From magic-powers
Use when building no-code/low-code data transformations in Microsoft Fabric with Dataflow Gen2, configuring Power Query transformations, setting up incremental refresh, or studying for DP-700 (Microsoft Fabric Data Engineer Associate).
npx claudepluginhub kienbui1995/magic-powers --plugin magic-powersThis skill uses the workspace's default tool permissions.
- Building data transformations without writing PySpark code in Microsoft Fabric
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.
| Destination | Use Case |
|---|---|
| Fabric Lakehouse (table) | Most common; Delta table in Lakehouse Tables/ |
| Fabric Warehouse (table) | SQL-first teams; T-SQL accessible |
| Azure SQL Database | Hybrid scenarios; push to existing SQL workloads |
| Fabric KQL Database | Real-time analytics (Eventhouse) |
ModifiedDate, EventTimestamp)Table.SelectRows(Source, each [ModifiedDate] >= RangeStart and [ModifiedDate] < RangeEnd)| Criteria | Dataflow Gen2 | Spark Notebook |
|---|---|---|
| User skill | Business analyst, no-code | Data engineer, PySpark |
| Transformation complexity | Low-medium (Power Query M) | High (arbitrary Python/Spark) |
| Scale | Medium datasets | Very large datasets (TB+) |
| Scheduling | Built-in refresh schedule | Manual or Pipeline trigger |
| ML/AI logic | Not supported | Full Python ecosystem |
| Best for | ETL from business systems | Large-scale data engineering |
Decision rule: Choose Dataflow Gen2 for straightforward ETL from standard connectors; use Notebooks for complex transformations, ML, or very large data volumes.