Agent skill · operations · alirezarezvani
scrum-master
Use for sprint and team analytics: velocity forecasts, sprint health, retrospectives, burndown or blockers. Forecasts with velocity_analyzer.py (Monte Carlo), scores with sprint_health_scorer.py and tracks retro actions with retrospective_analyzer.py.
Why this skill is useful
Adds 3 executable Python scripts for sprint analysis that provide unique insights and forecasting capabilities not available in standard agile practices.
What it needs
Requires python installed locally. About 5k tokens when loaded. Last updated 2026-08-05. 23,939 stars on the source repository.
What this skill does
Scrum Master Expert Data-driven Scrum Master skill combining sprint analytics, probabilistic forecasting, and team development coaching. The unique value is in the three Python analysis scripts and their workflows — refer to references/ and assets/ for deeper framework detail. --- Table of Contents Analysis Tools & Usage Input Requirements Sprint Execution Workflows Team Development Workflow Key Metrics & Targets Limitations --- Analysis Tools & Usage 1. Velocity Analyzer (scripts/velocityanalyzer.py) Runs rolling averages, linear-regression trend detection, and Monte Carlo simulation over sprint history. Outputs: velocity trend (improving/stable/declining), coefficient of variation, 6-sprint Monte Carlo forecast at 50 / 70 / 85 / 95% confidence intervals, anomaly flags with root-cause suggestions. Validation: If fewer than 3 sprints are present in the input, stop and prompt the user: "Velocity analysis needs at least 3 sprints. Please provide additional sprint data." 6+ sprints are recommended for statistically significant Monte Carlo results. --- 2. Sprint Health Scorer (scripts/sprinthealthscorer.py) Scores team health across 6 weighted dimensions, producing an overall 0–100 grade. Dimension Weight Target --- --- --- Commitment Reliability 25% >85% sprint goals met Scope Stability 20% <15% mid-sprint changes Blocker Resolution 15% <3 days average Ceremony Engagement 15% >90% participation Story Completion Distribution 15% High ratio of fully done stories Velocity Predictability 10% CV <20% Outputs: overall health score + grade, per-dimension scores with recommendations, sprint-over-sprint trend, intervention priority matrix. Validation: Requires 2+ sprints with ceremony and story-completion data. If data is missing, report which dimensions cannot be scored and ask the user to supply the gaps. --- 3. Retrospective Analyzer (scripts/retrospectiveanalyzer.py) Tracks action-item completion, recurring themes, sentiment trends, and team maturity progression. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills alirezarezvani/scrum-master--9fad10