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Get Started Free →Use when you need to design or implement Kafka messaging in Spring Boot — including topic design, producer/consumer implementation, JSON serialization with Boot factory customizers, Testcontainers `@ServiceConnection` integration tests, retries and dead-letter topics, idempotency, and error handling. This should trigger for requests such as Add Kafka in Spring Boot; Review Spring Kafka consumers; Improve retries and DLT in Spring Kafka; Configure Spring Kafka topics serializers or listener conta
.claude/skills/jabrena-314-frameworks-spring-kafka/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-11 | ✗→✓ | ▲ Improved | 18% | 0% |
| case-07 | ✓→✗ | ▼ Worse | -71% | 0% |
| case-19 | ✓→✗ | ▼ Worse | 3% | 0% |
| case-25 | ✓→✗ | ▼ Worse | 7% | 0% |
| case-04 | ✓→✓ | ＝ Same ✓ | 43% | 0% |
Apply Spring 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/314-frameworks-spring-kafka.md and inspect current messaging setup before proposing changes.
Identify reliability and throughput goals and define the minimum safe set of changes.
Implement/refactor Spring Kafka configuration, producer/consumer logic, and failure handling.
Execute build/tests and summarize what changed, what was verified, and follow-up actions.
For detailed guidance, examples, and constraints, see references/314-frameworks-spring-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 | 18,982 | 5,675 | -70% | 1 | 1 | 0% | 3,258 | 636 | -80% | 0 | 0 | — |
case-02 | fail→fail | 16,297 | 6,551 | -60% | 1 | 1 | 0% | 3,164 | 650 | -79% | 0 | 0 | — |
case-03 | fail→fail | 24,169 | 5,368 | -78% | 1 | 1 | 0% | 4,621 | 570 | -88% | 0 | 0 | — |
case-04 | pass→pass | 10,773 | 15,389 | +43% | 1 | 1 | 0% | 2,077 | 2,977 | +43% | 0 | 0 | — |
case-05 | pass→pass | 10,185 | 11,294 | +11% | 1 | 1 | 0% | 1,922 | 2,255 | +17% | 0 | 0 | — |
case-06 | pass→pass | 16,607 | 18,786 | +13% | 1 | 1 | 0% | 3,094 | 3,289 | +6% | 0 | 0 | — |
case-07 | pass→fail | 13,755 | 6,308 | -54% | 1 | 1 | 0% | 2,445 | 711 | -71% | 0 | 0 | — |
case-08 | pass→pass | 7,644 | 8,614 | +13% | 1 | 1 | 0% | 1,265 | 1,891 | +49% | 0 | 0 | — |
case-09 | pass→pass | 8,602 | 6,368 | -26% | 1 | 1 | 0% | 1,482 | 1,440 | -3% | 0 | 0 | — |
case-10 | pass→pass | 4,732 | 5,435 | +15% | 1 | 1 | 0% | 806 | 1,308 | +62% | 0 | 0 | — |
case-11 | fail→pass | 6,348 | 6,236 | -2% | 1 | 1 | 0% | 1,232 | 1,449 | +18% | 0 | 0 | — |
case-12 | pass→pass | 9,647 | 6,071 | -37% | 1 | 1 | 0% | 1,724 | 1,333 | -23% | 0 | 0 | — |
case-13 | pass→pass | 5,370 | 4,113 | -23% | 1 | 1 | 0% | 925 | 949 | +3% | 0 | 0 | — |
case-14 | pass→pass | 6,991 | 7,217 | +3% | 1 | 1 | 0% | 1,111 | 1,585 | +43% | 0 | 0 | — |
case-15 | pass→pass | 10,112 | 8,421 | -17% | 1 | 1 | 0% | 1,653 | 1,742 | +5% | 0 | 0 | — |
case-16 | pass→pass | 7,281 | 7,031 | -3% | 1 | 1 | 0% | 1,288 | 1,621 | +26% | 0 | 0 | — |
case-17 | pass→pass | 7,437 | 5,771 | -22% | 1 | 1 | 0% | 1,202 | 1,331 | +11% | 0 | 0 | — |
case-18 | pass→pass | 6,814 | 4,439 | -35% | 1 | 1 | 0% | 1,245 | 1,074 | -14% | 0 | 0 | — |
case-19 | pass→fail | 8,561 | 7,150 | -16% | 1 | 1 | 0% | 1,463 | 1,511 | +3% | 0 | 0 | — |
case-20 | pass→pass | 6,096 | 7,275 | +19% | 1 | 1 | 0% | 1,132 | 1,616 | +43% | 0 | 0 | — |
case-21 | pass→pass | 8,776 | 5,055 | -42% | 1 | 1 | 0% | 1,486 | 1,172 | -21% | 0 | 0 | — |
case-22 | pass→pass | 6,560 | 7,116 | +8% | 1 | 1 | 0% | 1,090 | 1,626 | +49% | 0 | 0 | — |
case-23 | fail→fail | 8,898 | 7,901 | -11% | 1 | 1 | 0% | 1,533 | 1,325 | -14% | 0 | 0 | — |
case-24 | pass→pass | 3,826 | 3,271 | -15% | 1 | 1 | 0% | 674 | 905 | +34% | 0 | 0 | — |
case-25 | pass→fail | 7,285 | 5,950 | -18% | 1 | 1 | 0% | 1,210 | 1,298 | +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. 25 cases were attempted, and 21 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 -33 percentage points is the difference between those two pass rates over the 21 comparable cases. 3 cases got worse with the skill loaded, and they are 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.