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-explaingit 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-explain)<a href="https://agentmods.dev/commands/justvinhhere/bigquery-expert/bq-explain"><img src="https://agentmods.dev/badge/commands/justvinhhere/bigquery-expert/bq-explain.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 | $0.00021 | $0.00297 |
| Opus 5 | $0.00010 | $0.00148 |
| Sonnet 5 | $0.00004 | $0.00059 |
| Haiku 4.5 | $0.00002 | $0.00030 |
Grade A, and why
bq-explain 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 5d 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 Feature Explainer
Explain the requested BigQuery feature using the bigquery-features skill.
Instructions
-
Determine the feature to explain:
- If an argument is provided, use it as the feature name or question.
- If no argument is provided, look for the most recent BigQuery feature question in the current conversation.
- If no feature is identifiable, ask the user what they want to learn about.
-
Explain the feature using the bigquery-features skill, covering all four aspects:
- What it is: concise definition.
- When to use it: concrete use cases and when to prefer it over alternatives.
- Working example: complete, runnable BigQuery SQL.
- Common pitfalls: gotchas, limits, performance traps.
-
Cross-reference cost implications using the
bigquery-optimizationskill when the feature affects cost or performance (e.g., MERGE DML quotas, BQML slot consumption, BI Engine reservations). -
Output format:
## <Feature Name>
### What It Is
<definition>
### When to Use
<use cases>
### Example
<working SQL>
### Pitfalls
<gotchas and limits>
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.
- 5d ago First seen · 42 lines · 21 tokens per session scan A 65cd24aa6d81
bq-explain is a command published in the GitHub repository justvinhhere/bigquery-expert (15 stars, last pushed 4mo ago), licensed Apache-2.0. It adds 21 tokens to every session and 297 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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