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Get Started Free →Database development and operations workflow covering SQL, NoSQL, database design, migrations, optimization, and data engineering.
| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-09 | ✗→✓ | ▲ Improved | — | — |
| case-16 | ✗→✓ | ▲ Improved | — | — |
| case-08 | ✗→✓ | ▲ Improved | — | — |
| case-19 | ✗→✓ | ▲ Improved | — | — |
| case-10 | ✗→✓ | ▲ Improved | — | — |
Comprehensive database workflow for database design, development, optimization, migrations, and data engineering. Covers SQL, NoSQL, and modern data platforms.
Use this workflow when:
database-architect - Database architecturedatabase-design - Schema designpostgresql - PostgreSQL designnosql-expert - NoSQL designUse @database-architect to design database schemaUse @postgresql to design PostgreSQL schemaprisma-expert - Prisma ORMdatabase-migrations-sql-migrations - SQL migrationsneon-postgres - Serverless PostgresUse @prisma-expert to set up Prisma ORMUse @database-migrations-sql-migrations to create migrationsdatabase-optimizer - Database optimizationsql-optimization-patterns - SQL optimizationpostgres-best-practices - PostgreSQL optimizationUse @database-optimizer to optimize database performanceUse @sql-optimization-patterns to optimize SQL queriesdatabase-migration - Database migrationframework-migration-code-migrate - Code migrationUse @database-migration to plan database migrationdata-engineer - Data engineeringdata-engineering-data-pipeline - Data pipelinesairflow-dag-patterns - Airflow workflowsdbt-transformation-patterns - dbt transformationsUse @data-engineer to design data pipelineUse @airflow-dag-patterns to create Airflow DAGsdata-quality-frameworks - Data qualitydata-engineering-data-driven-feature - Data-driven featuresUse @data-quality-frameworks to implement data quality checksdatabase-admin - Database administrationbackup-automation - Backup automationUse @database-admin to manage database operationsSkills: postgresql, postgres-best-practices, neon-postgres, prisma-expertSkills: nosql-expert, azure-cosmos-db-pySkills: bullmq-specialist, upstash-qstashSkills: clickhouse-io, dbt-transformation-patternsdevelopment - Application developmentcloud-devops - Infrastructureai-ml - AI/ML data pipelinestesting-qa - Data testing| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-11 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-21 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-09 | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
case-16 | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
case-20 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-08 | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
case-12 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-02 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-19 | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
case-10 | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
case-14 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-07 | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
case-03 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-13 | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
case-17 | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
case-15 | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
case-22 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-05 | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
case-04 | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
case-01 | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
case-06 | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
case-18 | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
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 +64 percentage points is the difference between those two pass rates over the 22 comparable cases.
The per-case answers from this run were removed by the retention sweep, so the case table below shows the verdicts without the text either arm produced. The counts above were recorded at the time and are unaffected. Answers are now kept for 180 days.
Other measured skills in the registry, with their headline benchmark lift.