Analyzes .jr/tickets data to identify workflow friction patterns and outputs categorized recommendations.
npx skills add https://github.com/bmsull560/Fabric_4L --skill jr-retro
Please help me install this Agent Skill. Skill: jr-retro Source: https://github.com/bmsull560/Fabric_4L/tree/main/.windsurf/skills/jr-retro Command: npx skills add https://github.com/bmsull560/Fabric_4L --skill jr-retro
This skill helps teams perform a structured post-mortem analysis of features that shipped, stalled, or underwent architect review to identify friction and opportunities for improvement.
Provide a feature ticket ID to begin a read-only retrospective analysis and generate actionable findings.
High-intent search queries and answers about installing and using this skill.
A structured post-mortem analysis gathers ticket histories from .jr/tickets to identify friction patterns like churn, missed catches, wasted cycles, and underspecified tasks. It outputs categorized recommendations across project, feature, and workflow dimensions to improve future development cycles.
During a post-release review, you should look for friction patterns including churning, missed catches, wasted cycles, underspecified tasks, and discoveries. Analyzing these patterns from ticket histories helps surface actionable process and product improvement opportunities.
To start a retrospective analysis, provide a feature ticket ID to initiate a read-only review. The system gathers ticket descriptions, statuses, assignees, and note history from .jr/tickets to diagnose friction and generate actionable findings.
Yes, you can use this retrospective approach for architecture triage sessions. It applies to features that shipped, stalled, or underwent architect review, diagnosing friction patterns to produce concrete project, feature, and workflow recommendations.
No, the post-mortem analysis maintains a strictly read-only stance and does not modify tickets or code during reviews. It only gathers and analyzes ticket data to output categorized recommendations for process improvement.
Ticket analysis generates three recommendation categories: Project Recommendations, Feature/Task Recommendations, and Workflow Recommendations. These provide concrete, actionable guidance for diagnosing churn and improving process and product outcomes.
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