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Get Started Free →CAN/CAN-FD bus analysis and development expertise
.claude/skills/a5c-ai-can-bus/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-09 | ✗→✓ | ▲ Improved | -59% | 0% |
| case-10 | ✗→✓ | ▲ Improved | -57% | 0% |
| case-11 | ✗→✓ | ▲ Improved | -54% | 0% |
| case-12 | ✗→✓ | ▲ Improved | -51% | 0% |
| case-20 | ✗→✓ | ▲ Improved | -66% | 0% |
This skill provides comprehensive CAN and CAN-FD bus analysis, development, and debugging capabilities for automotive and industrial embedded systems.
device-driver-development.js - CAN driver implementationsignal-integrity-testing.js - CAN bus signal validationhw-sw-interface-specification.js - CAN interface definitionfunctional-safety-certification.js - CAN safety requirementsThis skill is invoked when tasks require:
yamlcan: bitrate: 500000 # 500 kbps sample_point: 87.5 sjw: 1 seg1: 13 seg2: 2 prescaler: 4
yamlcan_fd: nominal_bitrate: 500000 data_bitrate: 2000000 brs: enabled # Bit Rate Switch esi: enabled # Error State Indicator
dbcBO_ 0x123 EngineData: 8 ECU SG_ EngineRPM : 0|16@1+ (0.25,0) [0|16383.75] "rpm" Vector__XXX SG_ EngineTemp : 16|8@1+ (1,-40) [-40|215] "C" Vector__XXX
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 15,277 | 24,255 | +59% | 1 | 1 | 0% | 3,095 | 3,518 | +14% | 0 | 0 | — |
case-02 | pass→pass | 16,645 | 15,399 | -7% | 1 | 1 | 0% | 3,071 | 3,623 | +18% | 0 | 0 | — |
case-03 | pass→pass | 10,501 | 11,593 | +10% | 1 | 1 | 0% | 2,173 | 3,025 | +39% | 0 | 0 | — |
case-04 | pass→pass | 6,838 | 6,990 | +2% | 1 | 1 | 0% | 1,348 | 2,069 | +53% | 0 | 0 | — |
case-05 | pass→pass | 9,894 | 10,307 | +4% | 1 | 1 | 0% | 1,810 | 2,528 | +40% | 0 | 0 | — |
case-06 | pass→pass | 3,481 | 5,669 | +63% | 1 | 1 | 0% | 594 | 1,685 | +184% | 0 | 0 | — |
case-07 | pass→pass | 13,186 | 19,996 | +52% | 1 | 1 | 0% | 2,240 | 4,343 | +94% | 0 | 0 | — |
case-08 | pass→pass | 9,849 | 6,081 | -38% | 1 | 1 | 0% | 1,863 | 1,655 | -11% | 0 | 0 | — |
case-09 | fail→pass | 24,435 | 2,691 | -89% | 1 | 1 | 0% | 2,820 | 1,148 | -59% | 0 | 0 | — |
case-10 | fail→pass | 15,492 | 2,697 | -83% | 1 | 1 | 0% | 2,129 | 919 | -57% | 0 | 0 | — |
case-11 | fail→pass | 13,714 | 2,758 | -80% | 1 | 1 | 0% | 2,262 | 1,042 | -54% | 0 | 0 | — |
case-12 | fail→pass | 15,086 | 3,383 | -78% | 1 | 1 | 0% | 2,521 | 1,224 | -51% | 0 | 0 | — |
case-13 | pass→pass | 16,345 | 16,314 | -0% | 1 | 1 | 0% | 2,527 | 2,977 | +18% | 0 | 0 | — |
case-14 | pass→pass | 11,682 | 15,696 | +34% | 1 | 1 | 0% | 2,447 | 3,820 | +56% | 0 | 0 | — |
case-15 | pass→pass | 8,274 | 8,041 | -3% | 1 | 1 | 0% | 1,593 | 2,238 | +40% | 0 | 0 | — |
case-16 | pass→pass | 8,527 | 6,385 | -25% | 1 | 1 | 0% | 1,289 | 1,766 | +37% | 0 | 0 | — |
case-17 | pass→pass | 6,901 | 8,958 | +30% | 1 | 1 | 0% | 1,137 | 2,224 | +96% | 0 | 0 | — |
case-18 | pass→pass | 10,660 | 9,629 | -10% | 1 | 1 | 0% | 2,014 | 2,089 | +4% | 0 | 0 | — |
case-19 | pass→pass | 19,661 | 15,583 | -21% | 1 | 1 | 0% | 3,116 | 3,300 | +6% | 0 | 0 | — |
case-20 | fail→pass | 33,566 | 7,975 | -76% | 1 | 1 | 0% | 6,981 | 2,372 | -66% | 0 | 0 | — |
case-21 | pass→pass | 9,369 | 5,008 | -47% | 1 | 1 | 0% | 1,726 | 1,619 | -6% | 0 | 0 | — |
case-22 | fail→fail | 31,000 | 25,786 | -17% | 1 | 1 | 0% | 5,085 | 6,273 | +23% | 0 | 0 | — |
case-23 | fail→fail | 46,642 | 28,742 | -38% | 1 | 1 | 0% | 4,762 | 6,169 | +30% | 0 | 0 | — |
case-24 | fail→fail | 24,080 | 24,687 | +3% | 1 | 1 | 0% | 3,900 | 5,806 | +49% | 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. 24 cases were attempted. The headline lift of +21 percentage points is the difference between those two pass rates over the 24 comparable cases. 1 case got worse with the skill loaded, and it is included in that figure.
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.