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scrum-master

Advanced Scrum Master skill for data-driven agile team analysis and coaching. Use when the user asks about sprint planning, velocity tracking, retrospectives, standup facilitation, backlog grooming, story points, burndown charts, blocker resolution, or agile team health. Runs Python scripts to analyse sprint JSON exports from Jira or similar tools: velocity_analyzer.py for Monte Carlo sprint forecasting, sprint_health_scorer.py for multi-dimension health scoring, and retrospective_analyzer.py for action-item and theme tracking. Produces confidence-interval forecasts, health grade reports, and improvement-velocity trends for high-performing Scrum teams.

skill-install — Terminal

Install via CLI (Recommended)

clawhub install openclaw/skills/skills/alirezarezvani/scrum-master
Or

What This Skill Does

The scrum-master skill for OpenClaw is a sophisticated data-driven engine designed to elevate agile team performance through advanced analytics and empirical forecasting. Unlike basic project management tools, this skill leverages three specialized Python analysis scripts to turn raw sprint exports into actionable coaching insights. By processing JSON data from Jira or similar platforms, it generates Monte Carlo simulations for accurate delivery forecasting, multidimensional team health scores based on objective performance metrics, and retrospective analysis to identify recurring cultural or procedural bottlenecks. It acts as an expert assistant that helps Scrum Masters, Agile Coaches, and Engineering Managers transition from intuition-based decisions to evidence-based team development.

Installation

To integrate this skill into your environment, run the following command in your terminal: clawhub install openclaw/skills/skills/alirezarezvani/scrum-master Ensure that you have your sprint history exported in a standard JSON format compatible with the script requirements provided in the documentation.

Use Cases

Use this skill when you need to answer complex questions about team predictability and health. It is ideal for:

  • Preparing for Sprint Planning by using Monte Carlo projections.
  • Conducting Sprint Retrospectives backed by sentiment and action-item tracking.
  • Investigating a drop in velocity to identify if it stems from blocker resolution delays or scope creep.
  • Presenting team maturity and improvement trends to stakeholders using quantitative data.

Example Prompts

  1. "Run the velocity_analyzer on this sprint_data.json and tell me if our 85% confidence interval for the next three sprints is trending upward."
  2. "I'm worried about our team's sustainability. Use the sprint_health_scorer to audit our last four sprints and identify the primary weight dragging down our overall grade."
  3. "Analyze our recent retrospectives in retrospect_data.json. Which themes keep reappearing and what is our current action-item completion rate?"

Tips & Limitations

For optimal results, ensure you provide at least 6 sprints for the Monte Carlo simulations; while 3 are the minimum, higher data density significantly increases statistical confidence. When using the health scorer, verify that your input data includes ceremony participation and story completion metadata, as missing fields will result in incomplete scoring across the six key dimensions. Always use the --format json flag when piping output into other automated reporting dashboards. Be aware that the tool relies heavily on consistent data entry in your project management software; if tickets are not moved correctly, the retrospective and velocity data may be skewed. Treat the output as a diagnostic aid rather than a final mandate, using the insights to spark collaborative conversations during your team syncs.

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Updated2026-05-01
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Add to Configuration

Paste this into your clawhub.json to enable this plugin.

{
  "plugins": {
    "official-alirezarezvani-scrum-master": {
      "enabled": true,
      "auto_update": true
    }
  }
}

Tags(AI)

#agile#scrum#data-analysis#project-management#coaching
Safety Score: 4/5

Flags: file-read, code-execution

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