Analyze sprint data to score team health and forecast velocity.
npx skills add https://github.com/amanhsn/flyerbuild --skill scrum-master-amanhsn
Please help me install this Agent Skill. Skill: scrum-master Source: https://github.com/amanhsn/flyerbuild/tree/main/.cursor/skills/scrum-master Command: npx skills add https://github.com/amanhsn/flyerbuild --skill scrum-master-amanhsn
This Skill addresses the challenge of understanding and improving team performance, velocity predictability, and overall agile process health by providing data-driven analysis and actionable insights.
Analyze the sprint data provided in 'sprint_data.json' to generate a comprehensive health report.
High-intent search queries and answers about installing and using this skill.
You can analyze unpredictable team velocity by feeding your historical sprint data into automated sprint analytics scripts. This process evaluates velocity trends and scores team health across multiple dimensions, identifying underlying patterns causing the unpredictability to help improve future sprint planning and execution.
Monte Carlo forecasting is a statistical technique used for agile velocity forecasting. By running deterministic Python scripts against past sprint performance data, it simulates numerous future outcomes to predict upcoming sprint velocities with a quantifiable degree of confidence for your project management.
You assess psychological safety and team maturity by applying structured retrospective intelligence frameworks to your team feedback data. This extracts continuous improvement patterns and evaluates team health to foster and sustain high-performing agile environments.
Yes, you can use Python scripts like retrospective_analyzer.py and sprint_health_scorer.py for retrospective analysis and sprint health scoring. These scripts execute deterministic tasks using pandas and scikit-learn to process sprint data and generate comprehensive team health reports.
Yes, you need pandas and scikit-learn along with numpy and scipy to perform data-driven sprint analytics. These dependencies are required to run the Python scripts that execute deterministic task calculations for velocity analysis, health scoring, and retrospective intelligence.
The best way to extract continuous improvement patterns from retrospective feedback is through automated retrospective intelligence analysis. Processing team feedback data with dedicated Python scripts identifies recurring themes and psychological safety insights to drive actionable team development.
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