Session Feedback Analyzer

Mines Claude Code session JSONL for implicit user feedback. When a user corrects, redoes, reverts, or partially accepts AI output after a skill invocation, that signals a skill gap. Outputs structured feedback.jsonl with per-event dimension attribution for the improvement pipeline.

When to Use

• Compute per-skill correction rates to find which skills users correct most often.
• Generate feedback.jsonl as input for improvement-generator's candidate prioritization.
• Track correction trends over time (30-day rolling windows) to detect skill quality regression.
• Identify hotspot dimensions (accuracy vs coverage vs trigger_quality vs efficiency) per skill.
• Compare correction_rate before and after an improvement to validate whether a change actually helped.
• Audit a single skill's feedback history with --skill-filter to understand why users reject its output.
• Feed dimension hotspots into improvement-generator so candidates target the dimensions users care about.
• Bootstrap the auto-improvement loop: analyzer output is the starting signal that tells the pipeline which skills need work.
• Investigate spikes in correction_rate after a skill update to decide whether to rollback.

When NOT to Use