GetCassis

Dbt Agent Readiness — Security skill for Claude Code

Security community

Audit a dbt project for what an AI agent will get wrong if you point it at the data today.

How to install Dbt Agent Readiness

This entry records only its repository, not the path inside it, so there is no exact command to give. Open GetCassis/dbt-agent-readiness and copy the folder into ~/.claude/skills/, or the file into ~/.claude/agents/.

What Dbt Agent Readiness does

Audit a dbt project for what an AI agent will get wrong if you point it at the data today.

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README

dbt-agent-readiness

Find the places your AI analyst will return a plausible — but wrong — answer.

`dbt-agent-readiness` is a Claude Code skill that audits a dbt project for ambiguous metrics, unsafe joins, misleading documentation, invalid columns, and unclear model grain. It is built for dbt teams piloting AI analysts, copilots, or internal data agents.

The audit produces one evidence-backed Markdown report containing:

what an agent will get wrong today
what still needs runtime verification
which models are safe to query
what to fix first

It does not query your warehouse, require warehouse credentials, or modify your dbt models and configuration.

Quick start

Clone the skill into your personal Claude Code skills directory:

git clone https://github.com/GetCassis/dbt-agent-readiness ~/.claude/skills/dbt-agent-readiness
python3 -m pip install -r ~/.claude/skills/dbt-agent-readiness/requirements.txt

Then ask Claude Code:

Run the dbt-agent-readiness skill on /path/to/dbt/project

The report is written to `{project_path}/dbt-agent-readiness.md`.

Requirements

Claude Code with Skills support
Python 3.9+
pyyaml and `sqlglot`, installed by the command above

Running `dbt compile` in the target project before the audit is recommended. It lets the skill resolve generated columns from macros such as `dbt_utils.star`, `SELECT *`, and Jinja loops. When compiled SQL is unavailable, checks that cannot be supported confidently are suppressed instead of reported as findings.

See what it finds

On the bundled 10-model test project, the audit finds four concrete ways an agent can fail:

Failure	What the agent gets wrong
Inconsistent entity names	Misses rows when joining `customer_id`, `cust_id`, and `user_id`
Competing revenue marts	Picks between two plausible models with different grains
Misleading descriptions