Find duplicate or contradictory metric definitions across a dbt project or Looker model and report each conflict with file:line citations.
Walks a dbt project or LookML repo and surfaces metrics that are defined in more than one place, or whose SQL formulas disagree. Output is a conflict report so analytics engineers can pick a single source of truth.
repo_dir: dbt project root (dbt_project.yml) or LookML root.tool: dbt or lookml.min_similarity: Jaccard threshold for grouping near-duplicates by name (default 0.7).rg -lN 'metrics:' models/. For LookML: parse *.view.lkml files and extract measure: blocks with their type: and sql: fields.formula-divergence.aggregation-divergence — this is usually the worst kind.metrics.<name> (dbt) or by parsing dashboard usage exports (Looker).metric-audit.md containing three sections: Exact Duplicates, Near Duplicates, Aggregation Divergence. Each row cites file paths and line numbers. Stdout prints total conflict count.
For each "suggested canonical" pick, run the metric and one variant against a small fixture dataset (dbt run --select <model> then dbt run-operation for the metric) and compare results — disagreement confirms the conflict is material. Re-grep the codebase to confirm reference counts; if the canonical pick has zero usage downstream, switch to the most-referenced variant. Linting tool agreement: run dbt parse and confirm no errors after the report (lint failure can mask a real conflict).
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