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.
npx skills add dbt-labs/dbt-agent-skills --skill using-dbt-for-analytics-engineeringgit clone --depth 1 https://github.com/dbt-labs/dbt-agent-skillsWrote 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.
[](https://agentmods.dev/skills/dbt-labs/dbt-agent-skills/using-dbt-for-analytics-engineering)<a href="https://agentmods.dev/skills/dbt-labs/dbt-agent-skills/using-dbt-for-analytics-engineering"><img src="https://agentmods.dev/badge/skills/dbt-labs/dbt-agent-skills/using-dbt-for-analytics-engineering/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/dbt-labs/dbt-agent-skills/using-dbt-for-analytics-engineering"><img src="https://agentmods.dev/badge/skills/dbt-labs/dbt-agent-skills/using-dbt-for-analytics-engineering.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk warn
- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 106 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00067 | $0.01614 |
| Opus 5.5 | $0.00027 | $0.00646 |
| Sonnet 5.5 | $0.00013 | $0.00323 |
| Haiku 4.5 | $0.00007 | $0.00161 |
Grade A, and why
using-dbt-for-analytics-engineering 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 1mo 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.
Copies of this mod
2 near-identical copies found in the catalogue:
- dbt-analytics-engineering — 92% identical, 19 lines differ
- using-dbt-for-analytics-engineering — 89% identical, 6 lines differ
How it starts
The opening of the file, as written. The whole thing — 107 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Using dbt for Analytics Engineering
Core principle: Apply software engineering discipline (DRY, modularity, testing) to data transformation work through dbt's abstraction layer.
STOP — is this a breaking change to a model with consumers? Renaming, removing, or retyping a column — on a model that downstream models, exposures, or external/BI consumers depend on — is a breaking change. Do not edit it in place (that breaks those consumers the moment it deploys). REQUIRED SUB-SKILL: Use the working-with-dbt-mesh skill to roll it out with model versions (and a latest version pointer) so consumers get a migration window. Come back here for the SQL once the versioning approach is decided.
When to Use
- Building new dbt models, sources, or tests
- Modifying existing model logic or configurations
- Refactoring a dbt project structure
- Creating analytics pipelines or data transformations
- Working with warehouse data that needs modeling
Do NOT use for:
- Querying the semantic layer (use the
answering-natural-language-questions-with-dbtskill) - Breaking changes to a model with consumers (column rename/remove/retype) — use the
working-with-dbt-meshskill to version the model instead of editing in place
Reference Guides
This skill includes detailed reference guides for specific techniques. Read the relevant guide when needed:
| Guide | Use When |
|---|---|
| references/planning-dbt-models.md | Building new models - work backwards from desired output and use dbt show to validate results |
| references/discovering-data.md | Exploring unfamiliar sources or onboarding to a project |
| references/writing-data-tests.md | Adding tests - prioritize high-value tests over exhaustive coverage |
| references/debugging-dbt-errors.md | Fixing project parsing, compilation, or database errors |
| references/evaluating-impact-of-a-dbt-model-change.md | Assessing downstream effects before modifying models |
| references/writing-documentation.md | Write documentation that doesn't just restate the column name |
| references/managing-packages.md | Installing and managing dbt packages |
What ships with it
8 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- references/debugging-dbt-errors.md 4.1 KB
- references/discovering-data.md 6.4 KB
- references/evaluating-impact-of-a-dbt-model-change.md 3.5 KB
- references/managing-packages.md 2.4 KB
- references/planning-dbt-models.md 7.1 KB
- references/writing-data-tests.md 6.2 KB
- references/writing-documentation.md 1.0 KB
- scripts/review_run_results.md 1.7 KB
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.
- 1mo ago First seen · 107 lines · 67 tokens per session scan A bcbf2ea18ff5
using-dbt-for-analytics-engineering is a skill published in the GitHub repository dbt-labs/dbt-agent-skills (746 stars, last pushed 3d ago), licensed Apache-2.0. It adds 67 tokens to every session and 1,614 once invoked, about $0.0003 per session on Opus 5.5. 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.
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