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Found 14 Skills
Use when SwiftData migrations crash, fail to preserve relationships, lose data, or work in simulator but fail on device - systematic diagnostics for schema version mismatches, relationship errors, and migration testing gaps
Database schema design, optimization, and migration patterns for PostgreSQL, MySQL, and NoSQL databases. Use for designing schemas, writing migrations, or optimizing queries.
Adds schema tests and data quality validation to dbt models. Use when working with dbt tests for: (1) Adding or modifying tests in schema.yml files (2) Task mentions "test", "validate", "data quality", "unique", "not_null", or "accepted_values" (3) Ensuring data integrity - primary keys, foreign keys, relationships (4) Debugging test failures or understanding why dbt test failed Matches existing project test patterns and YAML style before adding new tests.
Guidance for data resharding tasks that involve reorganizing files across directory structures with constraints on file sizes and directory contents. This skill applies when redistributing datasets, splitting large files, or reorganizing data into shards while maintaining constraints like maximum files per directory or maximum file sizes. Use when tasks involve resharding, data partitioning, or directory-constrained file reorganization.
Use when backing up, restoring, or validating golden datasets. Prevents data loss and ensures test data integrity for AI/ML evaluation systems.
Standards and best practices for writing LookML tests to ensure data integrity, accuracy, and logic validation.
Error handling patterns for ERPNext/Frappe database operations. Use when handling DoesNotExistError, DuplicateEntryError, transaction failures, and query errors. Covers retry patterns and data integrity. V14/V15/V16 compatible. Triggers: database error, DoesNotExistError, DuplicateEntryError, transaction failed, query error.
Idempotent Redundancy
Verify accounting integrity. Compare totals to source docs, check lots vs holdings, detect duplicates, report gaps.
Test database interactions, schemas, and data integrity
Apply Benford's Law to detect anomalies in numerical datasets by analyzing first-digit frequency distributions. Use this skill when the user needs to audit financial data for fraud indicators, validate data integrity, or detect fabricated numbers — even if they say 'data manipulation detection', 'first digit test', or 'accounting fraud screening'.
Guide for using molt verify to compare source and target databases for schema and row-level consistency after a migration. Use when running verify commands, tuning concurrency/sharding, handling schema mismatches, or validating data integrity post-migration.