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 saski/arnesto --skill developing-with-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/developing-with-bigquery)<a href="https://agentmods.dev/skills/saski/arnesto/developing-with-bigquery"><img src="https://agentmods.dev/badge/skills/saski/arnesto/developing-with-bigquery/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/saski/arnesto/developing-with-bigquery"><img src="https://agentmods.dev/badge/skills/saski/arnesto/developing-with-bigquery.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00063 | $0.00515 |
| Opus 5.5 | $0.00025 | $0.00206 |
| Sonnet 5.5 | $0.00013 | $0.00103 |
| Haiku 4.5 | $0.00006 | $0.00052 |
Grade A, and why
developing-with-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 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.
What it actually says
This skill provides comprehensive guidance for BigQuery services, optimizations, and data handling. It acts as a routing table for specialized BigQuery topics.
[!IMPORTANT] For general standards on running BigQuery in notebooks (SQL cells,
exportkeyword), see@skill:notebook-guidance.
[!IMPORTANT] You MUST check the data size before deciding on which libraries to use. Use the data size to justify your decision.
Refer to the following resources for expert guidance on specific BigQuery features:
1. Query Optimization
Performance and efficiency guidelines for BigQuery SQL. Includes rules for column pruning, pushdown, and materialization strategies. - Guide: OPTIMIZATION.md
2. BigFrames (BigQuery DataFrames)
Guidelines for generating valid BigFrames code for data manipulation, model development, and visualization. - Guide: BIGFRAMES.md
Bigframes should be the default library/tool as it is more efficient than using the BigQuery Python client library.
3. BigQuery ML & AI Functions (BQML SQL)
Usage rules and syntax standards for all BigQuery AI/ML functions via SQL (Forecasting, Generative AI, Classification, etc.). - Guide: BQML.md - Functions Reference: - AI.FORECAST - AI.EVALUATE - AI.GENERATE_TABLE - AI.GENERATE_EMBEDDING - Remote Models CONTRIBUTION_ANALYSIS VECTOR_SEARCH
4. Notebook SQL cells
Refer to @skill:notebook-guidance for standards on running BigQuery in
notebooks.
What ships with it
10 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.
- resources/ai-evaluate.md 2.2 KB
- resources/ai-forecast.md 3.0 KB
- resources/ai-generate-embedding.md 1.6 KB
- resources/ai-generate-table.md 2.9 KB
- resources/BIGFRAMES.md 2.6 KB
- resources/BQML.md 1.7 KB
- resources/ml-contribution-analysis.md 4.8 KB
- resources/OPTIMIZATION.md 1.4 KB
- resources/remote-models.md 871 B
- resources/vector-search.md 4.0 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 · 59 lines · 63 tokens per session scan A 020bd4ed7067
developing-with-bigquery is a skill published in the GitHub repository saski/arnesto (5 stars, last pushed yesterday), licensed Unlicense. It adds 63 tokens to every session and 515 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 2026-08-31.
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