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predictive-analytics

Predict sprint completion dates using Monte Carlo simulations on Azure DevOps velocity data.

47|10|Updated Feb 22, 2026
Search Tags:#sprint planning#forecasting#azure devops#prediction#monte-carlo#flow-metrics
One-click install
npx skills add https://github.com/gonzalezpazmonica/pm-workspace --skill predictive-analytics
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: predictive-analytics
Source: https://github.com/gonzalezpazmonica/pm-workspace/tree/main/.claude/skills/predictive-analytics
Command: npx skills add https://github.com/gonzalezpazmonica/pm-workspace --skill predictive-analytics

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill addresses the uncertainty in project timelines and team performance by providing data-driven predictions and insights into workflow efficiency.

Core Features & Use Cases

  • Sprint Forecasting: Predicts sprint completion dates using Monte Carlo simulations based on historical velocity.
  • Flow Metrics Analysis: Calculates and interprets Flow Efficiency to identify bottlenecks.
  • WIP Aging Alerts: Monitors Work-In-Progress items to proactively flag potential blockers.
  • Throughput Trend Analysis: Identifies trends in completed work items per week using linear regression.
  • Use Case: A Product Owner can use this Skill to get a realistic estimate of when a set of features will be completed, allowing for better stakeholder communication and expectation management.

Quick Start

Use the predictive-analytics skill to forecast sprint completion dates based on historical velocity and remaining work.

Frequently Asked Questions about predictive-analytics

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I forecast sprint completion dates using historical velocity in Azure DevOps?▼

You can forecast sprint completion dates by running Monte Carlo simulations based on historical velocity. The Skill integrates directly with Azure DevOps using WIQL queries to extract throughput data and predict realistic sprint outcomes.

What is Monte Carlo simulation for sprint planning and how does it handle project uncertainty?▼

Monte Carlo simulation for sprint planning runs multiple probability scenarios against historical flow metrics to model project uncertainty. It calculates likelihood ranges for sprint completion dates rather than relying on single-point estimates.

How do I calculate flow efficiency to identify process bottlenecks in my workflow?▼

You calculate flow efficiency by analyzing state transitions from your Azure DevOps work items. The Skill calculates this metric to pinpoint wait times versus active work times, identifying specific process bottlenecks.

Can I use WIQL queries to monitor WIP aging and alert for potential blockers?▼

Yes, you can use WIQL queries to monitor Work-In-Progress aging. The Skill gathers WIP data from Azure DevOps and proactively flags items that are aging beyond expected thresholds, highlighting potential blockers.

What is the best way to analyze throughput trends for completed work items per week?▼

The best way to analyze weekly throughput trends is using linear regression on completed work items. The Skill applies this analysis to your Azure DevOps historical data to identify upward or downward delivery trends.

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