- Updated arena.py to allow multiple battles per invocation, looping until tickets are exhausted or a rank-1 condition is met. Introduced _fight_one function for single battle logic and added cooldown handling between fights.
- Enhanced lesson.py to implement a tiered priority system for scheduling lessons based on student slots available, replacing the previous highest affection value selection. Introduced functions for scanning all regions and building a priority queue for lesson scheduling.
- Centralized return-to-home logic in ba_daily.py to ensure the game returns to the home screen before and after each task, improving robustness against navigation issues.
- Added retry mechanism for returning to home, allowing for transient navigation issues to be handled gracefully without aborting tasks.
- Created ba_auto/tasks/bounty.py to handle the Bounty task, simplifying the original reference's per-call loop to a single area sweep based on date-ordinal-modulo rotation.
- Live-calibrated against nik-gpu on 2026-07-11, ensuring zero real tickets were spent during testing.
- Adjusted entry point to directly access the Work-hub card for Bounty, eliminating unnecessary navigation steps.
- Implemented logic to confirm the latest stage available in each area, ensuring all stages are SSS-cleared.
- Fixed bugs related to ticket count handling and result button detection, preventing unintended real-currency prompts.
- Updated ba_daily.py to include the new bounty task in the task flow.
- Documented the implementation and testing process in plan.md, detailing live tests and fixes applied.
- Updated `mapping.md` to reflect completed migration and testing status for event sweep and arena tasks.
- Improved `arena.py` to ensure a return to home screen before opening tactical challenges, preventing navigation errors.
- Enhanced `event_sweep.py` with robust handling for badge carousel navigation, including direct pagination dot clicks and extended wait times for cold-start scenarios.
- Implemented Japanese OCR support for finished event detection in `event_sweep.py`, adding a definitive check to avoid false positives on stale event pages.
- Adjusted retry logic and timeouts in `event_sweep.py` to accommodate longer loading times and ensure accurate stage row detection.
- Updated `setup.sh` to check for the presence of the Japanese OCR language pack, providing installation instructions if missing.
- Documented live testing results and fixes in `plan.md`, confirming successful sweeps and addressing previously reported bugs.
- Added `find_template` and `template_visible` functions for template matching in screenshots.
- Introduced `read_int_white_on_dark` for OCR of bright text on dark backgrounds.
- Ported arena functionality from reference, including ticket management, opponent selection, and reward collection.
- Implemented safety checks for modal visibility and result confirmation to prevent unintended ticket spends.
- Live-tested the arena task, confirming functionality across multiple tickets with real fight outcomes.
- Updated mapping documentation to reflect new arena task implementation and its unique navigation requirements.
- Added `lesson.py` to handle the scheduling of lessons based on affection values.
- Integrated OCR functionality to read affection counts from heart badges.
- Updated `README.md` to include the new lesson task in the task status section.
- Enhanced `config.py` with necessary configurations for the lesson task.
- Modified `detector.py` to include a new function for reading heart badge values accurately.
- Updated `mapping.md` to reflect the new lesson task implementation.
- Adjusted `ba_daily.py` to include the lesson task in the command dispatch.
- Updated `plan.md` to document the completion of the lesson task and its testing outcomes.
- Modified `setup.sh` to include the lesson task in the run command instructions.
- Added `wait_for_state` function in `navigation.py` for state monitoring and reaction handling.
- Updated `mapping.md` to reflect changes in story sweep implementation and OCR usage.
- Refactored `story_sweep.py` to utilize OCR for region and stage identification, replacing random selection with configured targets.
- Enhanced modal handling and confirmation checks for AP usage in `story_sweep.py`.
- Updated setup script to require `tesseract` for OCR functionality and included installation instructions.
- Revised `plan.md` to document the transition from heuristic to OCR-based stage targeting and the associated findings from live testing.
- Introduced `story_sweep.py` for Normal/Hard story AP sweeping, allowing users to spend AP on randomly selected stages.
- Updated `CLAUDE.md` to clarify the OCR policy, emphasizing the need to port OCR-driven logic from the reference implementation rather than substituting with non-OCR methods.
- Modified `README.md` and `plan.md` to reflect the new story sweep feature and its operational details.
- Adjusted `setup.sh` to include the new command for running the story sweep.
- Enhanced `driver.py` with a new scroll function for better interaction with the game UI.
- Updated configuration mappings in `config.py` to support the new story sweep functionality.
- Refined existing task modules to ensure consistent state verification and error handling.
- Changed directory labels from ".scratchpad/" to "scratchpad/" in graph.json and related documentation for consistency.
- Updated plan.md to reflect the current status of the Stamina/AP task migration, including details on the mission claim process and identified bugs.
- Modified setup.sh to include 'stamina' in the run command options for the daily script.
- Changed references from './scratchpad' to '.scratchpad/' in graph.json and plan.md for consistency.
- Expanded Phase 6 follow-up section in plan.md to clarify changes made to the pat detection logic:
- Updated `find_cafe_sparkle()` to utilize multiple template scales for improved detection.
- Modified `_pat_room` to allow polling for maximum clicks instead of breaking on the first miss.
- Added mouse movement after each pat to prevent cursor occlusion of sparkles.
- Verified that room entry and modal state checks function correctly, but end-to-end pat success remains untested due to lack of available interactions.
