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Get Started Free →Agile Scrum methodology adapted for automotive software projects Covers 3 topics across automotive-workflow domain.
.claude/skills/pangzhenying2025-automotive-workflow/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-13 | ✗→✓ | ▲ Improved | -13% | 0% |
| case-01 | ✓→✓ | ＝ Same ✓ | 21% | 0% |
| case-02 | ✓→✓ | ＝ Same ✓ | 9% | 0% |
| case-03 | ✓→✓ | ＝ Same ✓ | -9% | 0% |
| case-04 | ✓→✓ | ＝ Same ✓ | 20% | 0% |
3 skill files covering automotive-workflow domain.
You are an expert Scrum Master for automotive software development teams.
Scrum Framework:
Automotive Adaptations:
Sprint Structure:
You are an expert in automotive project financial management.
Capabilities:
Estimation Techniques:
SOW Structure:
You are an expert in automotive V-Model software development lifecycle.
V-Model Structure: The V-Model has two sides connected by verification and validation:
Each phase produces deliverables that are verified on the corresponding right-side phase.
Key Principles:
When to use this skill:
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | pass→pass | 10,332 | 9,701 | -6% | 1 | 1 | 0% | 1,838 | 2,226 | +21% | 0 | 0 | — |
case-02 | pass→pass | 12,286 | 10,183 | -17% | 1 | 1 | 0% | 2,109 | 2,308 | +9% | 0 | 0 | — |
case-03 | pass→pass | 9,198 | 5,918 | -36% | 1 | 1 | 0% | 1,802 | 1,632 | -9% | 0 | 0 | — |
case-04 | pass→pass | 16,387 | 14,703 | -10% | 1 | 1 | 0% | 2,673 | 3,212 | +20% | 0 | 0 | — |
case-05 | pass→pass | 14,816 | 12,469 | -16% | 1 | 1 | 0% | 2,416 | 2,687 | +11% | 0 | 0 | — |
case-06 | pass→pass | 6,185 | 3,952 | -36% | 1 | 1 | 0% | 1,090 | 1,298 | +19% | 0 | 0 | — |
case-07 | pass→pass | 4,166 | 3,563 | -14% | 1 | 1 | 0% | 772 | 1,156 | +50% | 0 | 0 | — |
case-08 | pass→pass | 2,878 | 4,750 | +65% | 1 | 1 | 0% | 677 | 1,648 | +143% | 0 | 0 | — |
case-09 | pass→pass | 5,216 | 3,558 | -32% | 1 | 1 | 0% | 860 | 1,119 | +30% | 0 | 0 | — |
case-10 | pass→pass | 5,340 | 3,337 | -38% | 1 | 1 | 0% | 894 | 1,093 | +22% | 0 | 0 | — |
case-11 | pass→pass | 5,194 | 4,509 | -13% | 1 | 1 | 0% | 840 | 1,350 | +61% | 0 | 0 | — |
case-12 | pass→pass | 13,257 | 10,979 | -17% | 1 | 1 | 0% | 2,431 | 2,489 | +2% | 0 | 0 | — |
case-13 | fail→pass | 6,686 | 2,641 | -60% | 1 | 1 | 0% | 1,157 | 1,011 | -13% | 0 | 0 | — |
case-14 | pass→pass | 11,543 | 11,536 | -0% | 1 | 1 | 0% | 1,972 | 2,308 | +17% | 0 | 0 | — |
case-15 | pass→pass | 6,899 | 5,148 | -25% | 1 | 1 | 0% | 1,228 | 1,460 | +19% | 0 | 0 | — |
case-16 | pass→pass | 7,101 | 8,778 | +24% | 1 | 1 | 0% | 1,171 | 2,010 | +72% | 0 | 0 | — |
case-17 | pass→pass | 19,715 | 18,201 | -8% | 1 | 1 | 0% | 3,602 | 3,854 | +7% | 0 | 0 | — |
case-18 | pass→pass | 7,404 | 7,724 | +4% | 1 | 1 | 0% | 1,352 | 2,018 | +49% | 0 | 0 | — |
case-19 | pass→pass | 18,840 | 15,470 | -18% | 1 | 1 | 0% | 3,141 | 3,187 | +1% | 0 | 0 | — |
case-20 | pass→pass | 16,927 | 16,800 | -1% | 1 | 1 | 0% | 3,252 | 3,850 | +18% | 0 | 0 | — |
case-21 | pass→pass | 19,977 | 20,152 | +1% | 1 | 1 | 0% | 4,666 | 5,493 | +18% | 0 | 0 | — |
case-22 | pass→pass | 24,004 | 31,962 | +33% | 1 | 1 | 0% | 4,495 | 6,745 | +50% | 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. The headline lift of +5 percentage points is the difference between those two pass rates over the 22 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.