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adamkingsbury
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write-script-bigquery

Generate deterministic parameterized BigQuery scripts with YAML metadata.

Updated Feb 21, 2026
Search Tags:#automation#sql#data-analysis#reproducibility#bigquery#parameterized#query-generation
One-click install
npx skills add https://github.com/adamkingsbury/unified-data-model --skill write-script-bigquery-adamkingsbury
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: write-script-bigquery
Source: https://github.com/adamkingsbury/unified-data-model/tree/main/.claude/skills/write-script-bigquery
Command: npx skills add https://github.com/adamkingsbury/unified-data-model --skill write-script-bigquery-adamkingsbury

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

BigQuery users often struggle to create repeatable, parameterized queries, leading to inconsistent results and manual rework. This skill guides you to generate deterministic scripts that can be reused across projects and datasets.

Core Features & Use Cases

  • Deterministic script generation for reproducible BigQuery queries.
  • Parameterized queries using inline comments to define @name variables, enabling easy reuse.
  • Scalable for multi-project analytics workflows, with clear separation of concerns and documentation.

Quick Start

Create a parameterized BigQuery script and run wmill script generate-metadata to produce .script.yaml and .lock files.

Frequently Asked Questions about write-script-bigquery

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I create parameterized BigQuery scripts for reusable queries?▼

Parameterized BigQuery scripts are generated by defining @name variables through inline comments in SQL, producing deterministic queries that can be reused across datasets and projects. YAML frontmatter metadata provides discovery and description for each script.

Why do my BigQuery queries return inconsistent results across different analytics projects?▼

Inconsistent BigQuery results often stem from non-deterministic queries lacking parameterization. Generating deterministic scripts with inline comment parameters enforces repeatable query execution, ensuring consistent outputs across datasets and projects.

What is the best way to automate BigQuery query generation for multi-project workflows?▼

Automating BigQuery query generation for multi-project workflows involves creating deterministic scripts with clear separation of concerns and documentation. Parameterized inline comments enable scalable, repeatable query execution across projects.

How do I generate metadata for a BigQuery script to make it discoverable?▼

BigQuery script metadata is generated by running the wmill script generate-metadata command, which produces .script.yaml and .lock files that enable script discovery and description via YAML frontmatter.

Can I use inline comments to define variables in BigQuery SQL?▼

Yes, inline comments in BigQuery SQL are used to define @name parameter variables. This approach enables parameterized query generation, allowing scripts to be easily reused and executed deterministically across datasets.

Does this approach to BigQuery query generation work without external dependencies?▼

Yes, deterministic BigQuery script generation relies on inline SQL comments and YAML frontmatter metadata without requiring external dependencies, making it suitable for reproducible data analytics workflows.

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