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.

Full overview — what it is, what each tier finds, and how to run it as a standing part of your warehouse rather than a one-off audit.

How it works

The parser and the judgment are not two products. They are one division of labour, and it is the whole design: if a parser can answer it, Jev is never asked. A grain, a column's provenance, a test that cannot fail — those are facts, settled exactly and for free, and putting them to a model would be spending money to make a certainty approximate.

What is left over is not a gap in the parser. It is a different kind of question.

where status != 'CANCELLED'

That is domain logic, a patch over a bad feed, or the thing that makes the model mean what it means. The SQL is identical for all three, no parser will ever separate them, and which one it is decides whether the line gets deleted next quarter or guarded forever.

That is the question assay exists to answer, and it is why Jev is not an add-on.

One command

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

On a project assay has never seen. It reads your manifest, tells you what it can and cannot see, runs the structural checks, runs the judgment tier if a key is present, writes an audit.yml that gates nothing, and prints the next command. --agent also writes the skill file your coding agent follows.

Status

Both tiers work. Run it with a key. The structural tier needs nothing but your manifest and is genuinely useful — it found 38 tests that cannot fail in a 356-model warehouse — but it is a very good linter, and a linter is not the point. The point is a warehouse that knows what it means, and meaning is the half a parser cannot reach.

Nothing gates a build in either tier until a question has recorded your verdicts, and assay refuses rather than warns.

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@v0.3.0
  with:
    target: target-head
    baseline: base/target
    store: assay.duckdb      # optional: commit or cache it and judged findings post too

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.

There is no dialect: line because the manifest names its own adapter. Pass one only to override. An earlier version of this action defaulted it to duckdb, which silently misparsed every other warehouse: on a BigQuery project that turned 12 parse failures into 128 and lost two real findings, without changing how confident the output looked.

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 onboard --target path/to/dbt/target

No install step, no API key, no configuration. onboard reads the project, says what it can and cannot see, shows what it found for free, writes an audit.yml that gates nothing, and prints the next command. --agent also writes the skill file your coding agent follows.

The key is never in a config file. assay reads TYPESAFE_API_KEY or OPENROUTER_API_KEY from the environment or from a .env in your project or any parent directory, and an exported variable always beats the file. assay config prints what it resolved and where the key came from; assay config --check makes one real call to prove it works, for about $0.00001. This existed as a bug first: assay read only os.environ, so a key sitting in a .env was invisible and every judged command reported the tier as off.

You do not pass a dialect. manifest.json carries metadata.adapter_type, so assay parses Snowflake as Snowflake and BigQuery as BigQuery on its own; --dialect is an override for the rare project whose manifest does not say. This was a flag once, and forgetting it was expensive: parsed with the wrong dialect, basedosdados went from 12 parse failures to 128 and silently lost two real findings. Nothing spurious appeared — the failure mode was a clean-looking run that had quietly stopped looking.

What a parser settles, before Jev is asked anything

Exact, free, local, and never put to a model. These need nothing but your manifest.json and compiled SQL, and they run first precisely so the judged tier is only ever asked the questions it is the only thing that can answer:

• 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 Jev is, and why it is not a chat model in a trench coat

Jev is TypeSafe's flagship System One model — a class of model "built to make fast, structured decisions that software can use directly". It does not write replies, produce code, or explain its reasoning. You define the possible answers, and it returns one of them with a calibrated probability.

It takes a state — named JSON fields, not a prompt — and a map of typed questions.

# this is the real question assay ships, copied from questions/semantics.yml
noul("Does the documentation -- `description` and `documentation_in_the_file` together -- "
     "assert something about this model that the code in `contract` and `filters` does not do?",
     true_means="The description states something the code does not do, or states the opposite.",
     false_means="Everything the description claims is true of the code. It may be incomplete, "
                 "terse, or silent about details; that is not a contradiction.")

Three primitives, and choosing right is most of the work:

It cannot return anything outside what you defined. That is the difference from asking a chat model for JSON and hoping: no parse step, no retry loop, and no answer outside the option set, because the option set is the type.

Question ids are never sent to the model. assay names them role__zip, desc, pred__0 for its own bookkeeping; the model sees only instructions and criteria, so those have to carry the complete question.

And it is cheap enough to run on every model. $0.042 per million input tokens, output free. A 265-model warehouse: 60 calls, 15 seconds, $0.0026. Cached on a hash of the state, so an unchanged model is free forever and a rebuild only re-asks what moved.

What Jev is bad at, and what assay does about it

TypeSafe publishes the failure modes, which is the most useful page they have. Four of them shape this tool directly:

"Jev is not a calculator." It cannot reliably do arithmetic, and it reads dates as text rather than ordered quantities. Measured here: asked whether date_trunc('month', d) + interval 3 month - interval 1 day implements "the last day of the second month following the month in which the application is filed", it scored the correct implementation 0.39 and a wrong one 0.62. Every date, count and comparison in assay is therefore settled by sqlglot or by SQL, never asked.

"Unrelated detail acts as a distractor." A larger state is a worse state. Measured on one question: the structural claim alone read 0.96; the same claim plus a correct worked date example read 0.47. Nothing was wrong with the extra sentence — it was simply extra. This is also why stripping comments before judging code improved results (a false positive fell 0.50 → 0.14): assay sends the smallest state that can answer the question, and no more.

It interprets literally, reading "scoping words, negations, and implied conditions at face value". So every criterion here is written as a concrete situation rather than a standard to live up to, and every choice carries a no-match option.

Multi-hop reasoning costs accuracy. So one noul per rule, never one over a list of them. Asked as a single lumped question across three rules, a known defect read 0.64; split, the rule that applied read 0.85 and the two that did not read 0.02 and 0.05. The split is sharper where it applies, correctly near zero where it does not, and it kills a false positive the lumped version produced on clean code.

The two design rules that decide everything else

Keep deterministic work in code. TypeSafe's own guidance: "Keep code in control and give System One narrow, structured decisions." A grain, a column's provenance, a test that cannot fail — those are facts, settled exactly and for free. If a parser can answer it, Jev is never asked.

A question the state cannot answer returns a confident non-answer. keys_on_a_non_unique_column read 0.73 to 0.85 on every model tested, clean or broken — because a column's uniqueness is a property of the DATA, not of the SQL. It was not a bad question, it was in the wrong layer. It is now count(*) = count(distinct k) and needs no model at all. Before adding a question, ask what in the state could make the answer no.

Prior art: this is the citation-check pattern

TypeSafe's citation check cookbook is the same architecture, one level down. Stage one is exact string matching, which catches a fabricated quote with no model call at all. Stage two puts a choice to the surviving candidates: supports, contradicts, or says_nothing.

That third option is the one worth stealing. "The evidence neither supports nor contradicts this" is a real state, and collapsing it into "false" is how a findings list earns a reputation for noise.

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.

Claims: what this project says, checked against what it does

assay claims --extract     # every sentence in your prose, classified
assay claims --write claims.yml   # audit them, edit them, suppress them
assay verify               # check each claim against the code

Your prose is not one claim, it is many, and judging it whole produces a coin flip. Measured: "Boulder commercial building permits, residential filtered out" put to a single question split 0.51 supports / 0.47 contradicts and flipped between runs, because one half is true and the other is not. Split into atomic claims, the sharpest read contradicts at 0.82.

So code splits the prose and a judgment says what job each sentence is doing — a claim about output, a claim about a rule, rationale, an incident note, an instruction to maintainers, or not a statement at all. Extraction is selection, never generation, because Jev is not trained to generate. The model never writes a claim; it picks from what your team already wrote, which is why every claim points at the file and line it came from and why claims.yml is a real audit surface.

Each claim is then checked on its own, against evidence chosen for it: supports, contradicts, or says_nothing.

Four rounds of measurement on one slice, every change to how the question was asked rather than to the model:

The third row is the one worth reading twice. A claim about d_class_cn read contradicts at 0.97 purely because the evidence listed thirty other columns and not that one — the model could not see the thing it was asked about, so it did what TypeSafe document it does and returned a confident non-answer. Now each claim names its own evidence.

Traversals: the defect class no single-model check can see

assay traverse             # judge every hop in the graph

Every other question reads one model. A fan-out introduced at one hop and consumed three models downstream is invisible to all of them: every count past it is inflated, each individual row is valid, and nothing fails. It is the defect a person finds by chasing a number by hand, months later.

The graph facts are free — what each edge carries, what it drops, what it joins on — so code narrows to the 395 edges of 457 where something actually changes, and the judgment answers the one thing code cannot: does one child row still mean one of the same thing as one parent row? same_thing, deliberately_coarser, silently_multiplied, or different_entity.

The top hit on the warehouse this was built for:

stg_co_parcels_composite → int_water_parcel_irrigation, joined on xmin, xmax, ymin, ymax

which is join irr i on p.xmin <= i.xmax and p.xmax >= i.xmin — a bounding-box overlap join. One parcel matches many irrigation polygons. Around $0.02 for the whole graph.

And the one that needs no assay vocabulary to read: does the description still describe the code? Prose is written once and the SQL changes around it. Nothing in a warehouse tests a sentence, so it drifts silently and everyone downstream keeps believing it.

The first one this found on the author's own warehouse:

stg_boulder_permits — "Boulder commercial building permits (residential filtered out)."

The filter excludes exactly two substrings, %single family% and %dwelling%. Of the 14,150 rows that survive it, 373 are non-residential, 157 are explicitly building permit - multifamily, and 13,620 are trade permits with no commercial distinction at all. Valid SQL, passing tests, false prose, and a lead product shipping residential roofing jobs as commercial. No structural check reaches that.

A description that many models share is excluded before anything is asked. On that same warehouse 84 of 343 descriptions were boilerplate repeated across models, and they produced half the first run's findings. Every one was true and worthless: "Staging model: light cleanup of one raw source" reads as contradicting any model that also filters, because template prose never mentions what the model does. Repetition is the general form of a placeholder and needs no vocabulary to detect.

Opt-in, cached so an unchanged model is free forever. Measured on a 265-model warehouse: 60 calls, 15 seconds, $0.0026.

The key, and where assay looks for it

assay config              # provider, model, spend cap, and where the key came from
assay config --check      # one real call to prove it works, about $0.00001

TYPESAFE_API_KEY or OPENROUTER_API_KEY, from your environment or from a .env in your project or any parent directory. An exported variable always beats the file. The key never goes in audit.yml, because audit.yml belongs in git.

This existed as a bug first: assay read only os.environ, so a key sitting in a .env was invisible and every judged command reported the tier as off. A capability check that can be wrong needs a way to show what it decided, which is what assay config is for.

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.

Getting a question to the gate takes about ten minutes of keypresses, and there is no way to skip it that is not a lie:

assay columns --limit 40      # ask, so there is something to rule on
assay review -i               # a, d, u, s -- least certain first

min_adjudications is 20 per question, counted per question rather than overall, so gating on three families is sixty verdicts and not twenty. Only verdicts marked human count. --from-labels is real evidence and is deliberately excluded, because a unique test can itself be the thing that is wrong, and letting a project's own assertions authorise a gate over those assertions is circular.

assay config       # how far each question is from its floor, and which gate nothing

Three of the twelve question families have a finding resting on them. The other nine are worth asking -- their answers fill the inventory, the page and trace -- but no finding derives from them yet, so ruling on them records evidence and moves no gate. assay config marks them and assay review -i says so before the keypresses start, because an afternoon spent on a question that authorises nothing is an afternoon nobody gets back.

This was a bug before it was a feature. Verdicts are recorded per question and the gate counted them per finding, so nine of ten families satisfied nothing, silently. The one that worked did so because its finding happened to share its question's name.

Until then every judged finding is an annotation. That is the intended resting state, not a limitation to work around: a threshold set before anything was measured is a guess wearing a number.

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