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Get Started Free →Use when you need to design, review, or improve validation in Micronaut applications — including Bean Validation on @Controller methods, @Body @Valid, query/path parameter validation, @ConfigurationProperties validation, custom constraints, nested DTO validation, and ExceptionHandler mapping for constraint violations. This should trigger for requests such as Add validation support in Micronaut; Review Micronaut validation rules; Improve request validation in Micronaut REST APIs; Add custom valid
.claude/skills/jabrena-503-frameworks-micronaut-validation/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-05 | ✗→✓ | ▲ Improved | -8% | 0% |
| case-10 | ✗→✓ | ▲ Improved | -36% | 0% |
| case-18 | ✓→✗ | ▼ Worse | -46% | 0% |
| case-22 | ✓→✓ | ＝ Same ✓ | 7% | 0% |
| case-08 | ✓→✓ | ＝ Same ✓ | 15% | 0% |
Apply Micronaut validation best practices at HTTP API boundaries.
What is covered in this Skill?
Scope: Apply recommendations based on the reference rules and good/bad examples.
Before applying validation changes, ensure the project compiles. After improvements, run full verification.
./mvnw compile or mvn compile before applying any change./mvnw clean verify or mvn clean verify after applying improvementsRead references/503-frameworks-micronaut-validation.md and inspect the current project setup before proposing changes.
Identify requested outcomes, constraints, and the minimum safe set of changes to apply.
Implement or refactor validation-related configuration/code following the reference patterns and project conventions.
Execute appropriate build/tests and summarize what changed, what was verified, and any follow-up actions.
For detailed guidance, examples, and constraints, see references/503-frameworks-micronaut-validation.md.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-22 | pass→pass | 9,001 | 7,401 | -18% | 1 | 1 | 0% | 1,536 | 1,649 | +7% | 0 | 0 | — |
case-01 | fail→fail | 3,978 | 5,969 | +50% | 1 | 1 | 0% | 190 | 767 | +304% | 0 | 0 | — |
case-02 | fail→fail | 19,385 | 5,736 | -70% | 1 | 1 | 0% | 4,171 | 668 | -84% | 0 | 0 | — |
case-03 | fail→fail | 6,034 | 5,575 | -8% | 1 | 1 | 0% | 249 | 691 | +178% | 0 | 0 | — |
case-08 | pass→pass | 6,366 | 4,680 | -26% | 1 | 1 | 0% | 1,065 | 1,223 | +15% | 0 | 0 | — |
case-04 | pass→pass | 7,927 | 4,891 | -38% | 1 | 1 | 0% | 1,442 | 1,444 | +0% | 0 | 0 | — |
case-05 | fail→pass | 5,756 | 2,305 | -60% | 1 | 1 | 0% | 857 | 789 | -8% | 0 | 0 | — |
case-06 | pass→pass | 7,559 | 2,154 | -72% | 1 | 1 | 0% | 1,143 | 748 | -35% | 0 | 0 | — |
case-07 | pass→pass | 5,504 | 3,188 | -42% | 1 | 1 | 0% | 917 | 1,027 | +12% | 0 | 0 | — |
case-09 | pass→pass | 10,852 | 8,860 | -18% | 1 | 1 | 0% | 2,028 | 1,927 | -5% | 0 | 0 | — |
case-10 | fail→pass | 22,860 | 10,350 | -55% | 1 | 1 | 0% | 3,441 | 2,213 | -36% | 0 | 0 | — |
case-11 | pass→pass | 11,435 | 9,384 | -18% | 1 | 1 | 0% | 2,112 | 2,054 | -3% | 0 | 0 | — |
case-12 | pass→pass | 12,708 | 12,172 | -4% | 1 | 1 | 0% | 2,275 | 2,085 | -8% | 0 | 0 | — |
case-21 | fail→fail | 15,246 | 8,847 | -42% | 1 | 1 | 0% | 2,867 | 1,993 | -30% | 0 | 0 | — |
case-13 | pass→pass | 8,784 | 15,244 | +74% | 1 | 1 | 0% | 1,727 | 2,265 | +31% | 0 | 0 | — |
case-14 | pass→pass | 13,489 | 12,686 | -6% | 1 | 1 | 0% | 2,383 | 2,327 | -2% | 0 | 0 | — |
case-15 | pass→pass | 7,318 | 4,997 | -32% | 1 | 1 | 0% | 1,247 | 1,266 | +2% | 0 | 0 | — |
case-16 | pass→pass | 3,303 | 3,816 | +16% | 1 | 1 | 0% | 539 | 981 | +82% | 0 | 0 | — |
case-17 | pass→pass | 5,194 | 4,252 | -18% | 1 | 1 | 0% | 900 | 1,132 | +26% | 0 | 0 | — |
case-18 | pass→fail | 10,130 | 2,614 | -74% | 1 | 1 | 0% | 1,538 | 825 | -46% | 0 | 0 | — |
case-19 | pass→pass | 13,161 | 7,362 | -44% | 1 | 1 | 0% | 2,011 | 1,557 | -23% | 0 | 0 | — |
case-20 | fail→fail | 13,946 | 5,901 | -58% | 1 | 1 | 0% | 2,762 | 727 | -74% | 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. 2 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.