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 agentmods add skills/techequitycloud/rad-modules/bigquery-basicsnpx skills add techequitycloud/rad-modules --skill bigquery-basicsgit clone --depth 1 https://github.com/techequitycloud/rad-modulesWhat 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 | $0.00085 | $0.01210 |
| Opus 5 | $0.00043 | $0.00605 |
| Sonnet 5 | $0.00017 | $0.00242 |
| Haiku 4.5 | $0.00009 | $0.00121 |
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 2d 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
91% identical to bigquery-basics — 32 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 — 104 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 -
Create a Dataset:
bq mk --dataset --location=US my_dataset -
Create a Table:
Create a file named
schema.jsonwith your table schema:[ { "name": "name", "type": "STRING", "mode": "REQUIRED" }, { "name": "post_abbr", "type": "STRING", "mode": "NULLABLE" } ]Then create the table with the
bqtool: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
bqcommand-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.
If you need product information not found in these references, use the
Developer Knowledge MCP server search_documents tool.
What ships with it
6 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.
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.
- 2d ago First seen · 104 lines · 85 tokens per session scan A d8d9bc7a0ade
bigquery-basics is a skill published in the GitHub repository techequitycloud/rad-modules (2 stars, last pushed 8d ago), licensed MPL-2.0. It adds 85 tokens to every session and 1,210 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 91% identical to bigquery-basics, differing in 32 lines, and is treated as a copy.
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