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Skillv1.0.0

scrum-master

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, diagn

by borghei(0) 0 installs
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About

Imported from borghei/claude-skills (project-management/scrum-master/SKILL.md). Install upstream with npx skills add borghei/claude-skills --skill scrum-master. Copyright stays with the author (MIT + Commons Clause).

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:

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

Use it

Copy one of these into your project. Installing also returns the manifest and these snippets.

yaml
targets:
  - https://api.opensmartroute.ai/api/v1/registry/borghei-claude-skills-scrum-master/manifest   # or paste the manifest below

Manifest

An Open Capability Manifest: the router reads it to know what this does, what it costs and when to pick it.

borghei-claude-skills-scrum-master.ocm.jsonjson
{
  "ocm": "1",
  "id": "borghei-claude-skills-scrum-master",
  "kind": "skill",
  "name": "scrum-master",
  "description": "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.",
  "publisher": "borghei",
  "version": "1.0.0",
  "capabilities": {
    "domains": [
      "general"
    ],
    "tags": [
      "skill-md",
      "scrum",
      "agile",
      "sprint",
      "retrospective",
      "impediments",
      "skills-sh"
    ],
    "languages": [
      "en"
    ]
  },
  "quality_prior": 0.6,
  "examples": [
    "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."
  ],
  "primary": false,
  "metadata": {
    "source": {
      "provider": "skills.sh",
      "repository": "https://github.com/borghei/claude-skills",
      "path": "project-management/scrum-master/SKILL.md",
      "ref": "HEAD",
      "url": "https://github.com/borghei/claude-skills/blob/HEAD/project-management/scrum-master/SKILL.md",
      "key": "borghei/claude-skills/project-management/scrum-master/SKILL.md"
    },
    "license": "MIT + Commons Clause"
  },
  "instructions": "# Scrum Master Expert\n\nThe 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.\n\n## Core Capabilities\n\n- **Sprint health scoring** — 6 weighted dimensions (commitment reliability, scope stability, blocker resolution, ceremony engagement, completion distribution, velocity predictability) → 0-100 grade.\n- **Velocity forecasting** — Monte Carlo simulation with ro",
  "cost": {
    "context_tokens": 1885
  }
}

Fetch it by URL: GET /api/v1/registry/borghei-claude-skills-scrum-master/manifest?version=1.0.0

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