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Get Started Free →Cross-platform offline-first data management
.claude/skills/a5c-ai-mobile-offline-storage/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | 44% | 0% |
| case-02 | ✗→✓ | ▲ Improved | 0% | 0% |
| case-06 | ✗→✓ | ▲ Improved | 31% | 0% |
| case-17 | ✗→✓ | ▲ Improved | 81% | 0% |
| case-03 | ✓→✓ | = Same ✓ | 114% | 0% |
This skill provides cross-platform offline-first data management capabilities. It enables configuration of WatermelonDB, Realm, MMKV, and other offline storage solutions with sync queue architectures.
bash - Execute package managers and build toolsread - Analyze storage configurations and schemaswrite - Generate models and sync logicedit - Update storage implementationsglob - Search for storage filesgrep - Search for patternsoffline-first-architecture.js - Offline patternsrest-api-integration.js - API syncgraphql-apollo-integration.js - GraphQL synctypescript// database/schema.ts import { appSchema, tableSchema } from '@nozbe/watermelondb'; export const schema = appSchema({ version: 1, tables: [ tableSchema({ name: 'posts', columns: [ { name: 'title', type: 'string' }, { name: 'body', type: 'string' }, { name: 'is_published', type: 'boolean' }, { name: 'author_id', type: 'string', isIndexed: true }, { name: 'created_at', type: 'number' }, { name: 'updated_at', type: 'number' }, ], }), tableSchema({ name: 'comments', columns: [ { name: 'body', type: 'string' }, { name: 'post_id', type: 'string', isIndexed: true }, { name: 'author_id', type: 'string' }, { name: 'created_at', type: 'number' }, ], }), ], });
typescript// database/models/Post.ts import { Model, Q } from '@nozbe/watermelondb'; import { field, date, children, relation } from '@nozbe/watermelondb/decorators'; export class Post extends Model { static table = 'posts'; static associations = { comments: { type: 'has_many', foreignKey: 'post_id' }, author: { type: 'belongs_to', key: 'author_id' }, }; @field('title') title!: string; @field('body') body!: string; @field('is_published') isPublished!: boolean; @field('author_id') authorId!: string; @date('created_at') createdAt!: Date; @date('updated_at') updatedAt!: Date; @children('comments') comments!: Query<Comment>; @relation('users', 'author_id') author!: Relation<User>; }
typescript// sync/SyncQueue.ts interface SyncOperation { id: string; type: 'create' | 'update' | 'delete'; entity: string; payload: any; timestamp: number; retryCount: number; } class SyncQueue { private queue: SyncOperation[] = []; private isProcessing = false; async enqueue(operation: Omit<SyncOperation, 'id' | 'timestamp' | 'retryCount'>) { const op: SyncOperation = { ...operation, id: uuid(), timestamp: Date.now(), retryCount: 0, }; this.queue.push(op); await this.persistQueue(); this.processQueue(); } private async processQueue() { if (this.isProcessing || this.queue.length === 0) return; const isOnline = await NetInfo.fetch().then(state => state.isConnected); if (!isOnline) return; this.isProcessing = true; while (this.queue.length > 0) { const operation = this.queue[0]; try { await this.executeOperation(operation); this.queue.shift(); await this.persistQueue(); } catch (error) { operation.retryCount++; if (operation.retryCount >= 3) { this.queue.shift(); await this.logFailedOperation(operation, error); } break; } } this.isProcessing = false; } private async executeOperation(operation: SyncOperation) { switch (operation.type) { case 'create': return api.post(`/${operation.entity}`, operation.payload); case 'update': return api.put(`/${operation.entity}/${operation.payload.id}`, operation.payload); case 'delete': return api.delete(`/${operation.entity}/${operation.payload.id}`); } } }
ios-persistence - iOS Core Dataandroid-room - Android Roomgraphql-mobile - GraphQL offline| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→pass | 23,158 | 39,078 | +69% | 1 | 1 | 0% | 4,974 | 7,140 | +44% | 0 | 0 | — |
case-02 | fail→pass | 12,365 | 5,675 | -54% | 1 | 1 | 0% | 2,780 | 2,767 | -0% | 0 | 0 | — |
case-03 | pass→pass | 10,647 | 15,786 | +48% | 1 | 1 | 0% | 2,320 | 4,963 | +114% | 0 | 0 | — |
case-04 | pass→pass | 18,221 | 16,267 | -11% | 1 | 1 | 0% | 3,717 | 4,900 | +32% | 0 | 0 | — |
case-05 | pass→pass | 11,016 | 10,754 | -2% | 1 | 1 | 0% | 2,029 | 3,719 | +83% | 0 | 0 | — |
case-06 | fail→pass | 15,478 | 11,459 | -26% | 1 | 1 | 0% | 2,839 | 3,710 | +31% | 0 | 0 | — |
case-07 | pass→pass | 7,763 | 8,526 | +10% | 1 | 1 | 0% | 1,394 | 3,117 | +124% | 0 | 0 | — |
case-08 | pass→pass | 14,415 | 14,443 | +0% | 1 | 1 | 0% | 2,865 | 4,312 | +51% | 0 | 0 | — |
case-09 | pass→pass | 10,533 | 19,182 | +82% | 1 | 1 | 0% | 1,958 | 5,472 | +179% | 0 | 0 | — |
case-10 | pass→pass | 9,541 | 5,457 | -43% | 1 | 1 | 0% | 1,661 | 2,314 | +39% | 0 | 0 | — |
case-11 | pass→pass | 9,077 | 6,780 | -25% | 1 | 1 | 0% | 1,775 | 2,966 | +67% | 0 | 0 | — |
case-12 | pass→pass | 16,283 | 18,271 | +12% | 1 | 1 | 0% | 3,329 | 4,424 | +33% | 0 | 0 | — |
case-13 | pass→pass | 14,669 | 12,585 | -14% | 1 | 1 | 0% | 2,489 | 3,809 | +53% | 0 | 0 | — |
case-14 | pass→pass | 13,732 | 12,769 | -7% | 1 | 1 | 0% | 2,140 | 4,163 | +95% | 0 | 0 | — |
case-15 | pass→pass | 16,129 | 15,160 | -6% | 1 | 1 | 0% | 2,763 | 4,391 | +59% | 0 | 0 | — |
case-16 | pass→pass | 4,316 | 5,005 | +16% | 1 | 1 | 0% | 869 | 2,492 | +187% | 0 | 0 | — |
case-17 | fail→pass | 12,015 | 19,321 | +61% | 1 | 1 | 0% | 2,634 | 4,762 | +81% | 0 | 0 | — |
case-18 | pass→pass | 11,438 | 14,916 | +30% | 1 | 1 | 0% | 1,908 | 3,709 | +94% | 0 | 0 | — |
case-19 | pass→pass | 14,570 | 8,899 | -39% | 1 | 1 | 0% | 2,771 | 3,210 | +16% | 0 | 0 | — |
case-20 | pass→pass | 4,508 | 3,594 | -20% | 1 | 1 | 0% | 821 | 2,191 | +167% | 0 | 0 | — |
case-21 | pass→pass | 10,312 | 7,446 | -28% | 1 | 1 | 0% | 2,025 | 2,795 | +38% | 0 | 0 | — |
case-22 | pass→pass | 10,921 | 11,183 | +2% | 1 | 1 | 0% | 1,804 | 3,765 | +109% | 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 +18 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.