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Get Started Free →Sprint health scoring and velocity analysis for agile teams. Usage: /sprint-health <analyze|velocity> [options]
.claude/skills/alirezarezvani-sprint-health/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-11 | ✗→✓ | ▲ Improved | -49% | 0% |
| case-12 | ✗→✓ | ▲ Improved | -44% | 0% |
| case-01 | ✗→✓ | ▲ Improved | 84% | 0% |
| case-02 | ✗→✓ | ▲ Improved | 320% | 0% |
| case-04 | ✗→✓ | ▲ Improved | -39% | 0% |
Score sprint health across delivery, quality, and team metrics with velocity trend analysis.
/sprint-health analyze <sprint_data.json> Full sprint health score
/sprint-health velocity <sprint_data.json> Velocity trend analysisjson{ "sprint_name": "Sprint 24", "committed_points": 34, "completed_points": 29, "stories": {"total": 12, "completed": 10, "carried_over": 2}, "blockers": [{"description": "API dependency", "days_blocked": 3}], "ceremonies": {"planning": true, "daily": true, "review": true, "retro": true} }
/sprint-health analyze sprint-24.json
/sprint-health velocity last-6-sprints.json
/sprint-health analyze sprint-24.json --format jsonproject-management/skills/scrum-master/scripts/sprint_health_scorer.py — Sprint health scorer (<data_file> [--format text|json])project-management/skills/scrum-master/scripts/velocity_analyzer.py — Velocity analyzer (<data_file> [--format text|json])> project-management/skills/scrum-master/SKILL.md
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-10 | pass→pass | 4,421 | 2,016 | -54% | 1 | 1 | 0% | 720 | 699 | -3% | 0 | 0 | — |
case-11 | fail→pass | 9,277 | 2,800 | -70% | 1 | 1 | 0% | 1,593 | 812 | -49% | 0 | 0 | — |
case-12 | fail→pass | 7,884 | 2,812 | -64% | 1 | 1 | 0% | 1,517 | 849 | -44% | 0 | 0 | — |
case-01 | fail→pass | 4,999 | 8,836 | +77% | 1 | 1 | 0% | 804 | 1,479 | +84% | 0 | 0 | — |
case-02 | fail→pass | 3,574 | 10,351 | +190% | 1 | 1 | 0% | 525 | 2,205 | +320% | 0 | 0 | — |
case-03 | fail→fail | 2,814 | 8,341 | +196% | 1 | 1 | 0% | 429 | 770 | +79% | 0 | 0 | — |
case-04 | fail→pass | 7,379 | 2,297 | -69% | 1 | 1 | 0% | 1,257 | 767 | -39% | 0 | 0 | — |
case-05 | fail→pass | 4,320 | 2,547 | -41% | 1 | 1 | 0% | 659 | 807 | +22% | 0 | 0 | — |
case-06 | fail→pass | 4,745 | 1,861 | -61% | 1 | 1 | 0% | 796 | 676 | -15% | 0 | 0 | — |
case-07 | fail→pass | 6,445 | 1,355 | -79% | 1 | 1 | 0% | 1,115 | 560 | -50% | 0 | 0 | — |
case-08 | pass→pass | 3,868 | 2,075 | -46% | 1 | 1 | 0% | 779 | 705 | -9% | 0 | 0 | — |
case-09 | pass→pass | 9,526 | 2,006 | -79% | 1 | 1 | 0% | 1,683 | 711 | -58% | 0 | 0 | — |
case-13 | fail→pass | 15,304 | 8,440 | -45% | 1 | 1 | 0% | 2,753 | 1,897 | -31% | 0 | 0 | — |
case-14 | fail→pass | 8,146 | 2,473 | -70% | 1 | 1 | 0% | 1,553 | 766 | -51% | 0 | 0 | — |
case-15 | fail→pass | 3,917 | 1,336 | -66% | 1 | 1 | 0% | 600 | 570 | -5% | 0 | 0 | — |
case-16 | pass→pass | 3,502 | 1,435 | -59% | 1 | 1 | 0% | 590 | 571 | -3% | 0 | 0 | — |
case-17 | fail→pass | 4,837 | 1,367 | -72% | 1 | 1 | 0% | 793 | 573 | -28% | 0 | 0 | — |
case-18 | fail→pass | 7,745 | 2,152 | -72% | 1 | 1 | 0% | 1,381 | 712 | -48% | 0 | 0 | — |
case-19 | fail→pass | 1,533 | 1,639 | +7% | 1 | 1 | 0% | 182 | 571 | +214% | 0 | 0 | — |
case-20 | pass→pass | 11,241 | 8,887 | -21% | 1 | 1 | 0% | 1,809 | 1,806 | -0% | 0 | 0 | — |
case-21 | pass→pass | 9,677 | 9,745 | +1% | 1 | 1 | 0% | 1,771 | 2,117 | +20% | 0 | 0 | — |
case-22 | pass→pass | 13,966 | 12,391 | -11% | 1 | 1 | 0% | 2,217 | 2,365 | +7% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted, and 21 counted toward the lift figure. The other 1 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of +64 percentage points is the difference between those two pass rates over the 21 comparable cases.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.