maintain

Maintain

Keep the dbt project correct as the world underneath it moves. Maintenance is the recurring half of the loop: warehouses drift, loads half-fail, models go stale, keys stop being unique, and business definitions change. This skill compares a known-good baseline against current reality, classifies what drifted, and proposes the reconciling edit. It is manual and on-demand here; continuous drift detection and automated PRs are the commercial product.

The model: baseline, detect, reconcile

Drift is measured against a baseline (the .dex/snapshot.json fingerprint of the warehouse map and the project's per-layer definitions). Detection is read-only; only reconcile proposes edits.

Snapshot discipline matters. A snapshot is only as trustworthy as the moment it froze. Take one right after a known-good build (maintain snapshot), and commit .dex/snapshot.json like a lockfile so the whole team diffs against the same reference. Snapshot a state that is already drifted and check will mask the very drift you care about. When you accept a change as the new normal (re-run explore map first, then maintain snapshot); check warns when the baseline looks stale.

How to drive it

uv run "${CLAUDE_SKILL_DIR}/scripts/run.py" <subcommand> [flags]

• maintain snapshot captures or refreshes the baseline. Run it after a clean explore or transform session so later runs have a known-good reference. It pins the current .dex/cache.json (so the grain baseline is the exact-distinct verdicts explore map already computed) plus per-layer fingerprints of the dbt project. Without a cache it captures a metadata-only baseline and says so.
• maintain check is the everyday entry point: it sweeps every axis and returns a report ranked by blast radius. Read-only.
• maintain schema [<objects>] detects structural drift: source columns and tables added, dropped, retyped, or renamed; nullability changes; declared sources the warehouse no longer honors.
• maintain volume [<objects>] detects freshness drift: row counts that collapsed, spiked, or went to zero. This is the "is the data still flowing correctly?" axis, distinct from "did the shape change?".
• maintain grain [<objects>] detects grain drift: a declared primary or unique key that now has duplicates, a changed row-per-entity cardinality, or an increased join fanout. Uses aggregates, never raw rows.
• maintain semantic [<objects>] detects definition drift: metric, measure, dimension, or entity definitions that changed against the baseline; semantic references that no longer resolve to a model or column; and categorical dimensions whose set of values widened or narrowed underneath their metrics.
• maintain reconcile [<class>] proposes the dbt edits that bring the project back in sync, as reviewable diffs. Optionally scope it to one class (schema, volume, grain, or semantic).

The usual flow: check to triage, a focused detector to understand one axis in depth, then reconcile to get the proposed fix.

Per-axis cost: what is free and what scans

Detection is read-only, but read-only is not the same as free on a metered connector (BigQuery, Snowflake, Databricks, Postgres). The axes split:

• Schema, volume, and the reference/definition half of semantic are free everywhere: they read metadata and the snapshot, and run immediately.
• Grain and the dimension-cardinality half of semantic scan the warehouse, so on a metered connector they run the two-step handshake. The first call returns needs_confirmation with an estimate in cost.estimate (and a per-table breakdown). Surface it to the user in human units, get an explicit budget, and re-issue the same command with --confirm --budget <magnitude> in the paradigm's unit (bytes on BigQuery, warehouse-seconds on Snowflake and Databricks, database-seconds on Postgres). Never invent a budget the user did not agree to, and never retry with a raised budget on an over-ceiling refusal without asking.
• check is two-phase on a metered connector: the free axes complete immediately and their findings ride along in the needs_confirmation envelope, with one combined estimate for the scanning axes. Confirm to complete the sweep.

On DuckDB everything is free and local, so nothing prompts.

Reconcile proposals are mechanical or advisory

Reconcile tags every proposal by kind, because the fix differs sharply by axis:

• mechanical: schema drift on a dex-scaffolded staging model re-scaffolds the model from the drifted source. High-confidence, but still a reviewable diff: read it for hand-written logic the scaffold cannot know about.
• advisory: grain, volume, and semantic drift are decisions, not auto-fixes (dex cannot dedup your warehouse or decide whether a new 'refunded' status belongs in a metric). The proposal is the decision surfaced, at most backed by a test edit that makes the break visible in builds.

When reconcile produces edits it stores them as a plan and prints a plan_id. Apply them with transform apply <plan-id> (the one apply door): a human edit made since detection surfaces as a conflict, never a silent overwrite.

Guardrails (enforced in the engine, not here)

• Read-only against data. Schema, volume, and semantic references are computed from metadata and the snapshot; grain and dimension-cardinality use aggregates only. Raw rows and dimension values never cross the envelope.
• Propose, don't impose. Reconciliation is always a reviewable diff, applied through transform apply. Human dbt edits are authoritative; on conflict the engine surfaces the divergence and asks rather than overwriting.
• The dbt project is the source of truth; the .dex/ snapshot is a non-canonical fingerprint used only to detect change.