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 commands/justvinhhere/bigquery-expert/bq-generategit clone --depth 1 https://github.com/justvinhhere/bigquery-expertWrote 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/commands/justvinhhere/bigquery-expert/bq-generate)<a href="https://agentmods.dev/commands/justvinhhere/bigquery-expert/bq-generate"><img src="https://agentmods.dev/badge/commands/justvinhhere/bigquery-expert/bq-generate.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 5.1 | $0.00020 | $0.00300 |
| Opus 5 | $0.00010 | $0.00150 |
| Sonnet 5 | $0.00004 | $0.00060 |
| Haiku 4.5 | $0.00002 | $0.00030 |
Grade A, and why
bq-generate 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 6d 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
BigQuery Query Generation
Generate optimized BigQuery SQL using the bigquery-query-generation skill.
Instructions
-
Determine what SQL to generate from the argument or current conversation context. If neither provides a clear description, ask the user what data they need.
-
Gather schema context if available. Check whether the user has mentioned specific table names, column names, or project/dataset references. If not, use descriptive placeholders and state assumptions.
-
Generate the SQL using the
bigquery-query-generationskill. Proactively avoid all 11 anti-patterns from thebigquery-optimizationskill while generating. -
Output using this format:
## Generated Query (fenced SQL code block) ## Assumptions - Schema, data type, and business logic assumptions. - Placeholder table/column names that need replacing. ## Anti-Patterns Proactively Avoided - List only the anti-patterns that were relevant to this query and actively avoided. ## Customization Notes - Suggestions for adapting the query: filters, additional columns, partitioning, etc. -
Prefer generating with stated assumptions over asking too many clarifying questions. Generate a working query first, then offer to refine.
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.
- 6d ago First seen · 37 lines · 20 tokens per session scan A 003f8a7a7d99
bq-generate is a command published in the GitHub repository justvinhhere/bigquery-expert (15 stars, last pushed 4mo ago), licensed Apache-2.0. It adds 20 tokens to every session and 300 once invoked, about $0.0001 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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