skills.rest
sparkflows
sparkflowsOFFICIAL
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workflow-creation

Convert natural language workflow descriptions into import-ready Sparkflows JSON.

Updated Apr 20, 2026
Search Tags:#automation#workflow#json#etl#dag#node#sparkflows
One-click install
npx skills add https://github.com/sparkflows/vscode --skill workflow-creation-sparkflows
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: workflow-creation
Source: https://github.com/sparkflows/vscode/tree/main/workflow-creation
Command: npx skills add https://github.com/sparkflows/vscode --skill workflow-creation-sparkflows

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires requests, and includes scripts (resource) components.

What problem does it solve?

Translates natural-language workflow ideas into ready-to-import Sparkflows JSON by assembling node definitions and wiring edges according to a user’s description.

Core Features & Use Cases

  • Automated workflow generation: Converts intents into a structured workflow JSON with valid nodes, IDs, and connections that Sparkflows can import.
  • Catalog-driven node selection: Maps user requests to cataloged node JSONs and ensures engine compatibility (Scala or PySpark).
  • Use Case: Build a new ETL pipeline from a plain-English description, then export the workflow JSON for import into Sparkflows.

Quick Start

Describe your desired workflow in plain English, and the skill will generate and save a ready-to-import Sparkflows workflow JSON.

Frequently Asked Questions about workflow-creation

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

FAQPage Schema
How do I generate an ETL workflow from a natural language description?▼

To generate an ETL workflow from a natural language description, you provide a plain English prompt mapping to cataloged node JSONs, building a DAG with unique IDs that outputs import-ready Sparkflows JSON.

Can I create Sparkflows JSON workflows for PySpark and Scala engines?▼

Yes, you can create Sparkflows JSON workflows for PySpark and Scala engines because the generation process maps node definitions from a catalog while enforcing strict engine compatibility.

How do I build a data processing DAG with valid nodes and connections?▼

You build a data processing DAG by submitting a text description that triggers automated node sequencing and edge creation, mapping cataloged definitions into a structured workflow JSON.

Does automated workflow generation support end-to-end pipeline creation?▼

Automated workflow generation supports end-to-end pipeline creation by assembling complete node definitions, wiring edges according to your description, and exporting the final workflow JSON ready for import.

What is the best way to convert plain English into an ETL pipeline structure?▼

The best way to convert plain English into an ETL pipeline structure is using a catalog-driven approach that translates text intents into structured workflow JSON with valid nodes and connections.

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