Scrum Master

What This Skill Does

This skill provides data-driven Scrum Master capabilities, combining sprint analytics, probabilistic forecasting, and team development coaching. It's designed for Scrum Masters and agile teams looking to improve their sprint performance and team dynamics through data analysis.

When to Use

• Analyze sprint velocity trends.
• Score team health across key dimensions.
• Forecast sprint completion probabilities.
• Identify and address sprint anomalies.
• Improve team commitment reliability.
• Optimize sprint scope stability.

Key Features

• Analyzes sprint velocity using rolling averages and regression.
• Scores team health across six weighted dimensions.
• Performs Monte Carlo simulations for sprint forecasting.
• Provides anomaly detection with root-cause suggestions.
• Generates text and J.

SON reports for analysis. Offers guidance on improving sprint execution.

Manual Installation

Manual installation

View Full Skill Content

The complete markdown content that gets installed

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/velocity_analyzer.py)

Runs rolling averages, linear-regression trend detection, and Monte Carlo simulation over sprint history.

Text report

• python velocity_analyzer.py sprint_data.json --format text.

J

SON output for downstream processing

• python velocity_analyzer.py sprint_data.json --format json > analysis.json.

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/sprint_health_scorer.py)

Scores team health across 6 weighted dimensions, producing an overall 0–100 grade.

• python sprint_health_scorer.py sprint_data.json --format text.

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/retrospective_analyzer.py)

Tracks action-item completion, recurring themes, sentiment trends, and team maturity progression.

• python retrospective_analyzer.py sprint_data.json --format text.

Outputs: action-item completion rate by priority/owner, recurring-theme persistence scores, team maturity level (forming/storming/norming/performing), improvement-velocity trend.

Validation: Requires 3+ retrospectives with action-item tracking. With fewer, note the limitation and offer partial theme analysis only.

Input Requirements

All scripts accept J

SON following the schema in assets/sample_sprint_data.json:

{ "team_info": { "name": "string", "size": "number", "scrum_master": "string" }, "sprints": [ { "sprint_number": "number", "planned_points": "number", "completed_points": "number", "stories": [...], "blockers": [...], "ceremonies": {...} } ], "retrospectives": [ { "sprint_number": "number", "went_well": ["string"], "to_improve": ["string"], "action_items": [...] } ] }

• Jira and similar tools can export sprint data; map exported fields to this schema before running the scripts. See assets/sample_sprint_data.json for a complete 6-sprint example and assets/expected_output.json for corresponding expected results (velocity avg 20.2 pts, CV 12.7%, health score 78.3/100, action-item completion 46.7%).

Sprint Execution Workflows

Sprint Planning

• Run velocity analysis: python velocity_analyzer.py sprint_data.json --format text. Use the 70% confidence interval as the recommended commitment ceiling for the sprint backlog. Review the health scorer's Commitment Reliability and Scope Stability scores to calibrate negotiation with the Product Owner. If Monte Carlo output shows high volatility (CV >20%), surface this to stakeholders with range estimates rather than single-point forecasts. Document capacity assumptions (leave, dependencies) for retrospective comparison.

Daily Standup

Track participation and help-seeking patterns — feed ceremony data into sprint_health_scorer.py at sprint end. Log each blocker with date opened; resolution time feeds the Blocker Resolution dimension. If a blocker is unresolved after 2 days, escalate proactively and note in sprint data.

Sprint Review

Present velocity trend and health score alongside the demo to give stakeholders delivery context. Capture scope-change requests raised during review; record as scope-change events in sprint data for next scoring cycle.

Sprint Retrospective

Run all three scripts before the session:

• python sprint_health_scorer.py sprint_data.json --format text > health.txt.
• python retrospective_analyzer.py sprint_data.json --format text > retro.txt. Open with the health score and top-flagged dimensions to focus discussion. Use the retrospective analyzer's action-item completion rate to determine how many new action items the team can realistically absorb (target: ≤3 if completion rate <60%). Assign each action item an owner and measurable success criterion before closing the session. Record new action items in sprint_data.json for tracking in the next cycle.

Team Development Workflow

Assessment

• python sprint_health_scorer.py team_data.json > health_assessment.txt.
• python retrospective_analyzer.py team_data.json > retro_insights.txt.

Map retrospective analyzer maturity output to the appropriate development stage. Supplement with an anonymous psychological safety pulse survey (Edmondson 7-point scale) and individual 1:1 observations. If maturity output is forming or storming, prioritise safety and conflict-facilitation interventions before process optimisation.

Intervention

Apply stage-specific facilitation (details in references/team-dynamics-framework.md):

Progress Measurement

Sprint cadence: re-run health scorer; target overall score improvement of ≥5 points per quarter.

• Monthly: psychological safety pulse survey; target >4.0/5.0. Quarterly: full maturity re-assessment via retrospective analyzer. If scores plateau or regress for 2 consecutive sprints, escalate intervention strategy (see references/team-dynamics-framework.md).

Key Metrics & Targets