retro

Analyze completed sprints to identify velocity trends, scope changes, and blocker patterns.

Updated Apr 8, 2024

Search Tags:#retrospective #blockers #velocity #team-performance #sprint-analysis #cycle-trends

Ready to run

npx skills add https://github.com/tumes/dotfiles-nvim --skill retro-tumes

💬 Prompt for ChatGPT / Claude Chat▼

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

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Analyze completed linear cycles for retrospectives, identifying velocity trends, scope creep, and patterns to improve future planning.

Core Features & Use Cases

Analyze velocity and completion to reveal performance trends across cycles.
Detect scope changes and blockers to inform process improvements.
Produce data-driven insights you can discuss in sprint retrospectives.

Quick Start

Collect the most recent sprint cycle data and run the analysis to generate a retrospective summary with actionable items.

Frequently Asked Questions about retro

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

FAQPage Schema

How do I analyze sprint velocity trends for a retrospective?▼

Sprint analysis for retrospectives evaluates completed cycle data to identify velocity trends, scope creep, and blocker patterns across multiple sprints. It exports velocity metrics and completion rates to generate actionable insights for team discussion.

What is the best way to detect scope creep in linear cycles?▼

Detecting scope creep in linear cycles involves applying pattern analysis to completed sprint data to identify scope changes and blockers across multiple cycles. This reveals performance trends and process deviations to inform future sprint planning and continuous improvement.

Can I use this for cycle trend analysis across multiple sprints?▼

Yes, cycle trend analysis applies to team sprints across multiple cycles by evaluating historical completion data to track velocity trends and scope changes. This produces data-driven insights that satisfy retrospective requirements for ongoing sprint performance evaluation.

How do I generate actionable insights from completed sprints?▼

Generating actionable insights from completed sprints requires collecting recent cycle data and running an analysis that exports velocity metrics, completion rates, and blocker patterns. This produces a retrospective summary with specific items to discuss for process improvements.

Does sprint analysis require specific data formats to identify blockers?▼

Sprint analysis to identify blockers requires completed cycle data as input to detect blocker patterns and scope changes across sprints. By analyzing this data, the Skill produces data-driven insights without needing external components or specific framework dependencies.