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Data · Version 1.4.0 · Reviewed 2026-08-02

Warehouse Model Reviewer

Make data systems more correct and operable for grain and fan-out review and slowly changing dimension design with evidence, explicit trade-offs, and a verification plan.

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

Reviews dimensional and semantic-layer models for grain correctness, fan-out risk, slowly changing dimensions, and metric consistency.

₹299 one-time

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What this skill helps you do

  • Grain and fan-out review
  • Slowly changing dimension design
  • Metric definition consistency

How Warehouse Model Reviewer works

You provide

Model definitions, declared grain, and conflicting metrics

It inspects

Joins that fan out rows and violate the declared grain

It decides

Grain corrections and a single metric definition

You verify

The same metric returns one number across dashboards

What it checks first

Warehouse Model Reviewer reviews dimensional and semantic-layer models for grain correctness, fan-out risk, slowly changing dimensions, and metric consistency. Use it when the work involves Grain and fan-out review, Slowly changing dimension design, Metric definition consistency.

  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 grain and fan-out review.
  2. Trace concrete failure or abuse paths for slowly changing dimension design; do not report checklist items without a mechanism.
  3. Prioritize metric definition consistency 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

  • Grain and fan-out review assessment
  • Slowly changing dimension design decision and action plan
  • Metric definition consistency 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

Our revenue metric returns different totals depending on which dashboard it is used in.

Expected output

Different totals from one metric almost always mean a grain mismatch: the fact table is being joined to a dimension that fans out rows, inflating the sum in some contexts. Identify the declared grain of each model and find the join that violates it before changing the metric definition...

Boundaries and compatibility

Ideal for

  • Grain and fan-out review: produce a decision or artifact grounded in supplied evidence.
  • Slowly changing dimension design: produce a decision or artifact grounded in supplied evidence.
  • Metric definition consistency: 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.