Agent Skills: Scrum Master Expert

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.

Core Capabilities

• Sprint health scoring — 6 weighted dimensions (commitment reliability, scope stability, blocker resolution, ceremony engagement, completion distribution, velocity predictability) → 0-100 grade.
• Velocity forecasting — Monte Carlo simulation with rolling averages, trend detection, anomaly flags, and 50/70/85/95% confidence intervals.
• Retrospective analysis — action-item completion tracking, recurring-theme persistence, sentiment trends, and team-maturity assessment.
• Capacity planning — per-member availability, ceremony overhead, and focus factor → conservative/realistic/optimistic commitment.
• Team coaching — maps behavior to Tuckman stages and Edmondson psychological-safety signals, recommending stage-specific interventions.

When to Use

• Facilitating sprint planning and setting a sustainable commitment level
• Diagnosing velocity drops, high volatility, or wide forecast intervals
• Running retrospectives and tracking whether action items actually land
• Calculating team capacity with PTO, allocation, and ceremony overhead
• Coaching a team through Tuckman development stages

Clarify First

Before running the analysis, confirm these inputs. If any is unknown or vague, ASK — do not assume:

• [ ] Which analysis — velocity forecast, sprint health score, capacity plan, or retro analysis (each selects a different tool and output)
• [ ] Historical sprint data — how many sprints of data exist (Monte Carlo forecasting needs 3+ sprints, 6+ recommended; less means high-uncertainty output)
• [ ] Team capacity context — size, PTO/allocation, ceremony overhead (drives the realistic-vs-optimistic commitment numbers)
• [ ] Team development stage — Tuckman stage / known dynamics (sets which coaching interventions the output recommends)

Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.

Quick Start

| 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 |

All tools accept JSON following assets/sample_sprint_data.json. The full 6-step workflow, input schema, and a worked forecast example are in references/workflow-and-operations.md.

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

Load the reference that matches the task — keep this file lean and pull detail on demand:

• references/workflow-and-operations.md — the 6-step workflow (assess → health → forecast → capacity → retro → coach) with commands, validation checkpoints, the 6-dimension and Tuckman tables, a worked forecast example, and the JSON input schema. Read when running an end-to-end engagement.
• references/metrics-troubleshooting-and-tools.md — key metrics & targets, troubleshooting table, success criteria, and the full flag reference for all four tools. Read when setting targets, diagnosing problems, or scripting the tools.
• references/velocity-forecasting-guide.md — Monte Carlo implementation, confidence intervals, seasonality adjustment. Read when interpreting or tuning forecasts.
• references/team-dynamics-framework.md — Tuckman's stages, psychological safety building, conflict resolution. Read when coaching team development.
• references/sprint-planning-guide.md — pre-planning checklist, SMART goals, capacity methodology. Read when facilitating planning.
• references/retro-formats.md — retrospective formats and facilitation patterns. Read when designing a retro.
• references/red-flags.md — anti-patterns and warning signs in Scrum practice. Read when something on the team feels off.

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 "velocity" as a required artifact; this skill treats velocity as a diagnostic tool, not a performance measure. Use flow metrics (cycle time, throughput, WIP) alongside velocity.
• 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 |