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 working-with-dbt-meshgit 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/working-with-dbt-mesh)<a href="https://agentmods.dev/skills/dbt-labs/dbt-agent-skills/working-with-dbt-mesh"><img src="https://agentmods.dev/badge/skills/dbt-labs/dbt-agent-skills/working-with-dbt-mesh/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/working-with-dbt-mesh"><img src="https://agentmods.dev/badge/skills/dbt-labs/dbt-agent-skills/working-with-dbt-mesh.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector pass
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 | $0.00142 | $0.04186 |
| Opus 5 | $0.00071 | $0.02093 |
| Sonnet 5 | $0.00028 | $0.00837 |
| Haiku 4.5 | $0.00014 | $0.00419 |
Grade A, and why
working-with-dbt-mesh 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 11d 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.
How it starts
The opening of the file, as written. The whole thing — 271 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Working with dbt Mesh
Core principle: In a mesh project, upstream data comes through ref(), not source(). Every cross-project reference requires the project name. When in doubt, read dependencies.yml first.
When to Use
- Making a potentially breaking change to a model — renaming, removing, or retyping a column — especially when other models, exposures, or BI tools depend on it. Assess the blast radius before changing it, and reach for model versions rather than editing in place.
- Versioning a model (
versions:,latest_version,latest_version_pointer,deprecation_date) — this applies in a single project, not just multi-project setups - Working in a dbt project that references models from other dbt projects
- Resolving ambiguity when multiple upstream projects have similarly-named models (e.g. multiple
stg_models) - Adding model contracts, access modifiers, or groups
- Setting up cross-project references with
dependencies.yml - Splitting a monolithic dbt project into multiple mesh projects
Do NOT use for:
- General model building or debugging (use the
using-dbt-for-analytics-engineeringskill) - Unit testing models (use the
adding-dbt-unit-testskill) - Semantic layer work (use the
building-dbt-semantic-layerskill)
First: Orient Yourself in a Multi-Project Setup
Before writing or modifying any SQL in a project that uses dbt Mesh, follow these steps:
1. Read dependencies.yml
This file at the project root tells you which upstream projects exist:
# dependencies.yml
projects:
- name: core_platform
- name: marketing_platform
If this file has a projects: key, you are in a multi-project mesh setup. Every model you reference from those upstream projects must use cross-project ref().
2. Understand how upstream data gets into this project
In a mesh setup, upstream project models replace what would alternatively be sources:
| Alternative | Mesh multi-project |
|---|---|
{{ source('stripe', 'payments') }} |
{{ ref('core_platform', 'stg_payments') }} |
| Data comes from raw database tables | Data comes from another dbt project's public models |
Defined in sources.yml |
Declared in dependencies.yml |
What ships with it
4 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.
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.
- 11d ago First seen · 271 lines · 142 tokens per session scan A 69a2701a0dfd
working-with-dbt-mesh is a skill published in the GitHub repository dbt-labs/dbt-agent-skills (709 stars, last pushed today), licensed Apache-2.0. It adds 142 tokens to every session and 4,186 once invoked, about $0.0007 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.
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