Dbt Agent Readiness

Initial public release of the dbt-agent-readiness skill for Claude Code.

What it does

Audits a dbt project for what an AI agent will get wrong if you point it at the data today: wrong metric, wrong table, missed rows, broken joins. Produces a prioritized report organized by failure mode.

Highlights

• Evidence-based report split: Blockers (code-level failures an agent will hit today) and Hygiene (risk factors shipped with runnable verification queries).
• Deterministic Python inventory with 15+ catalogs: phantom columns, concept variants, unit drift, description-vs-SQL contradictions, overlapping-concept-columns, lineage cycles, enum value gaps, same-name-different-grain, convention drift, and more.
• Dialect-aware SQL parsing via sqlglot: BigQuery, Snowflake, DuckDB, Redshift, Postgres. Recursive CTE column resolution and column-level lineage for phantom-column detection.
• Two-pass subagent architecture that scales to project size: inline (≤30 models), 2-4 parallel subagents (31-500 models), checkpoint before dispatch (>500 models).
• Manifest-aware phantom detection: when target/manifest.json is present, macros (dbt_utils.star, SELECT *, Jinja for-loops) are resolved. When absent, phantom findings on macro-using models are suppressed rather than emitted as noise.
• dbt mesh support: two-arg ref('project', 'model') recognized; cross-project refs excluded from broken-ref checks.
• Doc block resolution ({% docs %} / {{ doc() }}) and Jinja-aware severity parsing.
• Safe-pilot perimeter: each audit ends with an explicit list of models agents can query safely today and a remediation backlog.

See CHANGELOG.md for the full 1.0.0 entry and examples/messy-jaffle-shop-audit.md for a sample audit.