Analyze BigQuery Usage

Analyze BigQuery Usage is a skill for Claude Code, Codex from wangke19/gemini-ai-helpers. It costs 19 tokens per session (2,612 once invoked), scanned A, a copy of analyze-usage, Apache-2.0.

A skill that analyzes Google BigQuery usage, costs, and query performance for a project. It uses Google Cloud’s bq command-line tool and BigQuery metadata tables to collect the analysis.

In plain words
What is it for?
Use it to check prerequisites and access, review a selected time period, summarize queries and processed data, rank expensive work, detect common query patterns, and suggest cost or performance improvements.
Why use it?
It shows which queries, users, and service accounts consume the most data or money, so teams can target specific changes.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: positional $N argument.

Good fit Use it to check prerequisites and access, review a selected time period, summarize queries and processed data, rank expensive work, detect common query patterns, and suggest cost or performance improvements.

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Install with agentmods
npx agentmods add skills/wangke19/gemini-ai-helpers/analyze-usage
Install

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.

Any agent
npx skills add wangke19/gemini-ai-helpers --skill analyze-usage
Clone the repo
git clone --depth 1 https://github.com/wangke19/gemini-ai-helpers

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for Analyze BigQuery Usage

README.md
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agentmods 80×15 button for Analyze BigQuery Usage

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<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>
Per session 19 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,612 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin 97% copy Near-identical to another mod in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 8d ago against content hash e132843cd0fd, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

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.

Origin

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.

extensions/bigquery/skills/analyze-usage/SKILL.md · 352 lines

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 (bq command-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.list permission 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 bq command 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

Read the full file on GitHub · 352 lines

Changes

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.

  1. 8d ago First seen · 352 lines · 19 tokens per session scan A e132843cd0fd

Subscribe to this mod's changes

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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