dbt-schema-verify

dbt-schema-verify is a skill for Claude Code, Codex from AltimateAI/altimate-code. It costs 216 tokens per session (1,681 once invoked), scanned A, original, MIT.

A check that compares the columns produced by a dbt model with the columns declared in its schema file. A schema file is the project specification for expected models and fields.

In plain words
What is it for?
Use it after creating, modifying, refactoring, renaming, or reconfiguring models whose columns are declared in schema.yml or models.yml.
Why use it?
A successful build only shows that the SQL ran; this check also catches mismatches between the actual output and the documented specification.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/altimateai/altimate-code/dbt-schema-verify
Any agent
npx skills add AltimateAI/altimate-code --skill dbt-schema-verify
Clone the repo
git clone --depth 1 https://github.com/AltimateAI/altimate-code

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for dbt-schema-verify

README.md
[![agentmods](https://agentmods.dev/badge/skills/altimateai/altimate-code/dbt-schema-verify.svg)](https://agentmods.dev/skills/altimateai/altimate-code/dbt-schema-verify)
Your own site
<a href="https://agentmods.dev/skills/altimateai/altimate-code/dbt-schema-verify"><img src="https://agentmods.dev/badge/skills/altimateai/altimate-code/dbt-schema-verify.svg" alt="Measured on agentmods" height="20"></a>
Per session 216 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,681 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00216 $0.01681
Opus 5 $0.00108 $0.00840
Sonnet 5 $0.00043 $0.00336
Haiku 4.5 $0.00022 $0.00168

Measured 4d ago against content hash 1ba97178e061, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

dbt-schema-verify scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

.opencode/skills/dbt-schema-verify/SKILL.md · 147 lines

How it starts

The opening of the file, as written. The whole thing — 147 lines — stays where its author put it; the contents beside it link to each section on GitHub.

dbt schema-verify

When to invoke this skill — every time

Run altimate-dbt schema-verify --model <name> before declaring any of the following tasks complete:

  • Creating a new dbt model that has (or will have) a schema.yml entry
  • Modifying an existing model whose schema.yml declares columns
  • Refactoring a CTE into its own intermediate model
  • Renaming columns or changing their order
  • Changing materialization config in a way that re-creates the table
  • Any task that says "match the schema", "produce these columns", "the output should have columns X, Y, Z", or references a _models.yml
  • Any task with AUTO_*_equality or AUTO_*_existence tests on a model

If the task touched N models, run schema-verify on all N of them, not just the last one. A build is not a verify.

How to run it

altimate-dbt schema-verify --model <name>

Note: altimate-dbt build --model <name> already runs schema-verify automatically after a successful build and includes the verdict in its response under a schema_verify field. You will see the diff in the same result that reported the build outcome — read it there before deciding the task is done. If you need to re-check after editing, call schema-verify directly.

Returns a structured JSON result:

{
  "model": "int_asana__project_user_agg",
  "verdict": "mismatch",
  "expected_columns": ["project_id", "users", "number_of_users_involved"],
  "actual_columns": ["project_id", "users"],
  "columns_extra": [],
  "columns_missing": ["number_of_users_involved"],
  "columns_reordered": [],
  "type_mismatches": []
}

How to read the verdict

verdict meaning what to do
match actual columns match the spec exactly (case-insensitive on names) DONE — proceed
mismatch one or more of columns_extra, columns_missing, columns_reordered, type_mismatches is non-empty NOT DONE — read the diff, fix the model SQL, rebuild, re-run schema-verify
no-spec the model has no columns declared in schema.yml DONE for shape-fidelity purposes — no contract to verify against

Read the full file on GitHub · 147 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 4d ago First seen · 147 lines · 216 tokens per session scan A 1ba97178e061

Subscribe to this mod's changes

dbt-schema-verify is a skill published in the GitHub repository AltimateAI/altimate-code (803 stars, last pushed 4d ago), licensed MIT. It adds 216 tokens to every session and 1,681 once invoked, about $0.0011 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Related

Other skills, from other repositories

data-divergence

Investigate why two datasets that should agree don't — two pipelines writing the same logical table, a rollup vs the detail it aggregates, a dashboard vs its source, one environment vs another. Use when row counts, totals, or date ranges disagree and the question is what happened rather than just what differs. Covers…

andre-salvati/databricks-template · 123 tokens

sql-diagram

Diagram a SQL query and explain what it shows — either its execution steps (mode=plan) or its column lineage (mode=lineage) — then trace it through small data so the defects the picture cannot show become visible. Use when asked to visualize, diagram, explain or review what a query does, how it joins its tables, or…

andre-salvati/databricks-template · 121 tokens

explore

Use this whenever you need to know what is actually in a database, warehouse, or DuckDB file before you trust it: ranked inventory of what exists, column profiles, PII detection, grain and data-quality problems, verified join inference, Mermaid ER diagrams, guarded ad-hoc SQL probes, and k-means segmentation…

exmergo/dex · 309 tokens

maintain

Use this to keep a dbt project correct as the warehouse and the business change. It detects drift on four axes and proposes the fix: schema drift (source columns and tables added, dropped, retyped, or renamed), volume drift (a row count that collapsed, a table that emptied, a load that half-failed), grain drift (a key…

exmergo/dex · 300 tokens

hugging-face-datasets

Create and manage datasets on Hugging Face Hub. Supports initializing repos, defining configs/system prompts, streaming row updates, and SQL-based dataset querying/transformation. Designed to work alongside HF MCP server for comprehensive dataset workflows.

synthetic-sciences/openscience · 49 tokens

bigquery-graph

Skill for Graph Query Language (GQL) or SQL/PGQ queries against a property graph. Includes path finding, multi-hop traversal, topological connection, shortest path, node reachability, edge connectivity, and semantic graph queries.

google/adk-python · 52 tokens