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.
git 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/commands/wangke19/gemini-ai-helpers/analyze-usage)<a href="https://agentmods.dev/commands/wangke19/gemini-ai-helpers/analyze-usage"><img src="https://agentmods.dev/badge/commands/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/commands/wangke19/gemini-ai-helpers/analyze-usage"><img src="https://agentmods.dev/badge/commands/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 | $0.00009 | $0.01828 |
| Opus 5.5 | $0.00004 | $0.00731 |
| Sonnet 5.5 | $0.00002 | $0.00366 |
| Haiku 4.5 | $0.00001 | $0.00183 |
Grade A, and why
analyze-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 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.
This is a copy
95% identical to analyze-usage — 30 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 — 219 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Name
bigquery:analyze-usage
Synopsis
/bigquery:analyze-usage <project-id> <timeframe>
/bigquery:analyze-usage openshift-ci-data-analysis "24 hours"
/bigquery:analyze-usage my-project "7 days"
Description
The analyze-usage command provides 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.
This command helps answer questions like:
- Which users or service accounts are consuming the most data?
- What are the most expensive queries?
- Which query patterns are running most frequently?
- How can we reduce BigQuery costs?
- Are we over any usage thresholds?
The analysis includes:
- Total usage summary (queries, data scanned, estimated costs)
- Usage breakdown by user/service account
- Per-user deep dive analysis for top 2-3 users
- Top individual queries by cost
- Query pattern analysis to identify optimization opportunities
- Specific, actionable optimization recommendations
- Optional markdown report generation
Implementation
This command uses the bigquery:analyze-usage skill to perform the 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)
Steps
-
Parse and Validate Arguments:
- If project-id is missing: Use AskUserQuestion to prompt for it
- If timeframe is missing: Use AskUserQuestion to prompt for it (options: "1 hour", "6 hours", "24 hours", "7 days", "30 days")
- Parse timeframe into hours (e.g., "24 hours" → 24, "7 days" → 168)
-
Invoke the analyze-usage Skill:
Use the Skill tool to invoke "bigquery:analyze-usage"The skill will handle all the data collection and analysis.
-
Present Results: The skill returns a comprehensive report. Present it to the user in a clear, readable format with:
- Executive summary at the top
- Tables for user usage and top queries
- Per-user deep dive for top 2-3 users showing their specific query patterns
- Detailed query pattern analysis
- Prioritized optimization recommendations
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 · 219 lines · 9 tokens per session scan A af448352adf8
analyze-usage is a command published in the GitHub repository wangke19/gemini-ai-helpers (2 stars, last pushed 6mo ago), licensed Apache-2.0. It adds 9 tokens to every session and 1,828 once invoked, about $0.0000 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 95% identical to analyze-usage, differing in 30 lines, and is treated as a copy.
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