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christian-byrne
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metrics-dashboard

Aggregate and analyze pipeline run data to track velocity and success rates.

Updated Aug 27, 2026
Search Tags:#performance#analytics#metrics#dashboard#reporting#pipeline
One-click install
npx skills add https://github.com/christian-byrne/ticket-to-pr-pipeline --skill metrics-dashboard-christian-byrne
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: metrics-dashboard
Source: https://github.com/christian-byrne/ticket-to-pr-pipeline/tree/main/skills/metrics-dashboard
Command: npx skills add https://github.com/christian-byrne/ticket-to-pr-pipeline --skill metrics-dashboard-christian-byrne

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

This Skill addresses the need to understand and improve the efficiency of the ticket-to-PR pipeline by providing insights into its performance metrics.

Core Features & Use Cases

  • Pipeline Velocity Tracking: Monitor the speed at which tickets move through the pipeline.
  • Success Rate Analysis: Identify the percentage of tickets that are successfully converted into pull requests.
  • Bottleneck Identification: Pinpoint stages in the pipeline where tickets tend to get stuck.
  • Use Case: A product manager can use this Skill to generate a weekly report on the average cycle time for feature requests, helping to forecast delivery timelines more accurately.

Quick Start

Use the metrics-dashboard skill to generate a report of the pipeline's performance metrics.

Frequently Asked Questions about metrics-dashboard

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

FAQPage Schema
How do I track pipeline velocity for ticket-to-PR workflows?▼

Pipeline velocity tracking aggregates ticket data via a central API to calculate cycle times, helping you monitor the speed tickets move through the pipeline and forecast delivery timelines accurately.

What is the best way to identify bottlenecks in a software development pipeline?▼

Bottleneck identification analyzes pipeline run data to pinpoint exact stages where tickets get stuck, allowing you to target specific improvement areas within your workflow and increase throughput.

Can I use client-side calculations to analyze ticket cycle time and success rates?▼

Yes, success rate analysis and cycle time calculations are performed client-side after fetching ticket data via a central API, providing immediate insights into your pipeline performance metrics.

Does pipeline performance monitoring require any external dependencies?▼

No external dependencies are required for pipeline performance monitoring, as the Skill operates independently using scripts to fetch ticket data and calculate metrics like velocity and success rates.

How do I generate a weekly report on average cycle time for feature requests?▼

Generate a weekly cycle time report by running the metrics dashboard scripts to aggregate pipeline run data, producing insights into average delivery speeds for your feature requests.

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