yeaight7

Audit whether a dbt incremental model uses the right incremental strategy for the repo, the data shape, and the operational constraints.

Deterministic 3-cycle loop for gathering documentation context before writing or updating docs. Broad search → exact source and existing docs → project conventions and setup.

Analyzes ML training scripts to enforce seed setting, deterministic operations, and environment tracking for exact reproducibility.

Use when validating rendered web pages, local dev servers, browser automation, screenshots, forms, auth sessions, or UI evidence with strict browser safety boundaries.

Use when creating or modifying dimensional dbt models in warehouse-backed analytics projects. Covers a four-layer warehouse architecture (sources/staging/core/marts), naming conventions, no-alias SQL rule, surrogate-key and missing-record patterns, incremental strategies, deduplication, and common project macros. Use when building fact tables, dimension tables, staging models, writing SQL, or desi

You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.

Ensure that new complex models actually outperform simple, naive baselines.

Vendor-neutral routing guide for choosing the right model tier by task type. Mechanical work uses a smaller/faster model; implementation uses a standard model; architecture, security, and release audit use the most capable model.

Use when you have a spec or requirements for a multi-step task, before touching code.

Use to identify and safely delete unused functions, classes, exports, and files.

Use when adding or reviewing data quality tests for dbt models in warehouse-backed analytics projects. Covers dbt generic tests, singular tests (assert_*.sql), accepted-values macros, dbt_utils patterns, cross-system consistency tests, and warehouse-oriented validation. Use when writing data tests, creating assert_*.sql files, testing business logic, or validating referential integrity.

Use to collapse over-engineered abstractions, remove unnecessary layers, or consolidate redundant logic.