DATA OPS
Agent-driven dbt cost-vs-usage triage for unused models
An agent cross-references dbt models against Snowflake query history to find expensive models that haven't been read in 30+ days, decides keep vs. deprecate.
How it runs
The automated pipeline, trigger to output.
- TriggerMonthly schedule
- ActionPull model inventory from GitLabGitLab
- ActionQuery Snowflake cost and access historySnowflake
- LogicAgent decides keep vs. deprecate per model
- ActionFile ranked Linear deprecation ticketsLinear
- OutputSend savings rollup to SlackSlack
What it does
Joins three signals — the dbt model list, Snowflake warehouse cost per model build, and Snowflake `ACCESS_HISTORY` showing who actually queries each table. An agent reasons over models that are both costly to build and unread for 30+ days, distinguishing true dead weight from rarely-but-critically-used tables, then files Linear deprecation tickets ranked by potential savings with a written rationale per model.
When to use it
Run monthly when you want to cut warehouse spend but need judgment, not just a threshold — some unread models are quarterly reports, others are genuinely abandoned. The agent's reasoning is what makes it more than a SQL query.
How it works
- 1A monthly schedule starts the triage.
- 2Pull the model inventory from the dbt repo in GitLab.
- 3Query Snowflake for build cost and last-access date per model.
- 4The agent evaluates each costly-and-unread model, weighing access patterns and naming hints to recommend keep or deprecate.
- 5For deprecate calls, create a Linear ticket with savings estimate and the agent's rationale.
- 6Send the Chairman a Slack rollup of projected monthly savings.
Set it up
What you configure once, before turning it on.
- 1Connect GitLabRepos, MRs, pipelines, registry.
- 2Connect SnowflakeWarehouses, queries, shares.
- 3Connect LinearIssues, projects, cycles, triage.
- 4Connect SlackChannels, DMs, threads, mentions.
- 5Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 6Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 7Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
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Run this workflow in your colony.
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