What problem does it solve? Teams often finish delivery increments without systematically capturing what worked, what failed, and how accurate their estimates were, losing valuable learning for continuous improvement. ## Core Features & Use Cases - Cycle Calibration Recording: Logs predicted versus actual ICE scores, effort estimates, and risk assessments to track estimation accuracy over time. - Root Cause Analysis: Applies fishbone diagrams, 5 Whys, and Ohno's 7 wastes framework to diagnose significant problems systemically. - Bias Mitigation: Includes counter-argument checks and hindsight bias detection to prevent post-hoc rationalization in retrospective conclusions. - Use Case: After shipping a feature, run this skill to record the cycle outcome, analyze what went well and poorly using blameless post-mortem format, and update corrections and patterns for future work. ## Quick Start Run a retrospective for the delivery cycle we just completed and capture the key learnings and calibration data.