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Get Started Free →Use when you need to design or implement MongoDB data access in Spring Boot — including document modeling, Spring Data Mongo repositories/templates, indexing, optimistic concurrency, and error handling. This should trigger for requests such as Add MongoDB in Spring Boot; Review Spring Data Mongo design; Improve error handling for Mongo writes; Model MongoDB documents for a Spring Boot service; Configure Spring Data MongoDB indexes or transactions. Part of Plinth Toolkit
.claude/skills/jabrena-315-frameworks-spring-mongodb/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-11 | ✗→✓ | ▲ Improved | 10% | 0% |
| case-05 | ✗→✓ | ▲ Improved | -21% | 0% |
| case-06 | ✗→✓ | ▲ Improved | -12% | 0% |
| case-07 | ✗→✓ | ▲ Improved | -18% | 0% |
| case-08 | ✗→✓ | ▲ Improved | -1% | 0% |
Apply Spring Data MongoDB guidance with concrete examples for design, implementation, and error handling.
Compile before MongoDB refactors; verify after changes.
./mvnw compile or mvn compile before applying any change./mvnw clean verify or mvn clean verify after applying improvementsRead references/315-frameworks-spring-mongodb.md and inspect persistence setup before proposing changes.
Identify data model, consistency, and query requirements to define safe improvements.
Implement/refactor mappings, repositories, indexes, and failure handling policies.
Execute build/tests and summarize what changed, what was verified, and follow-up actions.
For detailed guidance, examples, and constraints, see references/315-frameworks-spring-mongodb.md.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-11 | fail→pass | 11,591 | 10,780 | -7% | 1 | 1 | 0% | 2,128 | 2,339 | +10% | 0 | 0 | — |
case-10 | fail→fail | 12,019 | 5,691 | -53% | 1 | 1 | 0% | 2,102 | 1,295 | -38% | 0 | 0 | — |
case-01 | fail→fail | 5,029 | 4,432 | -12% | 1 | 1 | 0% | 177 | 665 | +276% | 0 | 0 | — |
case-02 | fail→fail | 19,608 | 6,015 | -69% | 1 | 1 | 0% | 4,834 | 618 | -87% | 0 | 0 | — |
case-03 | fail→fail | 22,606 | 6,307 | -72% | 1 | 1 | 0% | 4,448 | 547 | -88% | 0 | 0 | — |
case-04 | fail→fail | 12,414 | 3,308 | -73% | 1 | 1 | 0% | 2,393 | 717 | -70% | 0 | 0 | — |
case-05 | fail→pass | 11,477 | 5,781 | -50% | 1 | 1 | 0% | 2,183 | 1,723 | -21% | 0 | 0 | — |
case-06 | fail→pass | 35,942 | 5,976 | -83% | 1 | 1 | 0% | 1,609 | 1,422 | -12% | 0 | 0 | — |
case-07 | fail→pass | 8,586 | 5,743 | -33% | 1 | 1 | 0% | 1,680 | 1,376 | -18% | 0 | 0 | — |
case-08 | fail→pass | 14,523 | 12,296 | -15% | 1 | 1 | 0% | 2,653 | 2,627 | -1% | 0 | 0 | — |
case-09 | fail→pass | 8,988 | 6,607 | -26% | 1 | 1 | 0% | 1,989 | 1,590 | -20% | 0 | 0 | — |
case-12 | pass→pass | 6,691 | 2,420 | -64% | 1 | 1 | 0% | 1,022 | 746 | -27% | 0 | 0 | — |
case-13 | fail→pass | 14,508 | 7,763 | -46% | 1 | 1 | 0% | 1,992 | 1,653 | -17% | 0 | 0 | — |
case-14 | fail→fail | 9,591 | 5,497 | -43% | 1 | 1 | 0% | 1,784 | 649 | -64% | 0 | 0 | — |
case-15 | pass→pass | 14,114 | 7,251 | -49% | 1 | 1 | 0% | 2,412 | 1,792 | -26% | 0 | 0 | — |
case-16 | fail→pass | 6,621 | 5,958 | -10% | 1 | 1 | 0% | 1,221 | 1,192 | -2% | 0 | 0 | — |
case-17 | fail→pass | 36,787 | 16,329 | -56% | 1 | 1 | 0% | 4,033 | 3,418 | -15% | 0 | 0 | — |
case-18 | fail→pass | 6,781 | 4,754 | -30% | 1 | 1 | 0% | 1,292 | 1,233 | -5% | 0 | 0 | — |
case-19 | fail→pass | 9,339 | 7,995 | -14% | 1 | 1 | 0% | 1,554 | 1,809 | +16% | 0 | 0 | — |
case-20 | pass→pass | 9,386 | 16,130 | +72% | 1 | 1 | 0% | 1,838 | 2,438 | +33% | 0 | 0 | — |
case-21 | pass→pass | 15,379 | 12,077 | -21% | 1 | 1 | 0% | 2,828 | 2,491 | -12% | 0 | 0 | — |
case-22 | pass→pass | 10,036 | 9,826 | -2% | 1 | 1 | 0% | 1,847 | 2,101 | +14% | 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 +50 percentage points is the difference between those two pass rates over the 18 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.