- 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.
- Transitioned the project to a Python-first architecture, moving feature logic from Bash to Python.
- Created a new `setup.sh` script to bootstrap the environment on `nik-gpu`, ensuring necessary tools are installed and setting up a Python virtual environment.
- Updated project layout in `plan.md` to reflect the new structure and clarify the purpose of each component.
- Established a reference mapping table for feature implementation based on the existing reference project.
- Outlined a migration phase to transition existing Bash functionality to Python tasks.
- Created manifest.json to track file metadata for the project.
- Added plan.md detailing the implementation strategy for the ba-auto-daily automation script, including feature prioritization and prerequisites.
