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Get Started Free →Use when you need Kafka messaging in Micronaut — including @KafkaClient and @KafkaListener design, @Serdeable serialization, topic/partition strategy, TestPropertyProvider integration tests, retries and dead-letter processing, and error handling. This should trigger for requests such as Add Kafka in Micronaut; Review Micronaut Kafka listeners; Improve retry and failure handling for Micronaut Kafka; Configure Micronaut Kafka clients listeners or SerDes; Add Micronaut Kafka retry or dead-letter ha
.claude/skills/jabrena-514-frameworks-micronaut-kafka/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-17 | ✗→✓ | ▲ Improved | 17% | 0% |
| case-14 | ✓→✓ | ＝ Same ✓ | -14% | 0% |
| case-05 | ✓→✓ | ＝ Same ✓ | -51% | 0% |
| case-06 | ✓→✓ | ＝ Same ✓ | 9% | 0% |
| case-07 | ✓→✓ | ＝ Same ✓ | -35% | 0% |
Apply Micronaut Kafka guidance with concrete examples for design, implementation, and error handling.
Compile before messaging refactors; verify after changes.
./mvnw compile or mvn compile before applying any change./mvnw clean verify or mvn clean verify after applying improvementsRead references/514-frameworks-micronaut-kafka.md and inspect current messaging setup before proposing changes.
Identify delivery guarantees and resilience requirements to define safe improvements.
Implement/refactor clients, listeners, and failure strategies in Micronaut Kafka.
Execute build/tests and summarize what changed, what was verified, and follow-up actions.
For detailed guidance, examples, and constraints, see references/514-frameworks-micronaut-kafka.md.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 4,417 | 5,229 | +18% | 1 | 1 | 0% | 187 | 651 | +248% | 0 | 0 | — |
case-02 | fail→fail | 4,351 | 2,678 | -38% | 1 | 1 | 0% | 179 | 624 | +249% | 0 | 0 | — |
case-03 | fail→fail | 6,587 | 5,179 | -21% | 1 | 1 | 0% | 412 | 522 | +27% | 0 | 0 | — |
case-14 | pass→pass | 8,555 | 6,265 | -27% | 1 | 1 | 0% | 1,640 | 1,413 | -14% | 0 | 0 | — |
case-04 | fail→fail | 9,036 | 2,807 | -69% | 1 | 1 | 0% | 1,558 | 727 | -53% | 0 | 0 | — |
case-05 | pass→pass | 8,735 | 2,488 | -72% | 1 | 1 | 0% | 1,474 | 715 | -51% | 0 | 0 | — |
case-06 | pass→pass | 7,381 | 6,191 | -16% | 1 | 1 | 0% | 1,319 | 1,444 | +9% | 0 | 0 | — |
case-07 | pass→pass | 8,865 | 3,863 | -56% | 1 | 1 | 0% | 1,536 | 1,002 | -35% | 0 | 0 | — |
case-13 | fail→fail | 14,082 | 6,350 | -55% | 1 | 1 | 0% | 2,320 | 603 | -74% | 0 | 0 | — |
case-08 | pass→pass | 5,253 | 5,469 | +4% | 1 | 1 | 0% | 971 | 1,345 | +39% | 0 | 0 | — |
case-09 | pass→pass | 5,382 | 3,743 | -30% | 1 | 1 | 0% | 1,051 | 929 | -12% | 0 | 0 | — |
case-10 | pass→pass | 10,847 | 6,555 | -40% | 1 | 1 | 0% | 1,946 | 1,727 | -11% | 0 | 0 | — |
case-11 | pass→pass | 6,378 | 3,193 | -50% | 1 | 1 | 0% | 944 | 879 | -7% | 0 | 0 | — |
case-12 | pass→pass | 5,038 | 5,691 | +13% | 1 | 1 | 0% | 811 | 1,420 | +75% | 0 | 0 | — |
case-15 | pass→pass | 9,527 | 5,623 | -41% | 1 | 1 | 0% | 1,680 | 1,330 | -21% | 0 | 0 | — |
case-16 | pass→pass | 14,584 | 9,447 | -35% | 1 | 1 | 0% | 2,400 | 1,422 | -41% | 0 | 0 | — |
case-17 | fail→pass | 5,819 | 4,755 | -18% | 1 | 1 | 0% | 1,021 | 1,198 | +17% | 0 | 0 | — |
case-18 | fail→fail | 25,411 | 6,974 | -73% | 1 | 1 | 0% | 3,170 | 1,470 | -54% | 0 | 0 | — |
case-19 | pass→pass | 7,356 | 4,266 | -42% | 1 | 1 | 0% | 1,373 | 1,028 | -25% | 0 | 0 | — |
case-20 | pass→pass | 13,930 | 10,945 | -21% | 1 | 1 | 0% | 2,274 | 2,411 | +6% | 0 | 0 | — |
case-21 | pass→pass | 7,637 | 5,711 | -25% | 1 | 1 | 0% | 1,343 | 1,320 | -2% | 0 | 0 | — |
case-22 | pass→pass | 5,611 | 7,336 | +31% | 1 | 1 | 0% | 1,009 | 1,166 | +16% | 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 18 counted toward the lift figure. The other 4 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 +5 percentage points is the difference between those two pass rates over the 18 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.