What is bigquery-basics?

A guide for using BigQuery, Google's service for storing and analysing large datasets with SQL and Python. It covers datasets, tables, queries, command-line use, built-in machine learning, location analysis, and reporting tools.

Use it to create datasets and tables, define table fields, run SQL queries, manage BigQuery jobs, and use BigQuery's machine-learning and analysis features.

It gives you the commands and concepts needed to work with large datasets without managing database servers yourself.

bigquery-basics — Skill for Claude Code and Codex by richardhe-fundame

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 richardhe-fundamenta/practical-gcp-examples --skill bigquery-basics

Clone the repo

git clone --depth 1 https://github.com/richardhe-fundamenta/practical-gcp-examples

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.

README.md

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Your own site

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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.

Your own site · 80×15

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Per session 85 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 1,161 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. Third-party audits

NVIDIA SkillSpector pass 7 Sept 2026

How audits are shown

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.

Model	Per session	Once invoked
Fable 5.1	$0.00085	$0.01161
Opus 5	$0.00043	$0.00580
Sonnet 5	$0.00017	$0.00232
Haiku 4.5	$0.00009	$0.00116

Measured 10d ago against content hash ff5345c81e5a, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

bigquery-basics 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 10d 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

2 near-identical copies found in the catalogue:

bigquery-basics — 91% identical, 38 lines differ
bigquery-basics — 91% identical, 32 lines differ

adk-agy-agent/skills/bigquery-basics/SKILL.md · 110 lines

How it starts

The opening of the file, as written. The whole thing — 110 lines — stays where its author put it; the contents beside it link to each section on GitHub.

BigQuery Basics

BigQuery is a serverless, AI-ready data platform that enables high-speed analysis of large datasets using SQL and Python. Its disaggregated architecture separates compute and storage, allowing them to scale independently while providing built-in machine learning, geospatial analysis, and business intelligence capabilities.

Setup and Basic Usage

Enable the BigQuery API:

gcloud services enable bigquery.googleapis.com --quiet

Create a Dataset:

bq mk --dataset --location=US my_dataset

Create a Table:

Create a file named schema.json with your table schema:

[
  {
    "name": "name",
    "type": "STRING",
    "mode": "REQUIRED"
  },
  {
    "name": "post_abbr",
    "type": "STRING",
    "mode": "NULLABLE"
  }
]

Then create the table with the bq tool:

bq mk --table my_dataset.mytable schema.json

Run a Query:

bq query --use_legacy_sql=false \
'SELECT name FROM `bigquery-public-data.usa_names.usa_1910_2013` \
WHERE state = "TX" LIMIT 10'

Reference Directory

Core Concepts: Storage types, analytics workflows, and BigQuery Studio features.
CLI Usage: Essential bq command-line tool operations for managing data and jobs.
Client Libraries: Using Google Cloud client libraries for Python, Java, Node.js, and Go.
MCP Usage: Using the BigQuery remote MCP server and Gemini CLI extension.
Infrastructure as Code: Terraform examples for datasets, tables, and reservations.
IAM & Security: Roles, permissions, and data governance best practices.
AI Forecast: Leveraging pre-trained TimesFM model for forecasting without custom training.

Read the full file on GitHub · 110 lines

Files

What ships with it

9 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

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.

10d ago First seen · 110 lines · 85 tokens per session scan A ff5345c81e5a

Subscribe to this mod's changes

bigquery-basics is a skill published in the GitHub repository richardhe-fundamenta/practical-gcp-examples (57 stars, last pushed 24d ago), licensed MIT. It adds 85 tokens to every session and 1,161 once invoked, about $0.0004 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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