--- name: maintain description: 'Use this to keep a dbt project and its semantic layer correct as the warehouse and the business change, including a semantic layer that is native Apache Ossie documents rather than dbt. It detects drift on four axes and proposes the fix: schema drift (source columns and tables added, dropped, retyped, or renamed), volume drift (a row count that collapsed, a table that emptied, a load that half-failed), grain drift (a key that lost uniqueness, a changed row-per-entity cardinality, an increased join fanout), and semantic drift (a metric, measure, dimension, or entity definition that no longer matches, new categorical values, dangling semantic references). Reach for this when something that used to work has started failing or producing different numbers and the cause is more likely upstream than in the code you just wrote: a test that began failing with no code change, a dashboard whose numbers moved, a model that is suddenly empty or duplicated. Trigger it for requests like "what changed in the warehouse", "did anything drift", "is my dbt project still in sync", "my primary key has duplicates now", "the row count dropped", "did the load run", "the data stopped flowing", "the revenue metric definition changed", "reconcile my models with the source schema", "which models are stale", "did my Ossie semantic layer drift", or "is this relationship still valid". It reads the .dex/ snapshot and proposes reviewable diffs; it never overwrites hand-written work. To author new models or metrics from scratch, use transform. To learn an unfamiliar warehouse for the first time, use explore.' --- # Maintain Keep the repository correct as the world underneath it moves, on both of its axes: the dbt project and the semantic layer. Drift is measured against a **baseline**, the `.dex/snapshot.json` fingerprint of the warehouse map and of each project layer. Detection is read-only; only `reconcile` proposes edits, and `transform apply` is the one door that writes them. ## Procedure dex works in one loop: explore, transform, maintain. Every step runs one engine command, reads the single JSON envelope it prints, and decides the next step from that envelope. 1. **Explore before you write.** Before writing or fixing SQL against a table whose columns, types, grain, or join keys you have not verified in this session, run `explore map` (or `explore profile ` for a few named ones) and read its grain, keys, PII flags, and data-quality findings. Answer ad-hoc questions with `explore query`, never with a raw database client. 2. **Plan the change.** Author the file content and hand it to `transform plan` (or `semantic define|update|plan` for the semantic layer). Nothing is written yet. Read the diffs, the `warnings`, and, on a model that already exists, `data.row_attribution`; re-plan until they describe the change you meant. 3. **Apply it.** `transform apply ` writes the plan as a reviewable git diff. A human edit made since planning comes back as `needs_confirmation`: re-plan against the current files. 4. **Build and verify on dev.** Run `transform build --target dev --verify`. Read `data.verification.ran` first, then relay the findings and anything under `suppressed`. A green build says dbt ran, not that the rows are right. 5. **Maintain.** `maintain check` compares the warehouse and the project with the `.dex/snapshot.json` baseline, and `maintain verify` checks the project as it is now with no baseline. `maintain reconcile` proposes the fix as a plan, applied with `transform apply`. Take `maintain snapshot` after a known-good build. At every step: a `needs_confirmation` envelope waits on the user's spend decision, never on yours; only a human clears a PII flag; and you never edit the dbt project, `.dex/cache.json`, or `.dex/plans/` by hand in place of the command that guards them. ## Running the engine ```bash uv run --no-project --script "${CLAUDE_SKILL_DIR}/scripts/run.py" [flags] ``` - `uv` is a prerequisite that Claude Code does not install. If the shell reports `uv: command not found`, stop and tell the user to install it (`brew install uv` or `pipx install uv`, or the installer for their platform from the uv docs), then re-run. Tell them the command; never run an installer yourself. Never diff the warehouse against the project by hand instead: the drift axes and the baseline comparison live in the engine, so any other path is guesswork. - The first command in a fresh environment installs the engine and can take tens of seconds. Offer `run.py --warm` once at setup to pay that up front; do not run it before ordinary commands. ## Maintaining, step by step 1. **Pick the entry point.** With a baseline, start at `maintain check`: it sweeps every axis and ranks findings by blast radius. With no baseline, or on a project whose numbers were never right, start at `maintain verify`, the one command here that needs no snapshot: it answers "is this right" rather than "what moved". 2. **Read `warnings` first, always.** They carry the reasons the baseline may no longer describe the warehouse (a cache newer than the snapshot, a baseline pinned from a stale cache). That line bounds every finding above it and is never in `findings`. 3. **Drill into one axis** when a finding needs depth: `maintain schema` (structure), `maintain volume` (row counts), `maintain grain` (key uniqueness, declared grains, fanout), or `maintain semantic` (definitions and dangling references). Each takes optional ``. 4. **Reconcile.** `maintain reconcile []` stores the proposed edits as a plan and prints a `plan_id`. Read each proposal's `kind` (rules below), then apply with `transform apply `. A human edit made since detection comes back as a conflict, never an overwrite. 5. **Re-baseline** once the result is known-good: `explore map`, then `maintain snapshot`. Per-axis fields and finding codes are in `${CLAUDE_SKILL_DIR}/references/axes.md`. Read it when a finding's code or field is not described here. ## Rules that change what you do ### The baseline - Take `maintain snapshot` right after a known-good build, and tell the user to **commit `.dex/snapshot.json` like a lockfile**, so the whole team diffs against one reference. - Never snapshot a state that is already drifted: `check` would then mask the very drift that matters. To accept a change as the new normal, re-run `explore map` first, then `maintain snapshot`. - On a warehouse past the rank cutoff (more than 50 objects), run `explore map --full` before snapshotting, or the baseline cannot compare columns for the objects it never profiled. A partial snapshot is still valid; relay its coverage warning rather than calling the result clean. - After a project-only refactor (moved model files, a renamed dbt project), use `maintain snapshot --project-only`. It opens no connection and carries the warehouse evidence forward unchanged, so it never launders a stale warehouse into a fresh one. ### Reading findings - `key_lost_uniqueness` means the data changed: a key proven unique is not any more. `declared_grain_not_unique` means nothing changed: the project declares a grain the data never had, so the fix is to the declaration (widen it, dedup upstream, or drop the claim), not to the data. - On `maintain semantic`, read `unavailable` before hunting for an element kind. A native Ossie layer has no measures and no entities, so their absence is the format, not drift. - On `maintain verify`, read `data.suppressed` before reading an empty `data.findings` as a clean bill. A project that does not compile suppresses every other check. Row population is conservative by design, so a quiet answer is weaker evidence than a finding, and `warnings` names the models that could not be lined up. - For a change you just made, use `transform build --verify` (same sweep, scoped to that build). Use `maintain verify` for the whole project, or when somebody else ran the build. ### Reconcile proposals - **`mechanical`** (schema drift): re-scaffolds a dex-scaffolded staging model, or edits the drifted columns into a declaration. Still a reviewable diff: read it for hand-written logic the scaffold cannot know about before applying. - **`advisory`** (grain, volume, semantic): a decision for the user, at most backed by a test edit that makes the break visible in builds. dex cannot dedup a warehouse or decide whether a new `'refunded'` status belongs in a metric, so relay the decision rather than make it. - **A type change is advisory on every format.** The proposal names both spellings, and the edit is the user's. On ClickHouse nullability is part of the type, so a column that starts accepting nulls is reported as a retype. - With no editable dbt project, every proposal is advisory and no plan is stored. Authoring into a native semantic layer is `semantic ossie` in the transform skill, never this command. ### Cost - Schema, volume, and the definition half of `semantic` read metadata and are free everywhere. Grain and the dimension-cardinality half of `semantic` scan. - Asked for directly on a metered connector, `maintain grain` returns `needs_confirmation` with an estimate. Surface it in human units, get an explicit budget from the user, and re-issue with `--confirm --budget ` in the estimate's unit: bytes on BigQuery, warehouse-seconds on Snowflake and Databricks, compute-seconds on Redshift, database-seconds on Postgres and ClickHouse. On DuckDB nothing prompts. - `check`, `semantic`, and `verify` answer first and offer second: they return `ok` with final findings, and the price of the rest under `data.offer` (`data.axes_run` says what ran, `data.offer.axes` what the estimate adds). Confirming is the user's choice, not a required next step: quote the estimate, say which axes are still dark, and let them decide. A triage that stops at the free axes is complete work. - Never invent a budget. After an over-ceiling refusal, never retry with a raised budget without asking. Relay its calibration line, and point out that the ceiling binds on the estimate, so a budget set at that fraction of the estimate is refused again. - A `suggested_session_ceiling` is the project's one-time ask for a daily cap. Surface it, get the user's answer, and add `--session-ceiling ` or `--no-session-ceiling` to the same re-issue. Never answer it for them. ### Data and authority - Raw rows and dimension values never cross the envelope; grain and cardinality checks use aggregates only. - Human edits to the project and to the semantic layer are authoritative. On a conflict the engine surfaces the divergence; relay it and ask, never overwrite. ## References - `${CLAUDE_SKILL_DIR}/references/axes.md`: what each axis detects, the snapshot contents, `verify`'s finding classes, and what each axis costs.