Analyze commit history and contribution patterns to generate weekly engineering retrospective reports.
npx skills add https://github.com/SeaLion-hub/DICEE --skill retro-sealion-hub
Please help me install this Agent Skill. Skill: retro Source: https://github.com/SeaLion-hub/DICEE/tree/main/.agents/skills/gstack/retro Command: npx skills add https://github.com/SeaLion-hub/DICEE --skill retro-sealion-hub
Weekly engineering retrospectives are often manual, time-consuming, and inconsistent. This Skill automates the collection and analysis of commit history, contribution patterns, and quality signals to generate actionable team insights.
Run /retro to generate the latest weekly retrospective for the default 7-day window.
High-intent search queries and answers about installing and using this skill.
You can generate an automated weekly retrospective by running the /retro command, which analyzes commit history, contribution patterns, and quality metrics over a default 7-day window to produce a structured report.
Cross-repo focus analysis evaluates commit hotspots and contribution patterns across multiple repositories to identify team focus areas, needed when your engineering team works across several repositories and requires consistent windowed comparisons.
Yes, generating per-author praise and growth suggestions from contribution patterns is supported. The retrospective synthesizes individual commit data to highlight specific achievements and areas for improvement for each contributor.
Yes, the retrospective analysis supports windowed comparisons. While the quick-start workflow uses a default 7-day window, you can specify a chosen time window to compare metrics like insertions, deletions, and hotspots consistently.
The best way to export engineering team metrics is to use the Skill's end-to-end data collection and metrics synthesis, which produces an export-ready summary of team insights and quality signals for consistent improvement tracking.
No external dependencies are required to run commit history analysis. The Skill implements end-to-end steps for data collection, metrics synthesis, and reporting internally without relying on additional components.
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