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Data Engineering

Checked
eyadsibai·vlatest·data

Automates data pipelines, cleanses datasets, and transforms raw information into usable formats for analysis.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add eyadsibai/ltk --skill data-engineering -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

eyadsibai

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No undeclared network calls: passed

    Makes no network calls.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned eyadsibai/ltk at f8e85697b9.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
f8e85697b9
Manifest
plugins/ltk-data/skills/data-engineering/SKILL.md
Scanned
1 file, 248 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

What it does

This skill enables AI agents to automate data engineering tasks, including building data pipelines, designing data warehouses, and implementing ETL/ELT processes. It supports various components of a modern data stack, from ingestion and storage to transformation and serving. The skill provides examples of batch processing using Airflow and streaming processing with Kafka, as well as dbt modeling patterns for structuring data transformations.

When to use it

  • Building automated data pipelines.
  • Designing the architecture for data warehouses.
  • Implementing ETL/ELT processes to move and transform data.
  • Setting up data lakes for storing large volumes of raw data.
  • Optimizing existing data infrastructure for efficiency.

Key capabilities

  • Supports building batch processing pipelines using Airflow.
  • Provides examples of streaming data processing with Kafka.
  • Demonstrates dbt modeling patterns for organizing and transforming data (staging, intermediate, marts).
  • Covers components including ingestion, storage, transformation, orchestration, and serving layers within a modern data stack.
  • Includes example code snippets for common data engineering tasks.

Example prompts

  • "Create an Airflow DAG to extract data from [source], transform it using [transformation logic], and load it into [destination]."
  • "Show me the recommended dbt model structure for a project with orders and customers data."
  • "Give me an example of how to consume events from Kafka in Python."

Tips & gotchas

  • This skill focuses on providing examples and guidance for data engineering practices; it doesn't directly execute these tasks. You’ll need additional tools (like Airflow, dbt, Kafka) installed and configured separately.
  • The provided code snippets are illustrative and may require adaptation to your specific environment.