assay

I say, I say — assay.

Recover the semantics your warehouse never wrote down.

Your dbt project has types nobody declared. Every model has a grain, every number has a unit, every nullable column has a meaning for null, and every model assumes something about what its parents already did. None of it is written anywhere, all of it is load-bearing, and the only copy lives in the head of whoever last debugged it.

assay infers those semantics from the code, stores them as data, and finds the places where they contradict each other.

sqlglot reads the structure. TypeSafe's Jev reads the meaning. SQL does the rest.

Built while auditing a Colorado water rights warehouse, where a case number is only unique inside a water division and nothing in the stack could tell me that.

Status

Early. The no-key tier works; the judgment tier is landing next.

The inventory

assay inventory                     # every model: grain, columns, reach
assay inventory --model water_rights  # one model, in full
assay inventory --write contracts.yml # a SEPARATE file; your schema.yml is never touched

Not a findings list. Here is what every model in your project actually is. On a 356-model warehouse: grain settled for 250, 4,684 columns classified.

Every cell says where it came from. declared (a human wrote it), observed (the probe counted it), derived (code worked it out) or judged (with the probability). A fact resting on an unresolved premise says so rather than inheriting confidence it did not earn.

The page

assay inventory --html docs/warehouse.html

One self-contained file: every model, what one row is, what each column does, where each value came from, and who said so. Colour-coded, searchable, no build step, opens from a file:// URL. Commit it and a change in what your warehouse MEANS shows up as a diff.

dbt docs shows you lineage. This shows you meaning.

On the pull request

- uses: ryan-sunny/dbt-assay@v0.1.0
  with:
    target: target-head
    baseline: base/target
    dialect: snowflake

Posts what changed about what your models mean, who consumes it, and how many of those aggregate over it. It does not gate by default: nothing should fail a build until its question has recorded verdicts, and assay refuses to anyway.

Ruling on findings, one keypress each

assay review -i

column_role  zip_tiers
  role = measure  confidence 1.00
  models/marts/zip_tiers.sql · 0 downstream, 0 marts
  monthly_price = CAST(GREATEST(5, ROUND(12 * a.leads_per_week * pr.price_multiplier))
  comes from: computed — derived here by an expression

a agree, d disagree, u unclear, s skip. Least certain first, because a verdict on an answer already given at 0.99 teaches almost nothing and one on a 0.45 is where the question is actually being decided.

The evidence is on screen because a verdict nobody can reach in five seconds does not get given.

assay review --from-labels     # verdicts from assertions already in your project

A unique test says a column is an identifier; a declared key says what one row is; a join says two columns are the same concept. Those are real human judgments, made earlier, and they are recorded as label rather than human — evidence about a question, never permission for it to fail a build, because the label can itself be the thing that is wrong.

While you type, and for your agent

assay watch --compile --project-dir transform   # a pane that stays quiet until meaning moves
assay mcp                                       # assay as tools an agent can call

watch diffs your working tree against a snapshot taken when it started, so a reformat, a renamed CTE or a join rewritten as a subquery says nothing. Break something and fix it before the next save and it never speaks, because nothing ended up different. A file that does not parse is "still typing", never a finding.

mcp serves contract, lineage, blast_radius, findings, changed_contracts and rebase. A contract is fifteen lines where the SQL is two hundred, so an agent can hold a project's meaning in about what reading four models costs it now. changed_contracts is the self-check to run after an edit and before moving on: did that change what anything MEANS?

The rest of the bank

assay feeds --project-dir transform    # has a source changed its mind while its schema held still?
assay align                            # do two columns in different models mean the same thing?
assay tests --gaps-only                # what is a model exposed to that nothing asserts? (no key)
assay adjudicate                       # rows a dbt test flagged: does the row explain itself?

feeds samples ~20 rows per source, because the defect is uniform across a load. Half of it is arithmetic and never asked: a numeric column spiking at -9999, or a date whose max sits years in the future, is a placeholder found by counting.

align calibrates itself. Every join in your project is somebody asserting two columns hold the same concept, so the labels are already written: measured 100/100 agreement against pairs a real project already joins. Routed by rounding to the nearest level, no threshold to tune.

tests finds coverage gaps with no API key at all — 182 on a real project, the worst being a model with 24 marts downstream exposed to a fan-out that nothing asserts against. Severity fit is a judgment, and your current severity: setting is the weak label it argues with.

adjudicate reads dbt_test__audit, so store_failures is the candidate generator. A test returning 3,229 rows becomes a triage list instead of a reason to switch the test off. The explanation options are domain knowledge: explanations: in audit.yml holds one set per mart.

Standard practice, and your agent following it

assay practices --keys-only     # models with no uniqueness test, and the grain a test should cover
assay practices                 # the full standard set, adjudicated
assay skill --write .claude/skills/dbt-assay/SKILL.md

assay does not reimplement dbt-project-evaluator. It reads that package's own fct_* tables and splits its 23 checks three ways: enforce (exact, essentially no exception — a staging model reading downstream, a hard-coded table name), recommend (conventions; enforcing them is how a tool gets muted), and adjudicate (real candidates with real exceptions — a dimension with twelve children is what a dimension is).

What it adds is consequence, since evaluator has no notion of blast radius, and a judgment for the eight checks everyone currently ignores.

And where it beats the standard check outright: missing_primary_key_tests says "no PK test", while assay knows the inferred grain and says which columns it should cover.

assay skill writes the procedure an agent follows: call contract before editing, call changed_contracts after, never hand back work where the grain moved silently. The MCP server gives an agent the ability to check itself; the skill gives it the obligation.

It becomes part of your warehouse

assay export transform/seeds/assay   # CSV seeds + a generated schema.yml
dbt seed --select assay_*            # now it is a relation

The inventory, every finding, every stored judgment with its full probability distribution, every human verdict, and one row per DAG edge. Seeds work on every adapter with no external-table setup. Then assay's knowledge is just data you can join to:

select check_name, count(*) as findings, count(distinct subject_name) as models
from {{ ref('assay_findings') }}
group by 1 order by 2 desc

A report is read once. A table accrues: a probability per question per model per commit is something you can chart and diff.

Install

uvx dbt-assay scan --target path/to/dbt/target

No install step, no API key, no configuration.

What runs without an API key

Structure is exact and free. These need nothing but your manifest.json and compiled SQL:

• the project graph, blast radius, and per-edge facts
• tests that cannot fail — not_null on a coalesce(x, 0), unique on the group by key, accepted_values covering every branch of a CASE
• ranking by a function that returns degrees, after alias resolution
• window functions positioned where they can only see post-filter rows
• dialect traps, like ~ meaning full match in DuckDB rather than a partial one
• columns dropped at a model boundary, per edge
• joins that fan out: a child joining a parent on only part of the key that parent's own unique_combination_of_columns test declares

Column knowledge is built parents-first through the DAG, so a select * is expanded from what its parents were found to offer. target/catalog.json is used when present and every column list says whether it was derived from SQL, read from the catalog, or declared in schema.yml.

Counting what SQL cannot settle

Grain propagates through the DAG parents-first and has no base case: a model reads a source, the source declares no key, and propagation stops. assay probe settles it by counting.

assay probe --dry-run          # print the SQL it would run, run nothing
assay probe                    # run it through YOUR dbt
assay probe --emit > probe.sql # or run it yourself and --load the results

It shells out to dbt show --inline, so assay never sees a credential and every adapter and auth scheme your dbt already handles works unchanged. One statement per relation, one scan, and count(*), count(col) and count(distinct col) together, because count(distinct) ignores NULLs and a mostly-null column would otherwise look unique.

A result says unique, has_duplicates, has_nulls or unknown. A permissions error, a missing table or a timeout records unknown, never "not unique". And an observation is stored with its row count and timestamp, because unique in today's data is not a constraint.

What the judgment tier adds

Provenance needs no judgment at all. Every column is classified as constant, defaulted, ranked, aggregated, computed, carried or from_source, from the AST and the DAG. assay traces a column back through the graph until it reaches the hop that actually did something to the value, which is the answer to "where did this number come from" that no warehouse can give you today.

Grain, where code proposes the candidate columns and one noul per column decides which of them identify a row. Column role and null meaning, chunked so repeated criteria stay inside the token budget.

Opt-in, cached so an unchanged model is free forever, and roughly a third of a cent for sixty models.

Nothing gates until it has been measured

assay review records human verdicts, and config refuses to let a question fail a build until that question has enough of them. Not a warning in the docs, an actual downgrade to queue.

Two families can be calibrated on day one against tests the project already contains, and the measurement is honest about its own limits: role agreed with 57 of 61 such labels, and reading the four disagreements showed three were the label being wrong.

What leaves your machine

Nothing, until you turn the judgment tier on.

• The structural tier is entirely local. No network, no telemetry, ever.
• With judgments enabled, a digest of compiled SQL is sent: expressions, joins, predicates. Not your data.
• The row-adjudication layer sends actual rows and is off by default. init never enables it.
• --print-state on any command renders exactly what would be sent, without sending it.
• The warehouse connection is opened read-only.

Contract diff

assay diff --baseline ../main/target            # what changed about what your models MEAN
assay diff --baseline ../main/target --markdown # the paragraph, for a PR comment

int_water_section_irrigation: the SQL's grain moves from section_id, irrigation_year to section_id. 1 model consumes it, and 1 of them aggregates over it (water_section_summary). 9 marts downstream. Nothing in the SQL diff says this.

A grain change looks like somebody edited a GROUP BY. This says one row stopped being one row per (section, case), who consumes it, and which of those aggregate over it and are now inflated. A rewrite whose contract is unchanged produces nothing, which is exactly right.

It tracks the SQL's grain separately from the declared one, so a GROUP BY drifting away from a live unique test is caught and named: one of the two is now wrong.

Backtest: does it work on YOUR repo?

assay backtest --repo .

Replays every commit touching model SQL, reads the blob before and after with git show, and asks whether a check fired before and went quiet after. No checkout, no stash, nothing that can collide with other work in the clone.

On the repo it was built against it found the two models a spatial-ranking defect was removed from, at a commit whose message never contains the word "fix" — which is why the message is a label here and never a filter. Three of the four commits that removed that defect would have been missed by matching on wording.

Historical compiled SQL does not exist, so by default ref() and source() are resolved and control blocks stripped. That is fast and reads 83% of blobs. For the rest:

assay backtest --compile --project-dir transform

checks each commit out into a detached worktree (never your working tree), links this checkout's dbt_packages so nothing is fetched, generates a throwaway DuckDB profile so dbt can connect without touching your warehouse, and really compiles — but only for the blobs the strip could not read, so you pay the per-commit parse just where it buys something. On a real repo that took unreadable replays from 3 to 0.

The remaining caveat, stated rather than hidden: today's packages and dbt version are not guaranteed to render exactly what that commit rendered years ago, and on a Snowflake or BigQuery project the throwaway profile compiles through DuckDB's adapter, so an adapter-dispatching macro can differ. Pass --profiles-dir to use your real one.

Version when the meaning changed, never when it did not

assay version-check --baseline ../main/target          # flags models owing a bump, exits non-zero
assay version-check --baseline ../main/target --bump   # prints the exact edit
assay version-stamps --recommend                        # do rows say which logic produced them?

Every "you must bump the version" check ever written fires on whitespace, nags on a reformat, and gets switched off within a fortnight. assay is the one thing in the stack that can tell a renamed CTE from a grain change, so a reformat, a rewritten join and a tidied comment owe nothing.

The level comes from what the change does to a consumer. Major: the grain moved, a column left, a column's role changed, or its provenance changed in a way that alters nullability — from_source becoming defaulted means NULLs silently became zeros and every average downstream shifts. Minor: a column was added.

--bump prints the edit; --bump --write applies it as a targeted text edit. Measured on a real 2,327-line schema.yml: two lines added, all 94 comment lines intact. Inferred contracts still go to their own file — a bump is a decision, a contract is a guess, and only one of them belongs in a file you maintain by hand.

Configuration actually configures

assay init writes an audit.yml. It scopes questions, waives findings, and decides what an answer is allowed to do:

questions:
  ranks_by_degrees:
    action: fail            # EXACT check: a parser decided it, so it may gate immediately
  grain_contradicts_test:
    when: { select: "path:models/water+" }
    act:
      queue: "p > 0.60"     # JUDGED: written as an expression, so its direction is readable
      fail:  "p > 0.85"     # ...and refused until the question has recorded verdicts

waivers:
  int_water_section_county:
    - question: ranks_by_degrees
      reason: "ST_AREA among candidates at one latitude preserves the ordering"

assay check exits non-zero only when something earns fail. A selector assay does not understand is an error, never a silent match-all.

Where did this number come from

assay trace water_rights.water_right_id

Follows a column back through the DAG to the first hop that did something to the value, and stops honestly at a source, because what happened outside dbt is not knowable from a manifest.

Maintenance

Maintained for my own use. PRs read when convenient, issues may sit, fork freely.

Developed against DuckDB. Verified elsewhere, all with no warehouse connection at all:

On the 1,632-model BigQuery project, from raw SQL with no warehouse: 10 seconds, 7,858 tests read, 272 coverage gaps, and two not_null tests that can never fire because the column is the literal 'BA'.

The elementary number is the honest limit and not a dialect problem: its models are {% set %} blocks calling macros, so there is almost no SQL to read until dbt compiles them. assay reports what it could not read rather than counting it as clean.

--dialect snowflake | bigquery | postgres | redshift | databricks | duckdb.

Where there is no compiled SQL, assay strips the Jinja and says so. That is not a compile, and a macro-generated model will not survive it, but it means a project can be audited by somebody with no credentials -- a reviewer, a security team, or you evaluating this tool.

Licence

Apache-2.0