analyze-usage

analyze-usage is a skill for Claude Code, Codex from openshift-eng/ai-helpers. It costs 19 tokens per session (2,627 once invoked), scanned A, original, Apache-2.0.

A BigQuery usage-analysis tool for Google Cloud projects. BigQuery is Google's service for storing and querying large datasets.

In plain words
What is it for?
It is for reviewing BigQuery activity over a chosen time period, identifying expensive or heavy queries, and getting optimization recommendations.
Why use it?
It helps you find which queries and users consume the most resources, where costs come from, and which queries may be slow or inefficient.

Skill for Claude CodeCodex

Part of the bigquery plugin — 2 skills, 1 command shipped together

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.

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

Made for: Claude Code, Codex.

Or install bigquery, the plugin that ships this one along with the rest of its 2 skills, 1 command.

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-usage

README.md
[![agentmods](https://agentmods.dev/badge/skills/openshift-eng/ai-helpers/analyze-usage.svg)](https://agentmods.dev/skills/openshift-eng/ai-helpers/analyze-usage)
Your own site
<a href="https://agentmods.dev/skills/openshift-eng/ai-helpers/analyze-usage"><img src="https://agentmods.dev/badge/skills/openshift-eng/ai-helpers/analyze-usage.svg" alt="Measured on agentmods" 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 2,627 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found 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 $0.00019 $0.02627
Opus 5 $0.00010 $0.01314
Sonnet 5 $0.00004 $0.00525
Haiku 4.5 $0.00002 $0.00263

Measured 5d ago against content hash eb5cea643b42, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

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 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.

Origin

Copies of this mod

1 near-identical copy found in the catalogue:

plugins/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. 5d ago First seen · 352 lines · 19 tokens per session scan A eb5cea643b42

Subscribe to this mod's changes

analyze-usage is a skill published in the GitHub repository openshift-eng/ai-helpers (114 stars, last pushed yesterday), licensed Apache-2.0. It adds 19 tokens to every session and 2,627 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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