retro

Analyze Git history to generate retrospective reports with sprint metrics.

Updated Mar 27, 2026

Search Tags:#git #metrics #retrospective #engineering #velocity #sprint

Ready to run

npx skills add https://github.com/jangtrinh/EaseUI-Agent --skill retro-jangtrinh

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: retro
Source: https://github.com/jangtrinh/EaseUI-Agent/tree/main/.claude/skills/retro
Command: npx skills add https://github.com/jangtrinh/EaseUI-Agent --skill retro-jangtrinh

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Weekly retrospectives turn raw Git activity into structured insights that guide sprint planning and process improvement, reducing guesswork and bias.

Core Features & Use Cases

Collects commits, authors, timestamps, and file changes to compute velocity and engagement metrics.
Produces a narrative summary and a structured data snapshot suitable for reports and dashboards.
Use Case: End-of-sprint reviews that surface hotspots and actionable improvements.

Quick Start

Run the retro analysis on your current repository and review the generated metrics and narrative.

Frequently Asked Questions about retro

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

FAQPage Schema

How do I generate engineering metrics from Git history for a sprint retrospective?▼

Git history analysis computes commits, LOC, and file activity to reveal sprint velocity and collaboration patterns. By applying weekly sprint contexts to repository data, it surfaces actionable delivery insights and structured metrics for retrospective dashboards.

What is the best way to track engineering velocity using commit history?▼

Tracking engineering velocity from commit history involves computing commit counts, LOC changes, and file hotspots per author. This reveals delivery patterns and focus trends, producing structured data snapshots and narrative summaries for sprint planning.

Can I use Git activity to identify collaboration hotspots in my repository?▼

Yes, analyzing Git file activity and author commit patterns identifies collaboration hotspots and engagement metrics. This surfaces areas of high concentration, revealing focus patterns and potential bottlenecks for end-of-sprint reviews.

Does this approach produce structured data suitable for engineering dashboards?▼

Yes, analyzing repository history outputs structured data snapshots alongside narrative reports. These computed metrics, including commits, LOC, and hotspots, are suitable for direct integration into engineering dashboards and sprint review reports.

Do I need any external dependencies to analyze sprint velocity from repository commits?▼

No, analyzing sprint velocity from repository commits requires no external dependencies. The process operates directly on Git history to compute author engagement and file activity metrics without requiring additional software components or integrations.