retro-analysis

name	retro-analysis
description	Analyse sprint delivery data and produce a structured retrospective brief. Use when asked to run a retrospective, analyse sprint data, prepare a retro brief, or turn sprint metrics into discussion prompts. Produces a data-grounded retrospective brief with completion stats, pattern analysis, Start/Stop/Continue prompts, and one concrete experiment for next sprint.

Retrospective Analysis Skill

Generate a data-grounded retrospective brief that separates facts from feelings, so the team spends retro time on solutions rather than debating what happened.

Required Inputs

Ask the user for these if not provided:

• Sprint tickets: planned vs. completed
• Carry-over tickets and reasons (if known)
• Tickets reopened after closing (quality signal)
• Any incidents or unplanned work (scope creep signal)
• Sprint velocity vs. historical average (trend context)

Process

1. Calculate: completion rate, carry-over rate, unplanned work percentage
2. Identify patterns: which ticket types were most likely to carry over? Which caused blockers?
3. Note any process or communication breakdowns visible in the data
4. Prepare 3 "Start / Stop / Continue" prompts based on the data — not generic, specific to this sprint
5. Suggest 1 concrete experiment for the next sprint based on the biggest friction point
6. Validate — Confirm each prompt is specific to this sprint (not a recycled generic prompt), and that the recommended experiment is concrete and measurable

Output Structure

Sprint [Number] Retrospective Brief

By the Numbers:

• Planned: [n] tickets | Completed: [n] | Carry-over: [n] | Completion rate: [%]
• Unplanned work: [n] tickets ([%] of capacity)
• Velocity: [points] vs. [average] average

What the Data Suggests: [2-3 observations grounded in the numbers above]

Discussion Prompts:

• Start: [specific prompt based on this sprint's data]
• Stop: [specific prompt based on this sprint's data]
• Continue: [specific prompt based on this sprint's data]

Suggested Experiment for Next Sprint: [One concrete, testable process change — with a specific success metric]

Quality Checks

• Each Start/Stop/Continue prompt names a specific behaviour, not a vague category
• The recommended experiment is testable in one sprint
• Carry-over analysis identifies the ticket type or cause, not just the count
• Data observations don't assign blame — they describe patterns
• Velocity trend is mentioned in context (is this a one-off or a pattern?)