Analyze and coach agile teams

Analyze sprint velocity, team health, and retrospective themes with Monte Carlo forecasting

When forecasting sprint capacity with confidence intervals or diagnosing team health across 6 weighted dimensions.

• · sprint JSON exports (planned/completed points, blockers, ceremonies)
• · 3+ sprints for velocity analysis

• · velocity trend + 50/70/85/95% confidence intervals
• · health score (0-100)
• · action-item completion rate

• · Python 3.6+
• · velocity_analyzer.py
• · sprint_health_scorer.py
• · retrospective_analyzer.py

6+ sprints ideal (3 min); ceremony data; story completion tracking

• · Insufficient sprint history
• · missing ceremony/blocker data
• · high volatility (CV >20%)

• · Monte Carlo simulation
• · 6-dimension health matrix
• · MIT license
• · Alireza Rezvani author