Manage — Advanced Scrum Master skill for data-driven agile team analysis and coaching. Use when the user asks about sprint planning, velocity tracking, retrospectives, standup facilitation,
Manage — Advanced Scrum Master skill for data-driven agile team analysis and coaching. Use when the user asks about sprint planning, velocity tracking, retrospectives, standup facilitation,
[{"name":"code_or_task","type":"string","description":"Code snippet, script, or task description to process","required":true},{"name":"context","type":"string","description":"Additional context or background information","required":false}]
output_schema
[{"name":"report","type":"string","description":"Analysis report or summary from 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.
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.
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%
<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%
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.
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.
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
Metric
Target
Overall Health Score
>80/100
Psychological Safety Index
>4.0/5.0
Velocity CV (predictability)
<20%
Commitment Reliability
>85%
Scope Stability
<15% mid-sprint changes
Blocker Resolution Time
<3 days
Ceremony Engagement
>90%
Retrospective Action Completion
>70%
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.
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
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.
Why This Skill Exists
Manage — Advanced Scrum Master skill for data-driven agile team analysis and coaching.
When to Use
Use this skill when the user asks about sprint planning, velocity tracking, retrospectives, standup facilitation,
What If Fails
If this skill fails to produce the expected output: (1) verify input completeness, (2) retry with more specific context, (3) fall back to the parent workflow without this skill.
