- Introduced `_purge_stage_mismatch_roadmap_slots` to clear slots with persistent stage mismatches, improving the relevance of exercise suggestions.
- Updated `collect_gap_fill_specs` to handle stage mismatch issues more effectively, providing clearer rationale and title hints for off-topic exercises.
- Modified `_filter_learning_goal_candidate_ids` to enforce stricter filtering criteria, ensuring only relevant candidates are considered.
- Enhanced `rematch_roadmap_slots` to incorporate slot assignment history, preventing conflicts with previously assigned exercises.
- Bumped version to 0.8.230 to reflect the new features and improvements.
- Introduced `_peer_stage_learning_goals` to retrieve learning goals from peer stages, enhancing the ability to filter exercises based on cross-slot collisions.
- Added `_filter_learning_goal_candidate_ids` to refine candidate selection by incorporating peer learning goals and stage fit criteria, improving exercise relevance in suggestions.
- Enhanced `pick_best_path_hit` and `_match_roadmap_slot` to utilize peer learning goals for better exercise selection and to prevent conflicts with titles from other stages.
- Updated `stage_refinement_criteria_from_learning_goal` to provide clearer criteria for stage refinement based on learning goals.
- Bumped version to 0.8.229 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.
- Introduced `_step_neighbors_at_index` to safely retrieve neighboring steps without causing IndexErrors, improving robustness in gap fill specifications.
- Updated `collect_gap_fill_specs` to utilize the new neighbor retrieval function, ensuring safe access to adjacent steps during gap fill processing.
- Enhanced rematch logic in `_run_roadmap_rematch_loop` to incorporate `max_rematch_rounds`, allowing for controlled iterations during roadmap rematching.
- Improved handling of unfilled roadmap slots in `collect_rematch_slot_indices`, ensuring accurate identification of gaps in the progression path.
- Added tests to validate the new gap fill handling and rematch logic, ensuring reliability in path suggestion features.
- Introduced multistage path quality assurance (QA) functionality to improve exercise relevance and feedback through structured tiers and optimization hints.
- Updated stage specifications to include `start_state` and `target_state` for better contextualization in roadmap matching.
- Enhanced semantic brief construction with technique sibling exclusions to refine exercise selection based on primary topics.
- Improved path retrieval logic to incorporate new parameters for nuanced matching against learning goals.
- Incremented application version to reflect these updates.
- Introduced `intent_context` and `semantic_brief` parameters in `try_llm_stage_specs` to improve context handling for stage specifications.
- Updated `build_goal_analysis` to extract explicit exclusions from goal queries, enhancing constraint management.
- Enhanced `roadmap_context_from_override` to enrich semantic briefs with path constraints and finalize stage specifications with intent context.
- Incremented application version to reflect these updates.
- Introduced `resolve_path_anti_patterns` to improve handling of path exclusions based on explicit negations and semantic briefs.
- Updated `enrich_brief_with_path_constraints` to incorporate path-specific exclusions into semantic briefs, enhancing exercise relevance.
- Modified roadmap step annotation to allow for anti-pattern overrides, improving flexibility in exercise selection.
- Enhanced tests to validate new path exclusion features and ensure correct functionality against learning goals.
- Incremented application version to reflect these updates.
- Introduced `build_stage_match_brief` to create stage-specific semantic briefs, improving roadmap matching accuracy.
- Updated path retrieval logic to differentiate between general and stage-specific semantic weights, enhancing exercise relevance.
- Added support for anti-patterns and success criteria in stage matching, allowing for more nuanced exercise selection.
- Enhanced tests to validate new stage matching features and ensure correct functionality against learning goals.
- Incremented application version to reflect these updates.
- Added `semantic_brief_for_stage` function to enhance semantic briefs with stage learning goals for improved roadmap matching.
- Introduced `exercise_passes_stage_learning_goal_gate` to validate exercises against stage learning goals, enhancing relevance checks.
- Updated path retrieval and scoring logic to incorporate stage learning goals, allowing for more nuanced exercise selection.
- Enhanced UI to indicate weak matches with stage learning goals, improving user feedback on exercise relevance.
- Incremented application version to reflect these updates.
- Updated the `CLAUDE.md` to reflect changes in the Progression Graph, including the new Ist-Stand and roadmap specifications.
- Enhanced `PLANNING_EXERCISE_SUGGEST_CONTEXT.md` with detailed descriptions of the current state and features of the planning exercise.
- Revised `PLANNING_PROGRESSION_ROADMAP_SPEC.md` to document the implementation status of various phases and their corresponding migrations.
- Incremented application version to 0.8.217 to incorporate recent updates and improvements in the planning context and roadmap functionalities.
- Incremented application version to 0.8.217 to reflect recent changes.
- Added support for a planning roadmap in the Exercise Progression Path Builder, allowing users to save and load structured planning artifacts.
- Enhanced the persistence logic for the planning roadmap, ensuring updates are correctly handled during graph modifications.
- Improved the user interface to display saved planning hints, enriching the user experience and interaction with the progression graphs.
- Incremented application version to 0.8.216 to reflect recent changes.
- Added skill expectations handling in the Exercise Progression Path Builder, improving the integration of expected skills into the roadmap steps.
- Enhanced the mapping of major steps to include load profiles, success criteria, anti-patterns, and exercise types, enriching the user experience and functionality.
- 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 `include_llm_start_target` option to `ProgressionPathSuggestRequest` for improved roadmap suggestions.
- Introduced new classes `StartTargetExtractArtifact` and `StartTargetResolveMeta` to handle LLM extraction results and metadata.
- Implemented `try_llm_start_target_extract` function to extract start and target states from goal queries using LLM.
- Updated `resolve_roadmap_structured_input` to prioritize user inputs, LLM extractions, and regex parsing for start/target resolution.
- Enhanced `ExerciseProgressionPathBuilder` to utilize new structured inputs and display extraction sources.
- Incremented application version to 0.8.211 to reflect these changes.
- Introduced `RoadmapStructuredInput` to encapsulate structured inputs for start situation, target state, and roadmap notes.
- Updated `ProgressionPathSuggestRequest` to include new fields for structured roadmap inputs.
- Implemented parsing logic for goal queries to extract start and target states, enhancing the goal analysis process.
- Enhanced `build_goal_analysis` to utilize structured inputs, improving the clarity and relevance of generated goals.
- Updated the `ExerciseProgressionPathBuilder` component to support new structured input fields, enhancing user experience.
- Incremented application version to 0.8.210 to reflect these changes.
- Introduced `roadmap_qa_mode` to manage QA behavior based on roadmap-first logic, improving gap detection between major steps.
- Updated `detect_path_gaps` to skip gaps for roadmap-planned neighbor pairs, enhancing the accuracy of path assessments.
- Added new helper function `is_roadmap_planned_neighbor_pair` to facilitate roadmap neighbor checks.
- Updated relevant tests to validate new functionality and ensure robustness.
- Incremented application version to 0.8.209 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.
- Added support for editable major steps in the roadmap, allowing users to modify phase, learning goals, and order before exercise matching.
- Introduced a new `roadmap_override` feature to facilitate customized retrieval without re-invoking the roadmap AI.
- Updated the `ExerciseProgressionPathBuilder` component to incorporate these new features, enhancing user interaction and flexibility.
- Incremented application version to 0.8.207 to reflect these changes.
- Introduced a roadmap-first approach for retrieval, allowing for structured exercise suggestions based on stage specifications and major steps.
- Added functionality to generate gap-fill offers for unfilled roadmap stages, improving the relevance of exercise recommendations.
- Updated the `ExerciseProgressionPathBuilder` to support the new roadmap-first feature, enhancing user experience with clearer exercise paths.
- Incremented application version to 0.8.206 and updated the database schema version to reflect these changes.
- Introduced a roadmap-first approach for the planning AI, allowing for a structured progression graph that aligns with the overall project roadmap.
- Added new functionality to strip off-topic steps from exercise paths, improving the relevance of generated exercise suggestions.
- Implemented a detailed goal text generation for AI proposals, enhancing the context provided for new exercises.
- Updated the ExerciseProgressionPathBuilder component to support new features, including roadmap previews and improved focus area handling.
- Incremented application version to 0.8.205 and updated database schema version to 20260606086 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 capability catalog to reflect a registry-first approach, requiring modules to register rights and quotas upon implementation.
- Enhanced the backend to synchronize the rights registry with the database, ensuring only registered capabilities and features are displayed in the admin matrix.
- Modified SQL queries in the admin rights router to filter capabilities and features based on module registration.
- Updated documentation to clarify the new rights and features registry process, replacing the previous catalog-first method.
- Incremented application version to 0.8.201 and updated database schema version to 20260606084 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.
- Added support for club feature quota bypass based on portal roles and profile grants in the capabilities check.
- Introduced new functions to handle quota bypass logic in club feature access and consumption.
- Updated the FeatureUsageBadge component to reflect platform exemptions for features.
- Incremented application version to 0.8.195 and database schema version to 20260606083 to reflect these changes.
- Enhanced backend routers to include new logic for consuming club features during AI-related actions.
- 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.
- 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 `probe_club_feature_access` to check club feature limits and log access attempts without blocking by default.
- Added `_live_inventory_count` function to retrieve current counts for specific features, enhancing feature limit management.
- Updated various endpoints to utilize the new probing functionality, ensuring compliance with club feature access rules.
- Incremented version to 1.1.0 in version.py to reflect these enhancements in club feature management.
- Enhanced the ACCESS_LAYER_AND_GOVERNANCE_PLAN.md with new specifications for capability documentation and community features.
- Added references to new documents detailing capability IDs and club membership features.
- Updated MULTI_TENANCY_RBAC_ARCHITECTURE.md to include links to the new specifications.
- Marked certain features as deprecated in backend/auth.py, indicating migration paths for club feature access.
- Incremented DB_SCHEMA_VERSION to 20260606078 in version.py to reflect recent changes.
- Updated the AI gap filling logic to include structured offers for unfilled gaps, improving the user experience in the Exercise Progression Path Builder.
- Introduced new functions for detecting off-topic steps and parsing LLM-suggested exercises, enhancing the contextual relevance of exercise suggestions.
- Enhanced the frontend components to support new AI proposal features, including quick creation modals for newly suggested exercises.
- Incremented version to 0.8.190 and updated changelog to reflect these improvements in planning AI functionality.
- Replaced the manual path selection logic with a new `pick_best_path_hit` function to streamline the process of selecting the best exercise based on semantic scores and gating criteria.
- Updated the semantic gating logic to apply a soft penalty for off-topic exercises, improving the flexibility of exercise selection.
- Enhanced the handling of title, summary, and goal parameters in semantic checks to ensure more accurate relevance assessments.
- Incremented version to 0.8.189 and updated changelog to reflect these improvements in planning AI functionality.
- Updated the path selection logic to incorporate semantic gating, ensuring only relevant exercises are considered based on semantic scores.
- Introduced new functions for building path target profiles and resolving semantic skill weights, enhancing the contextual understanding of exercise suggestions.
- Improved the retrieval process by applying dynamic retrieval weights based on semantic strength, refining the accuracy of exercise recommendations.
- Incremented version to 0.8.188 and updated changelog to document these enhancements in planning AI functionality.
- Introduced path reordering functionality using LLM with `ordered_step_indices`, allowing for dynamic adjustment of exercise progression paths.
- Added AI gap filling capabilities, enabling the system to propose new exercises when unbridgeable gaps are detected.
- Updated the backend to support new request parameters for path reordering and AI gap filling.
- Enhanced frontend components to reflect these new features, including alerts for AI proposals and adjustments in exercise display.
- Incremented version to 0.8.187 and updated changelog to document these significant enhancements in planning AI functionality.
- Introduced new functions to load exercise goals and variant names in chunks, improving data retrieval efficiency.
- Integrated semantic scoring into the ranking logic, allowing for more nuanced exercise suggestions based on semantic relevance.
- Updated the planning exercise suggestion process to include semantic brief handling, enriching the context for exercise recommendations.
- Adjusted the retrieval phase to incorporate dynamic retrieval weights based on semantic strength, enhancing the overall suggestion accuracy.
- Incremented version to 0.8.186 and updated changelog to reflect these significant enhancements in planning AI functionality.
- Incremented version to 0.8.185, reflecting the implementation of Phase C3 features.
- Introduced the `POST /api/planning/progression-path-suggest` endpoint for generating exercise progression paths.
- Enhanced the ExerciseProgressionGraphPanel with a new ExerciseProgressionPathBuilder for reviewing and saving paths.
- Updated changelog to document the new capabilities in planning AI 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.
- Incremented version to 0.8.183, reflecting the implementation of Phase C1 enhancements.
- Added support for progression graph auto-matching and variant-aware successors in exercise suggestions.
- Updated request and response structures to include `anchor_exercise_variant_id`, `progression_graph_name`, and `suggested_variant_id`.
- Enhanced frontend components to integrate planning AI search capabilities, including a new modal for exercise creation and improved context display in the exercise list.
- Updated changelog to document these significant improvements in planning AI functionality.
- Introduced a new function `hybrid_ranking_ambiguous` to determine when to rerank candidates based on score proximity, improving the decision-making process for exercise suggestions.
- Updated `should_run_llm_rank_pipeline` to incorporate the new ranking logic and handle scenarios with ambiguous rankings more effectively.
- Adjusted the frontend to always include LLM ranking in requests, ensuring consistent behavior across different query lengths.
- Incremented version to 0.8.182 and updated changelog to reflect these enhancements in planning AI capabilities.
- Added support for section guidance notes and titles in the planning target profile, enabling richer context for exercise suggestions.
- Introduced deterministic text-to-catalog signal mapping, allowing for improved integration of planning text signals into the exercise retrieval process.
- Implemented a partner-related filter in exercise retrieval, enhancing the relevance of suggested exercises based on user intent.
- Updated the retrieval phase to account for text signals, improving the accuracy of exercise recommendations.
- Incremented version to 0.8.181 and updated changelog to reflect these significant enhancements in planning AI capabilities.
- Implemented a maximum of 3 exercises per preview request to prevent Gateway-504 errors, improving the stability of the exercise enrichment process.
- Adjusted batch sizes for applying exercises and previewing to optimize performance and resource management.
- Updated the frontend to reflect changes in preview handling, including user notifications about chunk sizes and potential timeouts.
- Incremented version to 0.8.180 and updated changelog to document these enhancements and fixes.
- 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 the planning exercise retrieval process to implement a multistage approach, ranking the entire visible library deterministically against the expectation profile.
- Removed the previous profile OR pool mechanism, simplifying the retrieval logic and ensuring full-text search is only used as a scoring signal.
- Adjusted the `compose_retrieval_phase` function to accommodate the new full library ranking strategy.
- Incremented version to 0.8.177 and updated changelog to reflect these changes in planning exercise capabilities.
- Added support for the new planning exercise expectation profile slug in the AI prompt runtime.
- Refactored SQL parameter handling in the planning exercise retrieval process to ensure correct binding for full-text search.
- Updated the planning exercise suggestion logic to incorporate LLM expectation handling, improving the accuracy of exercise recommendations.
- Introduced new functions to determine when to run the LLM expectation pipeline, enhancing the decision-making process for exercise suggestions.
- Incremented version to 0.8.176 and updated changelog to reflect these enhancements in planning AI capabilities.
