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retro-analysis【数据与研究】Analyses sprint delivery data and produces 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 f…
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Generate a data-grounded retrospective brief that separates facts from feelings, so the team spends retro time on solutions rather than debating what happened.
Work from what is given. If any of these is missing, still produce the complete output: fill each gap with a reasonable assumption marked (assumed, confirm) and list those assumptions at the end. Ask a question first only when the core subject itself is missing (there is nothing to write about).
By the Numbers:
What the Data Suggests: [2-3 observations grounded in the numbers above]
Discussion Prompts:
Suggested Experiment for Next Sprint: [One concrete, testable process change — with a specific success metric]
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.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 |