grain

DE//AGENT currently lists 4 resources under grain: 3 tutorials, 1 skill. Everything below is derived from the live registry — counts, capabilities, verification state and security posture come from the curated records, never from marketing copy.

Documented learning objectives across these resources:

• State a model's grain and match it to its unique key test — via dbt Model Review
• Choose table vs incremental vs view deliberately — via dbt Model Review
• Handle late data in an incremental model — via dbt Model Review
• State the grain of a result set in one sentence — via The relational mental model — sets, grain, and NULL
• Explain how a join's cardinality (1:1, N:1, N:N) changes the row count of the result — via The relational mental model — sets, grain, and NULL
• Name the five correctness failure modes the SQL review rules target — via The relational mental model — sets, grain, and NULL
• State a model's grain in one sentence and match it to the unique test — via Review a dbt model PR before merge
• Justify table vs incremental vs view for a given source — via Review a dbt model PR before merge

4 more objectives live on the individual resource pages.

These resources map to stages: Python + SQL (2), Warehousing (2). Follow the four-phase roadmap to place this topic inside a progression instead of learning it in isolation.

Every entry, its shape and its trust state — sorted by the registry's composite score. This table is the honest version of a marketing comparison: no sponsored placements, no unverifiable claims.

How scores are computed: source quality score.

Highest composite score first:

1. dbt Model Review score 40.5 — intermediate
2. The relational mental model — sets, grain, and NULL score 34.15 — beginner
3. Review a dbt model PR before merge score 34.15 — intermediate

Scores rank provenance, completeness and trust — read how verification works before adopting anything.

Start here if you're new: The relational mental model — sets, grain, and NULL.

Security-flagged entries: dbt Model Review (1 flag). Flags mean the record declares capabilities like credential use or write access — read the security posture on each page before connecting.

• dbt Model Review — Checks a dbt model's grain statement, ref/dependency hygiene, incremental strategy and late-arriving-data handling, then proposes a test set. details
• The relational mental model — sets, grain, and NULL — Before the first SELECT, build the model every later lesson uses: a query returns a set with a stated grain, a join changes that grain by its cardinality, and NULL never compares a… details
• Review a dbt model PR before merge — Take a pull request that adds or changes a dbt model and walk the reviewer's pass: read the diff in context, state the model's grain and check it against the unique key test, choos… details
• Window functions — keep the grain, rank the rows — A window function computes over a set of rows and hands the answer back to every row, so the result keeps its grain — the property that makes a keep-latest dedupe safe and a bare G… details

Real install commands from the registry — read the source before running any of them:

npx de-agent-platform add dbt-model-review

dbt Model Review — review first

Adjacent topics: tutorial (3 shared), review (2 shared), intermediate (2 shared), incremental (2 shared).

Study path: the roadmap places this topic in a progression · the scoring model explains the numbers · the trust rule catalog shows every security check these entries passed through.