Design and operate scalable data pipelines and architectures using best-fit patterns, tools, and modeling methodologies without external dependencies.
Data sourced from ClawHub. View on ClawSkills
Select your agent
Option 1: Install via CLI (recommended)
Recommended (no pre-install needed)
npx clawhub@latest --dir ~/.claude/skills install afrexai-data-engineeringOr via clawhub CLI (if already installed)
clawhub --dir ~/.claude/skills install afrexai-data-engineeringβ οΈ Requires Node.js 18+. No Node? Use Option 2 below to download the ZIP instead. Install Node.js β
Option 2: Manual install (no Node required)
Download the ZIP, extract it, and place the folder at the path below. Restart your agent to activate.
Install path
~/.claude/skills/afrexai-data-engineering/π‘Extract and place the folder at the path above, then restart your agent.
Category
π»Developer & DevOpsPlatforms
What Data Engineering can do for your AI workflow
Design and operate scalable directly from your Claude conversation
Works across Claude, Cursor, OpenClaw β install once, use everywhere
One-command installation β no complex setup required
Combine with other skills to build powerful multi-step AI workflows
Try these prompts with your AI agent after installing Data Engineering
Help me get started with Data Engineering
Explains what Data Engineering does, walks through the setup, and runs a quick demo based on your current project
Use Data Engineering to design and operate scalable data pipelines and architectures using ...
Invokes Data Engineering with the right parameters and returns the result directly in the conversation
What can I do with Data Engineering in my developer & devops workflow?
Lists the top use cases for Data Engineering, with example commands for each scenario
Guides & tutorials for AI skills
Data Engineering extends your AI assistant with the ability to design and operate scalable data pipelines and architectures using best-fit patterns, tools, and modeling methodologies without external dependencies. Rather than leaving your conversation to handle this manually, you can ask your Claude agent directly β and it will take care of the task end-to-end, using Data Engineering as its underlying capability.
Data Engineering works across Claude, Cursor, OpenClaw through the Model Context Protocol (MCP) β an open standard that lets AI clients share tools and skills without lock-in. Because MCP is platform-agnostic by design, you install Data Engineering once and it becomes available across all your AI clients. Whether you're working in Claude for focused sessions or Cursor for integrated workflows, the skill behaves consistently.
To install Data Engineering, copy the skill folder to `~/.claude/skills/afrexai-data-engineering/` for use across all your projects, or `.claude/skills/afrexai-data-engineering/` for a single project. Restart Claude and the skill is immediately active β invoke it with `/afrexai-data-engineering` or just describe your goal and the AI will pick it up automatically.
Data Engineering has 675 installs and is part of the growing Developer & DevOps skill ecosystem on DiscoverAISkills. The source code is open on GitHub β you can inspect it, contribute fixes, or fork it to adapt the skill for your specific setup. Like all skills on DiscoverAISkills, it is free to install and use. The broader AI skills ecosystem continues to expand as developers contribute new capabilities across categories like developer tools, data analysis, writing, automation, and more.
Place the skill folder at ~/.claude/skills/afrexai-data-engineering/ for personal use (all projects), or .claude/skills/afrexai-data-engineering/ for project-specific use. Restart your AI client, then invoke with /afrexai-data-engineering or let the AI discover it automatically.
Data Engineering supports Claude, Cursor, OpenClaw. It integrates seamlessly with these AI platforms to extend their capabilities.
Data Engineering is free to install. Check the repository for licensing information.
Design and operate scalable data pipelines and architectures using best-fit patterns, tools, and modeling methodologies without external dependencies.
Links
Automate my developer & devops tasks using Data Engineering
Identifies repetitive steps in your workflow and sets up Data Engineering to handle them automatically
MCP vs traditional plugins: what's the difference?
Data Engineering is categorized under Developer & DevOps. These skills help AI agents perform specialized tasks in this domain.