From magic-powers
Use when designing data quality checks, validating pipeline outputs, setting up schema validation, or using Dataform/Dataplex/Cloud DQ. Covers GCP-PDE domain: Prepare and use data for analysis (~10-15%).
npx claudepluginhub kienbui1995/magic-powers --plugin magic-powersThis skill uses the workspace's default tool permissions.
- Designing data quality checks for a pipeline
Generates design tokens/docs from CSS/Tailwind/styled-components codebases, audits visual consistency across 10 dimensions, detects AI slop in UI.
Records polished WebM UI demo videos of web apps using Playwright with cursor overlay, natural pacing, and three-phase scripting. Activates for demo, walkthrough, screen recording, or tutorial requests.
Delivers idiomatic Kotlin patterns for null safety, immutability, sealed classes, coroutines, Flows, extensions, DSL builders, and Gradle DSL. Use when writing, reviewing, refactoring, or designing Kotlin code.
| Tool | Use case |
|---|---|
| Dataform | SQL-based transformation + data quality tests in BigQuery |
| Cloud Data Quality (Cloud DQ) | Rule-based DQ checks on BigQuery tables at scale |
| Dataplex | Data governance, discovery, quality across data lake |
| Dataprep by Trifacta | Visual data cleaning and profiling (no-code/low-code) |
| BigQuery assertions | Inline SQL checks in queries or scheduled queries |
Use Dataform assertions to catch data quality issues:
assert_ prefix files run as data quality checks