Scrum Master

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.

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Table of Contents

• [Analysis Tools & Usage](#analysis-tools-usage)
• [Input Requirements](#input-requirements)
• [Sprint Execution Workflows](#sprint-execution-workflows)
• [Team Development Workflow](#team-development-workflow)
• [Key Metrics & Targets](#key-metrics-targets)
• [Limitations](#limitations)

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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

# JSON 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.

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2. Sprint Health Scorer (scripts/sprint_health_scorer.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% | 90% participation | | Story Completion Distribution | 15% | High ratio of fully done stories | | Velocity Predictability | 10% | CV 20%), surface this to stakeholders with range estimates rather than single-point forecasts.

1. Document capacity assumptions (leave, dependencies) for retrospective comparison.

Daily Standup

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

Sprint Review

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

Sprint Retrospective

1. Run all three scripts before the session:

``bash python sprint_health_scorer.py sprint_data.json --format text > health.txt python retrospective_analyzer.py sprint_data.json --format text > retro.txt ``

1. Open with the health score and top-flagged dimensions to focus discussion.
2. 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 health_assessment.txt

python retrospectiveanalyzer.py teamdata.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`):

| Stage | Focus |
|---|---|
| Forming | Structure, process education, trust building |
| Storming | Conflict facilitation, psychological safety maintenance |
| Norming | Autonomy building, process ownership transfer |
| Performing | Challenge introduction, innovation support |

### 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`).

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## Key Metrics & Targets

| Metric | Target |
|---|---|
| Overall Health Score | >80/100 |
| Psychological Safety Index | >4.0/5.0 |
| Velocity CV (predictability) | 85% |
| Scope Stability | 90% |
| Retrospective Action Completion | >70% |

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## Limitations

- **Sample size**: fewer than 6 sprints reduces Monte Carlo confidence; always state confidence intervals, not point estimates.
- **Data completeness**: missing ceremony or story-completion fields suppress affected scoring dimensions — report gaps explicitly.
- **Context sensitivity**: script recommendations must be interpreted alongside organisational and team context not captured in JSON data.
- **Quantitative bias**: metrics do not replace qualitative observation; combine scores with direct team interaction.
- **Team size**: techniques are optimised for 5–9 member teams; larger groups may require adaptation.
- **External factors**: cross-team dependencies and organisational constraints are not fully modelled by single-team metrics.

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## Related Skills

- **Agile Product Owner** (`product-team/agile-product-owner/`) — User stories and backlog feed sprint planning
- **Senior PM** (`project-management/senior-pm/`) — Portfolio health context informs sprint priorities

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*For deep framework references see `references/velocity-forecasting-guide.md` and `references/team-dynamics-framework.md`. For template assets see `assets/sprint_report_template.md` and `assets/team_health_check_template.md`.*

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [mrDesign-ww](https://github.com/mrDesign-ww)
- **Source:** [mrDesign-ww/vault-os](https://github.com/mrDesign-ww/vault-os)
- **License:** MIT

Install and usage instructions live in the source repository linked above.