scrum-master

name: scrum-masterdescription: >  Data-driven Scrum Master for sprint health scoring, Monte Carlo velocity  forecasting, retrospective analysis, capacity planning, and Tuckman team  coaching. Use when facilitating sprint planning, diagnosing velocity, or  running retrospectives.license: MIT + Commons Clausemetadata:   version: 2.0.1  author: borghei  category: project-management  domain: agile-development  updated: 2026-06-15  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.

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, settle the inputs below. When one is missing or vague and would change the result, ask about it; when it wouldn't, use a sensible default and say which one you used.

• 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).

Ask about the two or three inputs that matter most for this request, not the whole list. If the user wants a result straight away, produce it and state your assumptions at the top.

Quick Start

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

Anti-Patterns

Retro action items that never land

Mistake: Each retro produces 4-6 action items, and by the next retro most are undone while the team re-discusses the same problems. Why it happens: Adding an action item feels like progress, and nobody checks the old ones before writing new ones. Instead: Track completion with retrospective_analyzer.py and open each retro with last sprint's items. If the same problem shows up in more than 2 consecutive retros, root-cause it instead of adding another item.

Definition of Done drift

Mistake: The DoD says "tested + reviewed + deployed", but many tickets close as Done with no tests or with deploys still pending. Why it happens: Late in the sprint, closing tickets looks better than carrying them over. Instead: Each sprint, sample 10 closed tickets and check each against the DoD. If more than 20% fall short, enforce the DoD through automation. Otherwise reported velocity overstates what was really done.

A sprint goal that names no outcome

Mistake: The sprint goal is "complete the sprint backlog", or there is none. Why it happens: Planning centers on choosing tickets, so the goal gets written last as a summary of the list. Instead: Write the goal as a customer-visible outcome, and rewrite any goal that mentions a ticket count. A real goal lets the team trade scope mid-sprint and gives the sprint review something to inspect.

Seven more, each with a bad and a good example: references/red-flags.md.

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.

AI-Assisted Delivery

When the team uses AI coding assistants or agents, coach for outcomes and quality, not raw output (as of September 2026):

• Scrum Guide Expansion Pack (first released June 2025 as v2025.6; current v2026.1, scrumexpansion.org) is a companion to the 2020 Scrum Guide, not a replacement. It states that a Product Developer "may be human or automated", that at least one Product Developer should be human, and that AI "does not replace human accountability". Its AI and Scrum guidance adds that AI-assisted work must meet the same quality bar as any other work and that AI-generated work should be made transparent.
• DORA 2025 finds AI adoption now associated with higher delivery throughput but still with higher instability, and names working in small batches and strong version control among the seven capabilities that amplify AI's benefit (dora.dev).

Practical guidance for the Scrum Master:

1. Name the human owner. Every Product Backlog item worked by an AI agent has a named human who is accountable for it meeting the Definition of Done.
2. Make AI use transparent. Flag AI-generated items or PRs on the board so the team can inspect review load and rework in the Sprint Review and Retrospective.
3. Watch review load and rework. Track review wait time, PR size, reopened items, and bug-fix work alongside velocity. A velocity jump with rising rework is not improvement.
4. Keep items small. Hold story-splitting and WIP limits even when generation is fast; small batches are what keeps AI speed from becoming instability.
5. Don't re-baseline velocity blindly. Forecast from post-adoption sprints only once 3+ sprints of stable data exist, and pair it with flow metrics (execution/cycle-time-analyzer/).

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