# Sprint Report

> Generate a sprint report from the Auro Design System Azure DevOps board. It prompts for which iteration (sprint) to report on, reports the date range that iteration covers, then runs a saved ADO query filtered to work items changed during that range (excluding the Test Case/Test Plan/Test Suite/Epic/Feature/Initiative work item types report-wide) and summarizes them as a Work Item Type × current State matrix (User Story/Bug/Design Story/… against New/New (edit only)/Approved/Active/Blocked/Resolved/…), with the New column split into items actually created during the sprint versus older items still in New (New (edit only)), and the Closed column split into items that have a linked GitHub pull request or commit versus those that don't. It also renders a matrix showing the +/- change in each count versus the previous sprint, the same current/diff matrices filtered to items carrying the Support tag, a breakdown of the current sprint by assignee, the same current/diff matrices grouped by Area Path, two flat lists of tickets finished this sprint (current state Closed/Done/Rejected) split by whether they have a linked GitHub commit or pull request, a flat list of open work-in-progress — every ticket that held a state other than New or Approved at any point during the sprint (using each item's revision history, not just its current state) and whose current state is a still-open worked state (not New, Approved, Closed, Removed, Rejected, or Done) — and a sprint-completion breakdown table (completed User Story count and summed Story Points, plus resolved Bug counts by Severity: Critical/High/Medium/Low) over the closed-this-sprint-with-linked-code set. It also generates a per-bug root-cause narrative section (Section 8a) that, for every closed Bug that shipped code, flags whether it was a Support ticket (and who reported it), traces the fix's changed lines back through git blame to the prior GitHub pull request(s) that last touched them (link and date), names who reviewed that last touch (humans vs AI bots), lists the contributors who last touched the buggy lines, makes an evidence-grounded (fallback code-style) AI-vs-human guess about who wrote that code, gives a leadership-level explanation of the process gap that let the defect slip past that earlier review, records the regression remedies the fix already applied, and recommends follow-on prevention steps. After rendering, it offers to export the report to a Markdown file at a filename and directory you choose (defaulting the filename to the sprint name). Read-only against Azure DevOps — it never creates or edits work items.

## Facts
- Page: https://tashan.sh/capability/skill-alaskaairlines-sprint-report
- tashan id: skill:AlaskaAirlines/sprint-report
- Source: https://github.com/AlaskaAirlines/auro-ai
- Type: skill
- Category: devtools
- tashan score: not scored (catalogued only — too little public evidence)
- Adoption: 9.0
- Upkeep: 96.0
- Freshness: 92.0
- Evidence coverage: 84% of the inputs this score can use
- Health: active
- Instruction depth: not yet graded
- Official: no

## Install

```sh
cp -r sprint-report ~/.claude/skills/
```

## Security audit
Not scanned. We audit npm-published capabilities; this one has no npm package we can resolve, or has not reached the queue. This is not a clean bill of health.

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Measured 2026-09-12 by tashan (https://tashan.sh) from public evidence. Scorer s5.