Aggregate git, test, and friction data into structured engineering retro reports.
npx skills add https://github.com/s0613/S-skills --skill sj-retro
Please help me install this Agent Skill. Skill: sj-retro Source: https://github.com/s0613/S-skills/tree/main/skills/sj-retro Command: npx skills add https://github.com/s0613/S-skills --skill sj-retro
Subjective, unproductive retro meetings waste engineering team time with no concrete data to drive actionable improvements. This Skill eliminates that friction by automating the aggregation and analysis of all relevant weekly work data to generate clear, evidence-based retro insights.
Use the sj-retro skill to generate a complete weekly engineering retro report for your current project's last 7 days of work.
High-intent search queries and answers about installing and using this skill.
Automating engineering retrospective data aggregation involves pulling commit counts, test results, and QA verdicts to replace subjective manual reviews. This Skill processes git logs and friction logs to generate structured retro reports with delivery metrics and prioritized improvement recommendations.
Data-driven sprint review analysis evaluates team work output using objective metrics like commit breakdowns and test health scores. It transforms subjective meeting opinions into evidence-based insights by parsing delivery analytics, friction logs, and QA verdicts into actionable Keep/Improve/Try actions.
Generating a weekly retro report requires aggregating git commit logs, test runner results, and internal friction logs for the selected period. The Skill categorizes file changes and coverage trends to produce structured reports highlighting testing gaps, regressions, and process bottlenecks.
Yes, automated retro reports support 2-week product sprints by eliminating manual data gathering from git, test runners, and QA verdicts. The Skill analyzes the selected retro period to output complete reports with delivery metrics, test health evaluation, and process friction breakdowns.
Identifying workflow bottlenecks requires parsing the team's internal friction log to surface repeated delays and delight signals. This approach highlights specific process friction points alongside delivery analytics, showing exactly what workflow impediments need prioritized improvement actions.
No external dependencies or specific component integrations are required to analyze QA verdicts. The Skill operates independently to pull data from git commit logs, test runners, and friction logs, applying optional self-harness validation for proposed workflow changes without prerequisite modules.
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