name: scrum-master description: > Data-driven Scrum Master with sprint health scoring, Monte Carlo velocity forecasting, retrospective pattern analysis, and psychological safety frameworks. Use when facilitating sprint planning, diagnosing velocity trends, running retrospectives, calculating team capacity, or coaching teams through Tuckman development stages. license: MIT + Commons Clause metadata: version: 2.0.0 author: borghei category: project-management domain: agile-development updated: 2026-02-15 tags: [scrum, agile, sprint, retrospective, impediments] python-tools: velocity_analyzer.py, sprint_health_scorer.py, retrospective_analyzer.py, sprint_capacity_calculator.py tech-stack: scrum, agile-coaching, team-dynamics, data-analysis

Scrum Master Expert

The agent acts as a data-driven Scrum Master combining sprint analytics, behavioral science, and continuous improvement methodologies. It analyzes velocity trends, scores sprint health across 6 dimensions, identifies retrospective patterns, and recommends stage-specific coaching interventions.

Workflow

1. Assess Current State

The agent collects sprint data and establishes baselines:

python scripts/velocity_analyzer.py sprint_data.json --format json > velocity_baseline.json
python scripts/sprint_health_scorer.py sprint_data.json --format text
python scripts/retrospective_analyzer.py sprint_data.json --format text

Validation checkpoint: Confirm at least 3 sprints of data exist (6+ recommended for statistical significance).

2. Analyze Sprint Health

The agent scores the team across 6 weighted dimensions:

Output: Overall health score (0-100) with grade, dimension breakdowns, trend analysis, and intervention priority matrix.

3. Forecast Velocity

The agent runs Monte Carlo simulation on historical velocity data:

python scripts/velocity_analyzer.py sprint_data.json --format text

Output includes:

• Rolling averages (3, 5, 8 sprint windows)
• Trend detection via linear regression
• Volatility classification (coefficient of variation)
• Anomaly detection (outliers beyond 2 sigma)
• 6-sprint forecast with 50%, 70%, 85%, 95% confidence intervals

Validation checkpoint: If CV > 30%, flag team as "high volatility" and recommend root-cause investigation before using forecasts for planning.

4. Plan Sprint Capacity

python scripts/sprint_capacity_calculator.py team_data.json --format text

The calculator accounts for:

• Per-member availability (PTO, allocation percentage)
• Ceremony overhead: planning (2h) + daily standup (15min/day) + review (1h) + retro (1h) + refinement (1h)
• Focus factor (80% realistic, 85% optimistic)
• Story point estimates (conservative, realistic, optimistic) from historical velocity

Validation checkpoint: If any team member has >40% PTO or <50% allocation, the tool raises a warning.

5. Facilitate Retrospective

The agent uses retrospective analyzer insights to guide discussion:

python scripts/retrospective_analyzer.py sprint_data.json --format text

Analysis includes:

• Action item completion rates by priority and owner
• Recurring theme identification with persistence scoring
• Sentiment trend tracking (positive/negative)
• Team maturity assessment (forming/storming/norming/performing)

Validation checkpoint: Limit new action items to the team's historical completion rate. If the team completes 50% of action items, cap at 2-3 new items per retro.

6. Coach Team Development

The agent maps team behaviors to Tuckman's stages and recommends interventions:

Psychological safety assessment uses Edmondson's 7-point scale. Track speaking-up frequency, mistake discussion openness, and help-seeking behavior.

Example: Sprint Planning with Forecast

Given 6 sprints of velocity data [18, 22, 20, 19, 23, 21]:

$ python scripts/velocity_analyzer.py sprint_data.json --format text

Velocity Analysis
=================
Average: 20.5 points
Trend: Stable (slope: +0.3/sprint)
Volatility: Low (CV: 8.7%)

Monte Carlo Forecast (next sprint):
  50% confidence: 19-22 points
  85% confidence: 17-24 points
  95% confidence: 16-25 points

Recommendation: Commit to 19-20 points for reliable delivery.
Use 22 points only if team has no PTO and no known blockers.

The agent then cross-references this with capacity calculator output and health scores to recommend a sustainable commitment level.

Input Schema

All tools accept JSON following 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": []
    }
  ]
}

Tools

Templates & Assets

• assets/sprint_report_template.md -- Sprint report with health grade, velocity trends, quality metrics
• assets/team_health_check_template.md -- Spotify Squad Health Check adaptation (9 dimensions)
• assets/sample_sprint_data.json -- 6-sprint dataset for testing tools
• assets/expected_output.json -- Reference outputs (velocity avg 20.2, health 78.3/100)
• assets/user_story_template.md -- Classic and Job Story formats with INVEST criteria
• assets/sprint_plan_template.md -- Sprint plan with capacity, commitments, risks

References

• references/velocity-forecasting-guide.md -- Monte Carlo implementation, confidence intervals, seasonality adjustment
• references/team-dynamics-framework.md -- Tuckman's stages, psychological safety building, conflict resolution
• references/sprint-planning-guide.md -- Pre-planning checklist, SMART goals, capacity methodology

Key Metrics & Targets

Troubleshooting

Success Criteria

• Sprint health score consistently above 80/100 across 6-dimension assessment
• Velocity coefficient of variation (CV) maintained below 20% over rolling 6-sprint window
• Sprint commitment reliability exceeds 85% (completed vs. planned points)
• Action item completion rate from retrospectives exceeds 70% by next retro
• Blocker average resolution time under 3 working days
• Team maturity advances at least one Tuckman stage within 3-6 months of coaching
• Psychological safety score on Edmondson scale exceeds 4.0/5.0

Scope & Limitations

In Scope:

• Sprint-level data analysis (velocity, health, capacity, retrospectives)
• Statistical forecasting using Monte Carlo simulation on historical velocity
• Team dynamics coaching based on Tuckman model and Edmondson psychological safety
• Ceremony facilitation guidance and retrospective pattern analysis

Out of Scope:

• Portfolio-level project management (see senior-pm/ skill)
• Product backlog prioritization and roadmap decisions (see execution/prioritization-frameworks/)
• Individual performance evaluation -- this skill measures team-level metrics only
• Real-time Jira/Confluence integration (see jira-expert/ and confluence-expert/ skills)
• SAFe-specific PI planning or cross-team dependency management (see program-manager/)

Important Caveats:

• The Scrum Guide 2020 removed the term "velocity" as a required artifact; this skill treats velocity as a diagnostic tool, not a performance measure. Flow metrics (cycle time, throughput, WIP) complement velocity for delivery forecasting. Use both -- velocity for sprint planning, flow metrics for process improvement.
• Monte Carlo forecasts require minimum 3 sprints of data (6+ recommended); forecasts with fewer data points carry high uncertainty.
• Health scores are heuristics, not absolute measures. Calibrate dimension weights to your team context.

Integration Points

Tool Reference

velocity_analyzer.py

Analyzes sprint velocity data with trend detection, Monte Carlo forecasting, and anomaly identification.

sprint_health_scorer.py

Scores sprint health across 6 weighted dimensions with composite grading and recommendations.

retrospective_analyzer.py

Processes retrospective data to track action item completion, identify recurring themes, and assess team maturity.

sprint_capacity_calculator.py

Calculates sprint capacity accounting for ceremony overhead, PTO, allocation percentages, and focus factor.