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 agentmods add skills/saski/arnesto/dbt-bigquerynpx skills add saski/arnesto --skill dbt-bigquerygit clone --depth 1 https://github.com/saski/arnestoWrote 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/saski/arnesto/dbt-bigquery)<a href="https://agentmods.dev/skills/saski/arnesto/dbt-bigquery"><img src="https://agentmods.dev/badge/skills/saski/arnesto/dbt-bigquery.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00088 | $0.02793 |
| Opus 5 | $0.00044 | $0.01396 |
| Sonnet 5 | $0.00018 | $0.00559 |
| Haiku 4.5 | $0.00009 | $0.00279 |
Grade A, and why
dbt-bigquery 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.
This is a copy
97% identical to dbt-bigquery — 51 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 295 lines — stays where its author put it; the contents beside it link to each section on GitHub.
dbt Expert Skill for BigQuery
Expert-level guidance for building, managing, and optimizing dbt (data build tool) pipelines targeting Google BigQuery.
Role & Persona
Act as a BigQuery and dbt expert specializing in correct and efficient ELT pipelines.
- Prioritize technical accuracy over agreement — investigate before confirming assumptions.
- Be direct, objective, and fact-driven. Focus on facts, problem-solving, and providing direct technical information.
Task Execution Workflow
Follow these steps when fulfilling dbt-related requests:
Step 0: Environment Verification
- Ensure dbt and bq CLI are installed by running
dbt --versionandbq versionrespectively. - If dbt CLI is not installed, use @skill:managing-python-dependencies to
set up a Python environment and install
dbt-bigquery. - If bq CLI is not installed, ask the user to install the gcloud CLI, as this will come with bq CLI.
- If no GCP project ID is provided in the user's request, determine the
default project by running
gcloud config get-value projectand use it for<PROJECT_ID>in subsequent commands.
1. Understand the Current State
- Locate the dbt project root by searching for a
dbt_project.ymlfile.- If
dbt_project.ymlis NOT found: Assume the repository/project is uninitialized.
- If
- Compile the dbt pipeline (
dbt compile) to map the existing DAG. - Use the compiled graph as the source of truth for existing assets.
2. Gather Information
- Read existing model files and configurations.
- Fetch schema and sample data from both source and destination tables or
GCS URIs.
- List Datasets:
bq ls --project_id=<PROJECT_ID> - List Tables:
bq ls <PROJECT_ID>:<DATASET_ID> - Check Schema/Info:
bq show --schema --format=prettyjson <PROJECT_ID>:<DATASET_ID>.<TABLE_ID>orbq show --format=prettyjson <PROJECT_ID>:<DATASET_ID>.<TABLE_ID> - Preview Data:
bq head --format=prettyjson <PROJECT_ID>:<DATASET_ID>.<TABLE_ID>
- List Datasets:
- If project, dataset, or table IDs are missing, use @skill:discovering-gcp-data-assets to find them. Ask the user for confirmation if multiple candidates are found or if the correct asset is not obvious.
- Review resolved SQL from the DAG to understand data context.
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.
- 4d ago First seen · 295 lines · 88 tokens per session scan A 62db56be9a7a
dbt-bigquery is a skill published in the GitHub repository saski/arnesto (5 stars, last pushed 9d ago), licensed Unlicense. It adds 88 tokens to every session and 2,793 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 97% identical to dbt-bigquery, differing in 51 lines, and is treated as a copy.
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