Dbt incremental strategy audit

Skill yeaight7/agent-powerups/plugins/data-engineering/skills/dbt-incremental-strategy-audit

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Install

npx -y skills add yeaight7/agent-powerups --skill dbt-incremental-strategy-audit

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

6 stars6 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

Use when reviewing a new or changed dbt incremental model -- the strategy looks over-engineered, rebuild or delete behavior is unclear, or the model deviates from how the rest of the repo handles incrementals.

SKILL.md

3.5 KB, as published. Nobody here has run it

Purpose

Determine whether a dbt incremental model's chosen strategy is justified, understandable, maintainable, and consistent with repository conventions. The goal is not to praise clever implementations. Default stance: skeptical, low-risk, root-cause oriented, convention-preserving — prefer the simplest correct strategy.

When to Use

Reviewing a new or modified materialized='incremental' model
An incremental model shows confusing runtime behavior: unexpected rebuilds, deletes, or reprocessing
A model deviates from the repo's established incremental conventions without explanation

Inputs

The model SQL and its config: incremental_strategy, unique_key, partition_by/cluster_by/partitions, pre_hook/post_hook
Comparable incremental models in the same repo

Workflow

Understand the repo pattern first. Inspect similar incremental models and how they configure their strategies:
```
grep -rn "incremental_strategy" models/
grep -rln "is_incremental()" models/
```
Explain the actual runtime behavior. Compile the model and read the SQL that really runs — is_incremental() branches make the Jinja source misleading:
```
dbt compile --select <model>
# then read target/compiled/<project>/models/.../<model>.sql
```
is_incremental() is false when the target table does not yet exist or on --full-refresh, so reason through both branches.
Review the full incremental surface: materialized='incremental', incremental_strategy, is_incremental() logic, unique_key, partition_by/cluster_by/partitions, pre_hook/post_hook, delete/overwrite/merge semantics, rolling window logic, deduplication choices.
Identify hidden risks:
- hybrid full-refresh behavior hidden inside an incremental model
- deletes outside expected scope
- reprocessing more data than intended
- partition/window drift and maintenance burden
- strategy mismatch with the repo's established conventions
- operational complexity without clear business benefit
- confusing runtime behavior that future maintainers may misread
Check downstream stability before recommending a change:
```
dbt ls --select <model>+
```
Strategy changes can shift BI/reporting outputs; list affected downstream assets.

Output

A. Current model runtime behavior
B. Repo convention comparison
C. Strategy risks
D. Whether the complexity is justified
E. Safer alternative
F. Minimal-diff recommendation
G. Validation plan

Verification

Compiled SQL inspected, not just the Jinja source
At least one comparable incremental model in the repo compared
Delete/overwrite/merge scope stated explicitly
Downstream assets listed before any strategy change is recommended
Recommendation is a minimal diff and includes a validation plan

Failure Modes

Praising cleverness — operational complexity without clear business benefit is a finding, not a feature.
Reading Jinja instead of compiled SQL — is_incremental() branches hide the real runtime behavior.
Auditing in isolation — a strategy that is fine on its own may still be an unjustified deviation from repo conventions.