retro-analysis

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]

Deeper Materials

This skill ships with support files — use them when they are available:

• references/root-cause-vs-symptom.md — Retros That Change Things: Root Causes vs Symptoms. Apply it while producing the output; it carries the calibration and judgment calls the method summary above compresses.
• templates/retro-board.md — a fill-in version of the deliverable with the quality gates inline. Offer it when the user wants to work the document themselves rather than have it generated.

Scoring Rubric (0–40)

Score any output of this skill before handing it over; 32+ is ship-quality.

Dimension	0	5	10
Data grounding	Numbers missing or wrong; observations are opinions with no traceable source	Core rates computed correctly, but observations only restate the numbers without pattern analysis (ticket types, historical comparison)	Every observation traces to a computed figure, carry-over is broken down by type/cause, and velocity is compared against the historical trend, not just the average
Blamelessness	Brief names or implies individuals/disciplines as the cause ("QA missed them")	Neutral wording, but framing still points at effort or diligence rather than systemic conditions	Failure modes are reframed as process/coverage/scheduling patterns the data actually supports — a defensive reader would find nothing aimed at them
Prompt specificity	Start/Stop/Continue are recycled generic categories ("communicate better")	Prompts reference this sprint but stay at category level — no numbers, no named behaviour	Each prompt is welded to this sprint's data, names one specific behaviour, and is phrased to open discussion rather than dictate the answer
Experiment quality	No experiment, or a multi-quarter initiative dressed as one	One concrete change, but success is unmeasurable or can't be evaluated at the next retro	One process change testable within a single sprint, with explicit success metrics and a stated cost-if-wrong, checkable at the next retro

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?)

Anti-Patterns

• Do not assign blame to individuals in the retrospective brief — observations must describe patterns, not people
• Do not produce Start/Stop/Continue prompts that are vague categories — each must name a specific behaviour
• Do not recommend an experiment that cannot be completed within one sprint — small, testable experiments only
• Do not treat carry-over tickets as a velocity problem without first identifying the root cause category
• Do not run the same retrospective format every sprint — vary the format to prevent engagement fatigue