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Data Engineer

Checked
rmyndharis·vlatest·data·MIT

Automates data pipeline creation, transformation, and validation tasks using Python and cloud services for efficient data management.

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 rmyndharis/antigravity-skills --skill data-engineer -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

rmyndharis

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 rmyndharis/antigravity-skills at 3eff4af253.

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

Commit
3eff4af253
Manifest
skills/data-engineer/SKILL.md
Scanned
1 file, 225 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

The rmyndharis-data-engineer skill enables AI agents to perform common data engineering tasks. It allows for automated data transformation, cleaning, and loading (ETL) processes. The agent can also generate SQL queries based on natural language instructions related to database manipulation.

When to use it

  • You need to automate a repetitive data cleaning process.
  • You want to extract insights from a relational database without writing complex SQL.
  • You're building an application that requires automated ETL pipelines.
  • You are looking for assistance in generating SQL queries for common tasks like filtering, aggregation and joining tables.

Key capabilities

  • Automated data transformation
  • Data cleaning
  • ETL (Extract, Transform, Load) processes
  • SQL query generation from natural language prompts

Example prompts

  • "Clean the 'customers' dataset by removing duplicates and handling missing values."
  • "Generate a SQL query to find the average order value for each product category."
  • "Create an ETL pipeline to load data from this CSV file into my PostgreSQL database."

Tips & gotchas

The skill’s effectiveness depends on clear instructions and well-defined data schemas. Providing examples of desired transformations can improve accuracy when automating data cleaning or transformation tasks.