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Data · Version 1.4.0 · Reviewed 2026-08-02

dbt Model Reviewer

Make data systems more correct and operable for model-grain review and incremental correctness with evidence, explicit trade-offs, and a verification plan.

4 method steps 4 documented failure modes 4 diagnostic checks 7 quality gates

Reviews dbt model grain, tests, incremental logic, lineage, naming, source freshness, and warehouse-cost implications.

₹199 one-time

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What this skill helps you do

  • Model-grain review
  • Incremental correctness
  • Data-test coverage

How dbt Model Reviewer works

You provide

Schema, volumes, and the queries that actually run

It inspects

Access patterns and skew affecting model-grain review

It decides

A incremental correctness design with migration ordering

You verify

Row counts and checksums compared before cutover

What it checks first

dbt Model Reviewer reviews dbt model grain, tests, incremental logic, lineage, naming, source freshness, and warehouse-cost implications. Use it when the work involves Model-grain review, Incremental correctness, Data-test coverage.

  1. The real access patterns, since schema quality is meaningless without the queries it serves.
  2. Cardinality, skew, and growth rate of the key columns.
  3. Consistency and freshness requirements per consumer.
  4. Whether the model preserves history or overwrites it, which determines what questions remain answerable.

Failure modes it recognizes

  • A schema optimized for writes that makes the dominant read pattern a full scan.
  • Skewed partitioning concentrating load on one node.
  • Nullable columns encoding several meanings, making every downstream query ambiguous.
  • A pipeline silently dropping late-arriving records and under-reporting totals.

Answers it will reject

  • Normalizing or denormalizing by preference rather than by measured access pattern.
  • Adding a column for each new requirement until the table encodes multiple entities.
  • Backfilling without a reconciliation check that proves completeness.

Decision rules it applies

  • Design from the read path backward for read-heavy systems.
  • Make invalid states unrepresentable with constraints rather than conventions.
  • Version the schema and treat consumer compatibility as a contract.

Evidence it asks for

  • Profile real query patterns and volumes before proposing a change.
  • Validate migrations with row counts, checksums, and spot comparisons.
  • Monitor freshness and completeness as first-class data-quality signals.

The method inside

  1. Map the artifact, actors, boundaries, and invariants relevant to model-grain review.
  2. Trace concrete failure or abuse paths for incremental correctness; do not report checklist items without a mechanism.
  3. Prioritize data-test coverage findings by impact, likelihood, confidence, and cost of correction.
  4. Recommend the smallest defensible change, then define how an independent reviewer can verify it.

Deliverables

  • Model-grain review assessment
  • Incremental correctness decision and action plan
  • Data-test coverage verification checklist

Evidence requirements

  • Schema, access patterns, query plans, or event contracts
  • Volume, cardinality, retention, and freshness
  • Consistency, latency, and migration constraints

Quality gates

  • Every material claim traces to supplied evidence or is labeled as a hypothesis.
  • The response follows the declared deliverable contract.
  • No execution, access, measurement, or verification is invented.
  • Secrets and personal data are redacted rather than repeated.
  • The user receives a concrete independent verification step.
  • The relevant failure modes in this domain were considered rather than only the reported symptom.
  • No listed anti-pattern was recommended as a solution.

Example task

Input

Review this incremental customer-revenue model; totals drift after late-arriving payment corrections.

Expected output

The model filters only on created_at, so updates to old payments never re-enter the incremental window. Use the source updated_at watermark with overlap and assert one row per customer at the declared grain...

Boundaries and compatibility

Ideal for

  • Model-grain review: produce a decision or artifact grounded in supplied evidence.
  • Incremental correctness: produce a decision or artifact grounded in supplied evidence.
  • Data-test coverage: produce a decision or artifact grounded in supplied evidence.

Out of scope

  • Recommending indexes without a workload
  • Treating eventual consistency as universally acceptable

Agent compatibility

  • GitHub Copilot Agent Skills
  • Cursor Agent Skills
  • Claude Code Skills
  • OpenAI Codex Skills
  • JetBrains Junie Skills

Tool policy: Advisory by default. No tools are assumed. If the host provides tools, use read-only evidence gathering unless the user explicitly approves a scoped write or execution action.