scrum-master

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":"2026-02-15T00:00:00.000Z","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:

Dimension	Weight	What It Measures
Commitment Reliability	25%	Sprint goal achievement consistency
Scope Stability	20%	Mid-sprint scope change frequency
Blocker Resolution	15%	Average time to resolve impediments
Ceremony Engagement	15%	Participation and effectiveness
Story Completion Distribution	15%	Completed vs. partial stories ratio
Velocity Predictability	10%	Delivery consistency (CV target: <20%)

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:

Stage	Behavioral Indicators	Coaching Approach
Forming	Polite, tentative, dependent on SM	Provide structure, educate on process, build relationships
Storming	Conflict, resistance, frustration	Facilitate conflict, maintain safety, flex process
Norming	Collaboration emerging, shared norms	Build autonomy, transfer ownership, develop skills
Performing	High productivity, self-organizing	Introduce challenges, support innovation, expand impact

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",
       
       
       
    
  

Tools

Tool	Purpose	Command
velocity_analyzer.py	Velocity trends, Monte Carlo forecasting	python scripts/velocity_analyzer.py sprint_data.json --format text
sprint_health_scorer.py	6-dimension health scoring	python scripts/sprint_health_scorer.py sprint_data.json --format text
retrospective_analyzer.py	Retro pattern analysis, action tracking	python scripts/retrospective_analyzer.py sprint_data.json --format text
sprint_capacity_calculator.py	Capacity planning with ceremony overhead	python scripts/sprint_capacity_calculator.py team_data.json --format text

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

Metric	Target	Measurement
Health Score	>80/100	Sprint-level, 6 dimensions
Velocity Predictability (CV)	<20%	Rolling 6-sprint window
Commitment Reliability	>85%	Sprint goals achieved / attempted
Scope Stability	<15% change	Mid-sprint scope changes
Blocker Resolution	<3 days avg	Time from raised to resolved
Action Item Completion	>70%	Retro items done by next retro
Ceremony Engagement	>90%	Attendance + participation quality
Psychological Safety	>4.0/5.0	Monthly pulse survey

Troubleshooting

Symptom	Likely Cause	Resolution
Velocity drops for 2+ sprints without team change	Hidden scope creep, unclear definition of done, or tech debt accumulation	Run sprint_health_scorer.py to check scope stability score; tighten DoD and refinement process
CV exceeds 30% despite stable team	Inconsistent story sizing, mid-sprint scope injection, or unplanned absences	Analyze anomalies via velocity_analyzer.py; introduce reference stories for estimation calibration
Action item completion rate below 50%	Too many action items per retro, no owners assigned, or unrealistic scope	Cap new items at 2-3 per retro based on retrospective_analyzer.py historical completion data
Health score below 60 but team feels productive	Dimension weights may not match team context, or ceremony data is incomplete	Review dimension weights in HEALTH_DIMENSIONS config; ensure ceremony attendance data is populated
Monte Carlo forecast has wide confidence intervals	Insufficient historical data or high velocity volatility	Accumulate 6+ sprints of data; address root causes of volatility before relying on forecasts
Sprint capacity calculator overestimates	Focus factor set too high or ceremony overhead not calibrated	Adjust focus factor from 0.85 to 0.80; verify ceremony durations match actual team practices
Retrospective themes keep recurring across sprints	Systemic issues not addressed at root cause, or action items too superficial	Use retrospective_analyzer.py persistent issue detection; escalate recurring themes to management

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

Integration	Direction	Description
senior-pm/	Feeds into	Sprint velocity and health data informs portfolio-level health dashboards and executive reporting
sprint-retrospective/	Complements	Git-based velocity analysis complements this skill's JSON-based sprint data analysis
execution/brainstorm-okrs/	Feeds into	Sprint capacity data helps set realistic OKR targets for the quarter
execution/prioritization-frameworks/	Receives from	Prioritized backlog items feed into sprint planning commitment decisions
discovery/pre-mortem/	Receives from	Launch-blocking tigers may surface as sprint blockers requiring SM intervention
Jira via Atlassian MCP	Bidirectional	Pull sprint data for analysis; push health reports to Confluence dashboards
CI/CD Pipelines	Receives from	Deployment frequency and lead time data supplement velocity metrics

Tool Reference

velocity_analyzer.py

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

Flag	Type	Default	Description
data_file	positional	(required)	Path to JSON file containing sprint data
--format	choice	text	Output format: text or json

sprint_health_scorer.py

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

Flag	Type	Default	Description
data_file	positional	(required)	Path to JSON file containing sprint health data
--format	choice	text	Output format: text or json

retrospective_analyzer.py

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

Flag	Type	Default	Description
data_file	positional	(required)	Path to JSON file containing retrospective data
--format	choice	text	Output format: text or json

sprint_capacity_calculator.py

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

Flag	Type	Default	Description
data_file	positional	(optional)	Path to JSON file containing team capacity data
--format	choice	text	Output format: text or json
--demo	flag	off	Run with built-in sample data