ClaudeSuperPower

gcp-spark

Skill

Develops and executes Spark code on Dataproc Clusters and Serverless. Reads and writes data using BigLake Iceberg catalogs, BigQuery and Spanner. Debugs execution failures. Use when: - Writing Spark ETL pipelines on GCP. - Training or running inference with ML models with spark on GCP. - Managing Sp

Install

git clone https://github.com/gemini-cli-extensions/data-agent-kit-starter-pack.git ~/.claude/skills/gcp-spark

What is gcp-spark?

Develops and executes Spark code on Dataproc Clusters and Serverless. Reads and writes data using BigLake Iceberg catalogs, BigQuery and Spanner. Debugs execution failures. Use when: - Writing Spark ETL pipelines on GCP. - Training or running inference with ML models with spark on GCP. - Managing Spark clusters, jobs, batches, and interactive sessions. Don't use when: - Writing generic Python scripts that don't use Spark. - Performing simple SQL queries that can be done directly in BigQuery.

What this can do

Capabilities declared in this component's own frontmatter — not inferred.

Inherit all session tools

Declares no tool restrictions — inherits every session tool

~124 tokens of context used while enabled, before you invoke anything

Documentation

README · ~2 min read

Spark on Dataproc

[!IMPORTANT]

You MUST ALWAYS follow the Task Execution Workflow when writing spark code.

Task Execution Workflow

  1. Understand schemas: ALWAYS use @skill:discovering-gcp-data-assets skill or references/schema_direct_inspection.md to understand input and output schemas. Include the schema in your thought process BEFORE generating any code. Do NOT guess column names.
  2. Generate spark code:

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