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Get Started Free →Implement Room schemas and DataStore preferences with proper async patterns in Android. Use when the primary task is storage schema, DAO, migration, or preference isolation; defer auth-token/security storage, any CoroutineWorker/WorkManager task, Hilt graph wiring, and cache-policy design.
.claude/skills/hoangnguyen0403-android-persistence/SKILL.md| Model | Eval pass | Runs |
|---|---|---|
| gemini-3.6-flash | 100% | 12 |
| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-11 | ✗→✓ | ▲ Improved | -37% | 0% |
| case-17 | ✗→✓ | ▲ Improved | 44% | 0% |
| case-01 | ✓→✓ | = Same ✓ | -21% | 0% |
| case-02 | ✓→✓ | = Same ✓ | -48% | 0% |
| case-03 | ✓→✓ | = Same ✓ | -33% | 0% |
Flow<List<T>> for queries, use suspend for Write/Insert.@Entity data classes simple. Map to Domain models in Repository.@Transaction for multi-table queries (Relations).See DAO templates for Room DAO patterns.
SharedPreferences with ProtoDataStore (type-safe) or PreferencesDataStore.See DAO templates for DataStore migration patterns.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | pass→pass | 11,986 | 8,270 | -31% | 1 | 1 | 0% | 2,532 | 1,999 | -21% | 0 | 0 | — |
case-02 | pass→pass | 9,235 | 3,812 | -59% | 1 | 1 | 0% | 1,671 | 869 | -48% | 0 | 0 | — |
case-03 | pass→pass | 4,671 | 2,052 | -56% | 1 | 1 | 0% | 844 | 566 | -33% | 0 | 0 | — |
case-04 | pass→pass | 4,280 | 2,522 | -41% | 1 | 1 | 0% | 643 | 606 | -6% | 0 | 0 | — |
case-05 | pass→pass | 9,513 | 5,306 | -44% | 1 | 1 | 0% | 1,952 | 1,337 | -32% | 0 | 0 | — |
case-06 | pass→pass | 6,514 | 4,457 | -32% | 1 | 1 | 0% | 1,214 | 1,037 | -15% | 0 | 0 | — |
case-07 | pass→pass | 14,181 | 8,617 | -39% | 1 | 1 | 0% | 2,167 | 2,049 | -5% | 0 | 0 | — |
case-08 | pass→pass | 8,808 | 2,802 | -68% | 1 | 1 | 0% | 1,317 | 674 | -49% | 0 | 0 | — |
case-18 | pass→pass | 12,373 | 6,136 | -50% | 1 | 1 | 0% | 2,044 | 1,241 | -39% | 0 | 0 | — |
case-09 | pass→pass | 3,483 | 3,670 | +5% | 1 | 1 | 0% | 582 | 976 | +68% | 0 | 0 | — |
case-10 | pass→pass | 5,298 | 2,667 | -50% | 1 | 1 | 0% | 1,014 | 682 | -33% | 0 | 0 | — |
case-11 | fail→pass | 10,253 | 5,274 | -49% | 1 | 1 | 0% | 1,821 | 1,142 | -37% | 0 | 0 | — |
case-12 | pass→pass | 15,180 | 6,306 | -58% | 1 | 1 | 0% | 2,381 | 1,394 | -41% | 0 | 0 | — |
case-17 | fail→pass | 2,766 | 2,404 | -13% | 1 | 1 | 0% | 450 | 649 | +44% | 0 | 0 | — |
case-13 | pass→pass | 8,997 | 2,796 | -69% | 1 | 1 | 0% | 1,699 | 731 | -57% | 0 | 0 | — |
case-14 | pass→pass | 8,565 | 5,587 | -35% | 1 | 1 | 0% | 1,845 | 1,363 | -26% | 0 | 0 | — |
case-15 | pass→pass | 11,115 | 5,737 | -48% | 1 | 1 | 0% | 1,901 | 1,233 | -35% | 0 | 0 | — |
case-16 | pass→pass | 10,060 | 3,654 | -64% | 1 | 1 | 0% | 1,521 | 799 | -47% | 0 | 0 | — |
case-19 | pass→pass | 10,139 | 6,999 | -31% | 1 | 1 | 0% | 1,907 | 1,484 | -22% | 0 | 0 | — |
case-20 | pass→pass | 12,566 | 8,283 | -34% | 1 | 1 | 0% | 2,436 | 1,850 | -24% | 0 | 0 | — |
case-21 | pass→pass | 11,674 | 6,148 | -47% | 1 | 1 | 0% | 2,056 | 1,409 | -31% | 0 | 0 | — |
case-22 | pass→pass | 11,482 | 8,304 | -28% | 1 | 1 | 0% | 2,391 | 1,996 | -17% | 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. The headline lift of +9 percentage points is the difference between those two pass rates over the 22 comparable cases.
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.