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 wangke19/gemini-ai-helpers --skill analyze-usagegit clone --depth 1 https://github.com/wangke19/gemini-ai-helpersWrote 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/wangke19/gemini-ai-helpers/analyze-usage)<a href="https://agentmods.dev/skills/wangke19/gemini-ai-helpers/analyze-usage"><img src="https://agentmods.dev/badge/skills/wangke19/gemini-ai-helpers/analyze-usage/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/wangke19/gemini-ai-helpers/analyze-usage"><img src="https://agentmods.dev/badge/skills/wangke19/gemini-ai-helpers/analyze-usage.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 | $0.00019 | $0.02612 |
| Opus 5 | $0.00010 | $0.01306 |
| Sonnet 5 | $0.00004 | $0.00522 |
| Haiku 4.5 | $0.00002 | $0.00261 |
Grade A, and why
Analyze BigQuery Usage 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 8d 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 analyze-usage — 8 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 — 352 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Analyze BigQuery Usage
This skill performs comprehensive analysis of BigQuery usage patterns, costs, and query performance for a given project. It identifies expensive queries, heavy users, and provides actionable optimization recommendations.
When to Use This Skill
This skill is automatically invoked by the /bigquery:analyze-usage command to perform usage analysis.
Prerequisites
- Google Cloud SDK (
bqcommand-line tool) must be installed - User must have BigQuery read access to the project
- User must be authenticated (
gcloud auth login) - User needs
bigquery.jobs.listpermission at minimum
Parameters
When invoked, this skill expects:
- Project ID: The GCP project ID to analyze (required)
- Timeframe: Time period for analysis in hours (e.g., 24, 168 for 7 days)
Analysis Workflow
1. Validate Prerequisites
First, verify the environment is ready:
- Check if
bqcommand is available - Verify project access
- Parse timeframe into hours
2. Collect Usage Data
Execute the following BigQuery queries against INFORMATION_SCHEMA:
Total Usage Summary
SELECT
COUNT(*) as total_queries,
ROUND(SUM(total_bytes_processed) / POW(10, 12), 2) as total_tb_scanned,
ROUND(SUM(total_bytes_processed) / POW(10, 12) * 6.25, 2) as estimated_cost_usd
FROM `region-us`.INFORMATION_SCHEMA.JOBS_BY_PROJECT
WHERE creation_time >= TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL @hours HOUR)
AND job_type = 'QUERY'
AND state = 'DONE'
AND statement_type != 'SCRIPT'
Usage by User/Service Account
SELECT
user_email,
COUNT(*) as query_count,
ROUND(SUM(total_bytes_processed) / POW(10, 12), 2) as total_tb_scanned,
ROUND(SUM(total_bytes_processed) / POW(10, 12) * 6.25, 2) as estimated_cost_usd,
ROUND(AVG(total_bytes_processed) / POW(10, 9), 2) as avg_gb_per_query
FROM `region-us`.INFORMATION_SCHEMA.JOBS_BY_PROJECT
WHERE creation_time >= TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL @hours HOUR)
AND job_type = 'QUERY'
AND state = 'DONE'
AND statement_type != 'SCRIPT'
GROUP BY user_email
ORDER BY total_tb_scanned DESC
LIMIT 20
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.
- 8d ago First seen · 352 lines · 19 tokens per session scan A e132843cd0fd
Analyze BigQuery Usage is a skill published in the GitHub repository wangke19/gemini-ai-helpers (2 stars, last pushed 5mo ago), licensed Apache-2.0. It adds 19 tokens to every session and 2,612 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 97% identical to analyze-usage, differing in 8 lines, and is treated as a copy.
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