How does this help with release planning?

The skill uses historical velocity data and predictive models like Monte Carlo simulations to calculate realistic release dates with clear confidence intervals.

Does it support data visualization?

It provides structured guidance for designing three-level dashboards (Executive, Management, Team) and analyzing cumulative flow diagrams.

What types of metrics does this skill cover?

It covers three main areas: development metrics (velocity, lead time), quality metrics (test coverage, defect density), and business metrics (DAU/MAU, churn, and revenue).

Can I use this for quality assurance tracking?

Yes, it includes specific standards for defect density and test coverage targets, such as the 80% coverage goal for critical logic.

How does it integrate with existing workflows?

It aligns with PDCA (Plan-Do-Check-Act) cycles and provides data preparation templates for sprint retrospectives and stakeholder reporting.

Metrics Tracking & Analysis

Name: Metrics Tracking & Analysis
Author: daishiman

bydaishiman

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Analíticas y Monitorización

Tracks and analyzes agile development metrics, quality indicators, and business KPIs to drive data-driven process improvements.

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The Metrics Tracking skill enables Claude to systematically monitor and interpret development workflows through key performance indicators such as velocity, cycle time, and lead time. It provides a comprehensive framework for visualizing progress via burndown charts and dashboards while simultaneously tracking technical health through test coverage and defect density. This skill is essential for teams practicing Scrum or Kanban who need evidence-based decision-making, accurate release forecasting, and structured continuous improvement cycles (PDCA) within their software development lifecycle.

Características Principales

Statistical analysis and predictive forecasting models
1 GitHub stars
Agile workflow tracking (velocity, cycle time, lead time)
Technical quality monitoring including test coverage and defect density
Business KPI integration for product and customer success metrics
Automated reporting frameworks and dashboard design patterns

Casos de Uso

Identifying process bottlenecks using cumulative flow diagrams and cycle time analysis.
Quantifying technical debt and tracking quality improvements across multiple development cycles.
Managing sprint progress and forecasting release dates based on historical velocity trends.

Acerca de

Características Principales

Statistical analysis and predictive forecasting models
1 GitHub stars
Agile workflow tracking (velocity, cycle time, lead time)
Technical quality monitoring including test coverage and defect density
Business KPI integration for product and customer success metrics
Automated reporting frameworks and dashboard design patterns

Casos de Uso

Identifying process bottlenecks using cumulative flow diagrams and cycle time analysis.
Quantifying technical debt and tracking quality improvements across multiple development cycles.
Managing sprint progress and forecasting release dates based on historical velocity trends.