Manages and corrects Claude Code's learning evaluations to improve system accuracy and autonomy scores.
The ALM Review skill serves as the primary feedback mechanism for the Claude Code Learning Memory System, allowing users to audit and refine how the model learns from interactions. It provides a structured way to view recent task evaluations, identify misclassified corrections, and override outcome statuses. By providing this human-in-the-loop validation, users can directly influence the confidence scores and autonomy levels of the AI, ensuring that the system's internal memory remains accurate and that Claude evolves correctly based on real-world usage.
Key Features
01Manually update task classifications and success outcomes
020 GitHub stars
03Override false positive or false negative correction detections
04View detailed logs of recent task evaluations and session IDs
05Recalculate task-specific confidence and autonomy scores in real-time
06Directly edit local JSONL evaluation records via terminal automation
Use Cases
01Adjusting task types for better categorization of future automated workflows
02Correcting a misidentified 'failure' outcome to prevent unnecessary confidence score drops
03Auditing recent interactions to ensure the ALM system is capturing learning moments correctly
