Implement Puzzle Difficulty Auto-Calibration

[Mkalbani]

Description

Build an analytics-driven difficulty calibration system that adjusts a puzzle's effective difficulty rating based on real player performance data. A puzzle tagged as "Easy" that 80% of players abandon should be reclassified. The system runs as a scheduled background job, analyses session outcomes, and proposes or automatically applies difficulty adjustments.

Acceptance Criteria

• A scheduled job runs daily and evaluates all published puzzles
• Calibration factors include: solve rate, average time, average hint usage, and abandon rate
• A DifficultyCalibration entity logs the computed score and recommended difficulty per puzzle
• If the recommended difficulty diverges from the tagged difficulty, a notification is sent to admins
• GET /admin/calibration returns all puzzles with their current vs recommended difficulty
• PATCH /admin/calibration/:puzzleId/apply applies the recommendation
• Auto-apply mode can be enabled via config to skip manual approval
• Unit tests cover calibration scoring formula and threshold comparisons

[Songu3020]

Hi! I’m a full-stack developer with a strong blockchain background. I’d love to handle this issue and can deliver a clean, reliable solution. Kindly assign it to me.

[grantfox-oss]

🦊 GrantFox — @Songu3020 has been assigned to this issue as part of the Official Campaign | FWC26 campaign!

Next steps:

1. Open a Pull Request referencing this issue (e.g., Closes #71)
2. Your PR will be reviewed by the MindFlowInteractive maintainers

Good luck! Track your progress on GrantFox.