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Data Engineering Data Pipeline

Listed
sickn33·vlatest·data·MIT

Helps with data, pipeline automation as part of automating DevOps pipelines and CI/CD workflows workflows.

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 sickn33/agentic-awesome-skills --skill data-engineering-data-pipeline -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

sickn33

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 failed 1 of 6 checks. A failure is not proof of bad intent; it means something in the code needs a human to look at it before you run it.

  • Declares what it does: failed

    No SKILL.md was found in the skill's directory.

    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 sickn33/antigravity-awesome-skills at 5a4cdd9df8.

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

Commit
5a4cdd9df8
Manifest
skills/data-engineering-data-pipeline/SKILL.md
Scanned
0 files
Copies in repo
3 — all of them scanned
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 construct, orchestrate, and manage end-to-end data pipelines for moving, transforming, and storing information across diverse systems. It automates the workflow of extracting data from sources, applying necessary logic or cleaning rules, and loading results into target destinations like databases or data lakes.

When to use it

  • Automating the nightly refresh of sales reports by pulling raw transaction logs from a CRM and aggregating them into a central analytics warehouse.
  • Building an ETL process that scrapes competitor pricing data from multiple websites, cleans the values, and updates a local inventory database in real-time.
  • Migrating legacy customer records from a flat-file system into a modern cloud data lake while applying schema validation rules during the transfer.

Key capabilities

  • Orchestration: Coordinates complex sequences of data movement tasks across different environments.
  • Transformation: Applies logic to clean, filter, and reshape raw data before storage.
  • Integration: Connects disparate sources (APIs, databases, files) with various sinks for unified data access.
  • Automation: Runs scheduled or event-triggered workflows without manual intervention.

Example prompts

  • "Create a pipeline that extracts daily user activity logs from our server API and loads them into a PostgreSQL database."
  • "Build an automated workflow to scrape product descriptions from three e-commerce sites, clean the text, and save the results to a CSV file."
  • "Design a data pipeline that transforms raw JSON sensor readings into time-series data and stores it in a cloud data lake for analysis."

Tips & gotchas

Ensure you define clear error handling strategies within your pipeline logic, as network failures or schema mismatches can halt entire workflows. Always validate the structure of incoming data before transformation to prevent downstream processing errors.