated the previous payload building function in favor of the new preview-based approach, streamlining the exercise creation workflow.
- Updated APP_VERSION to 0.8.164 and added changelog entry for the new version.
- Enhanced ExercisePickerModal to support quick exercise creation using AI, including fields for sketch and focus area.
- Implemented error handling for AI suggestions and improved user prompts for input validation.
- Updated UI elements to reflect changes in exercise creation workflow.
- Added new functionality for exporting and importing matrix editor data in JSON and CSV formats within the MaturityMatrixToolsAdmin component.
- Updated the API utility functions to support matrix editor exports and imports, enhancing the backend communication for Superadmin tasks.
- Refactored the client API to streamline request handling and improve code clarity.
- Included new UI elements for file upload and download actions, improving user experience in managing matrix data.
- Incremented version to 1.1 and updated the status to reflect the implementation of core features including `ai_prompts`, `prompt_resolver`, and the Superadmin HTTP API.
- Documented the current API endpoints for managing AI prompts, including CRUD operations and preview functionality.
- Introduced a new placeholder catalog and preview capabilities for the Superadmin interface.
- Enhanced the backend with new functions for handling AI prompt templates and integrated them into the API.
- Updated frontend components to include navigation and routing for the new Admin AI Prompts page.
- Incremented application version to 0.8.158 and updated changelog to reflect these changes.
- Added documentation for the new Superadmin CRUD endpoints for managing AI Skill Retrieval Profiles (`/api/admin/ai-skill-retrieval-profiles*`).
- Updated the ACCESS_LAYER_ENDPOINT_AUDIT.md to include the new Superadmin API and its exempt status.
- Registered the ai_skill_retrieval_admin router in the backend and updated versioning to reflect the changes.
- Enhanced the frontend with a new Admin page for AI Skill Retrieval, including navigation and API integration for profile management.
- Replaced hardcoded visibility labels with the new constant EXERCISE_VISIBILITY_FIELD_LABEL in multiple components, ensuring consistent terminology throughout the application.
- Updated UI text to reflect the change from "Sichtbarkeit" to "Freigabelevel" in various contexts, enhancing clarity for users.
- Improved accessibility by standardizing the visibility-related labels in the ExercisePickerModal, ExerciseProgressionGraphPanel, and other related components.
- Updated the FrameworkProgramsFilterBlock to include a search input and filter modal, improving user interaction and accessibility.
- Refactored CSS styles for filter components to ensure consistent layout and spacing.
- Removed deprecated panel open state management, streamlining the component logic.
- Integrated new filtering capabilities in the TrainingPlanningFrameworkImportModal and TrainingModulesListPage, enhancing the overall filtering experience.
- Improved the display of active filters and results count, providing clearer feedback to users.
- Introduced new helper functions for managing artifact type corpus, improving code organization and readability.
- Updated the `compute_club_corpus_reference` function to utilize the new corpus handling methods, enhancing clarity and maintainability.
- Refactored skill profile functions to leverage the new corpus structure, ensuring consistent data retrieval across different artifact types.
- Improved the handling of visibility clauses for library content, streamlining database queries for skill profiles.
- Enhanced the batch skill profile summary function to aggregate reference data by artifact type, improving performance and accuracy.
- Modified the `compact_profile_summary` function to allow for dynamic skill and category limits, enhancing flexibility in profile data retrieval.
- Updated frontend components to display skill weights and scores more effectively, improving user interaction with skill metrics.
- Adjusted CSS styles for skill KPI tiles to better differentiate between score and percentage displays, ensuring a clearer visual representation.
- Refactored utility functions to streamline skill summary handling, enhancing overall code maintainability and performance.
- Introduced a new function to calculate club-specific skill percentages, ensuring values are capped at 100%.
- Updated skill profile calculations to include indicators for the best club performance per skill.
- Enhanced frontend components to display club best indicators and improved layout for skill profiles.
- Refactored CSS styles for skill profile components, ensuring a more cohesive and user-friendly interface.
- Updated tests to validate new functionality and ensure accurate representation of skill metrics.
- Updated the skill scoring specification to include club-specific metrics and improved aggregation methods for skill profiles.
- Introduced new API endpoints for batch skill profile summaries, allowing for efficient retrieval of compact skill data.
- Enhanced frontend components to display skill profiles with club comparisons, improving user interaction and visibility of skill strengths.
- Added filtering options for skills in the framework programs, enabling users to refine selections based on training weight relative to club maximums.
- Improved CSS styles for skill profile displays, ensuring a cohesive and user-friendly interface across the application.
