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measure

Calculate delivery and quality metrics from git history and PR data.

4|Updated Jul 20, 2026
Search Tags:#git#performance#analytics#productivity#metrics#engineering
One-click install
npx skills add https://github.com/highflame-ai/ai-factory --skill measure-highflame-ai
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: measure
Source: https://github.com/highflame-ai/ai-factory/tree/main/skills/measure
Command: npx skills add https://github.com/highflame-ai/ai-factory --skill measure-highflame-ai

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill addresses the lack of objective visibility into development workflows by providing a data-driven way to verify if engineering process improvements are actually delivering value.

Core Features & Use Cases

  • Delivery Metrics: Automatically calculates PR throughput, cycle time, and rework rates from git and forge history.
  • Process Analysis: Tracks spec-to-merge times, review-finding trends, and knowledge growth to identify bottlenecks or improvements.
  • Use Case: Use this skill at the start of a project to establish a baseline, then run it quarterly to determine if your team's adoption of AI-assisted coding practices has improved delivery speed or code quality.

Quick Start

Run the measure skill to generate a performance report comparing your current metrics against the established baseline.

Frequently Asked Questions about measure

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

FAQPage Schema
How do I calculate cycle time and rework rates from git history?▼

You can calculate cycle time and rework rates by analyzing git logs and forge PR data to evaluate software delivery metrics. This process correlates toolkit usage with development cycle times to generate comparative performance reports.

What are engineering productivity metrics and how do they track code review trends?▼

Engineering productivity metrics quantify delivery speed and code quality by tracking spec-to-merge times and review-finding trends. These metrics identify development bottlenecks and verify if engineering process improvements are actually delivering value.

Can I measure the impact of AI-assisted coding practices on PR throughput?▼

Yes, you can measure the impact by establishing a baseline of PR throughput at the start of a project. Running periodic comparative performance reports determines if AI-assisted coding adoption has improved delivery speed or code quality.

Do I need write access to git logs to analyze software development lifecycles?▼

No, you do not need write access to git logs to analyze software development lifecycles. Generating comparative performance reports requires only read-only access to git logs and project-specific metadata to calculate delivery and quality metrics.

What is the best way to establish a baseline for engineering process effectiveness?▼

The best way to establish a baseline for engineering process effectiveness is to calculate delivery and quality metrics from git history and forge PR data. This data-driven approach provides objective visibility into development workflows before improvements are applied.

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