AltimateAI / AltimateAI/altimate-code

Add a dbt-optimizer agent: scan dbt projects for fixable issues with cost/impact reporting

Open
#1,091 0 comments 0 reactions 0 assignees View on GitHub
Dominant language
TypeScript
Stars
811
Forks
134
Avg merge
3d 2h
Merged PRs (30d)
50

Description

## Feature

Add a new native primary agent, `dbt-optimizer`, that scans an existing dbt project for concrete, fixable issues and proposes targeted fixes with cost and impact reporting. This is the agent behind the planned Optimize workflow.

**Input:** an existing dbt project (connected repository and environment).
**Output:** candidates (detected issues), fixes for user-selected candidates, impact reports (batch effect incl. estimated cost change), and PRs for applied fixes.

**Detection scope** (from the dbt-optimization taxonomy research, `docs/internal/2026-08-12-dbt-optimization-taxonomy-research.md`):
- materialization & incremental processing (strategy-aware: merge / delete+insert / insert_overwrite / microbatch, precondition-gated)
- warehouse physical design (clustering / partitioning / sort keys, query-history gated)
- SQL anti-patterns, DAG economics (fan-out, duplicate scans, dead models), run-level orchestration (Slim CI, threads, full-refresh overuse), tests/docs/storage

**Safety requirements:** scan phase is read-only; warehouse writes denied non-overridably; SQL rewrites gated on decidable equivalence; cost claims only from attributed query-history evidence.

Named `dbt-optimizer` (not `optimizer`) because more optimizer agents are planned later.

Contributor guide

Open the contributing guide

Research direction

Start with docs/internal/2026-08-12-dbt-optimization-taxonomy-research.md and map its detection areas to the planned dbt-optimizer agent behind the Optimize workflow. Define the read-only scan, candidate selection, fix application, cost/impact reporting, and PR outputs, while preserving the stated warehouse-write, equivalence, and query-history safety gates. Done means the named agent covers the agreed scope without weakening those requirements.

Written by the indexing model from the issue text.

Assessment

Tech stack
sql, typescript
Domain
ai, data-engineering
Issue type
Feature
Difficulty
5/5
Estimated time
Over a week
Activity status
Quiet
Clarity
Mostly clear
Newbie friendliness
32/100

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