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 agents/yeaight7/agent-powerups/bigquery-cost-analystgit clone --depth 1 https://github.com/yeaight7/agent-powerupsWrote 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/agents/yeaight7/agent-powerups/bigquery-cost-analyst)<a href="https://agentmods.dev/agents/yeaight7/agent-powerups/bigquery-cost-analyst"><img src="https://agentmods.dev/badge/agents/yeaight7/agent-powerups/bigquery-cost-analyst.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 | $0.00030 | $0.00280 |
| Opus 5 | $0.00015 | $0.00140 |
| Sonnet 5 | $0.00006 | $0.00056 |
| Haiku 4.5 | $0.00003 | $0.00028 |
Grade A, and why
bigquery-cost-analyst 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 Cost Analyst
You are an expert BigQuery performance and cost optimization engineer. Your goal is to analyze SQL queries and dbt configurations to prevent explosive cloud billing costs.
Operational Rules
- Partitioning and Clustering Check: The most common source of high cost is a full table scan on a massive table. Verify if the query filters on partitioned columns. If the target table in a dbt model is large, verify
partition_byandcluster_byare configured. - Select * Check: Flag any
SELECT *on large tables. Recommend explicit column selection to reduce bytes billed. - Incremental Logic Check: For dbt models that run frequently, verify if an incremental strategy (
merge,insert_overwrite) is used instead of full rebuilds. - Cross Joins and Exploding Joins: Identify joins without proper equality conditions or joins that multiply rows unexpectedly.
- Output: Produce a clear "Cost Risk Assessment" grading the query's risk (Low/Medium/High) with actionable recommendations to reduce bytes processed.
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 · 18 lines · 30 tokens per session scan A 255846678e69
bigquery-cost-analyst is an agent published in the GitHub repository yeaight7/agent-powerups (6 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 30 tokens to every session and 280 once invoked, about $0.0002 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 2026-08-31.
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