Data Engineering Data Driven Feature

Name: Data Engineering Data Driven Feature
Author: sickn33

Listed

sickn33·vlatest·data·MIT

Automates feature engineering pipelines from raw data using statistical analysis and machine learning techniques for improved model performance.

Repository

Install on your platform

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

Run this in your project

terminal

$ npx skills add sickn33/agentic-awesome-skills --skill data-engineering-data-driven-feature -a claude-code

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

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Install target

Live repository

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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 28 Sept 2026

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
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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-driven-feature/SKILL.md
Scanned: 0 files
Copies in repo: 3 — all of them scanned

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About this skill

What it does

This skill automates feature engineering pipelines from raw data using statistical analysis and machine learning techniques to improve model performance. It orchestrates a comprehensive data-driven development process, guiding you through phases of data analysis, hypothesis formulation, feature implementation, and post-launch analysis. Specialized agents are used in each phase to ensure features are built based on data insights and validated through experimentation.

When to use it

When working on data-driven feature development tasks or workflows.
When needing guidance, best practices, or checklists for data-driven feature development.
For initial exploratory data analysis of user behavior data.
To identify patterns, opportunities, and user segments based on existing data.

Key capabilities

Data Analysis & Hypothesis Formation: Uses a "data-scientist" subagent to perform exploratory data analysis (EDA).
Modern Analytics Tools Integration: Can leverage tools like Amplitude, Mixpanel, and Segment for understanding user journeys.
Workflow Orchestration: Guides users through a structured process from initial data analysis to feature implementation and post-launch analysis.
A/B Testing Support: Facilitates the validation of features through controlled experiments.

Example prompts

"Perform exploratory data analysis for feature: [feature name]. Analyze existing user behavior data, identify patterns and opportunities."
"Segment users by behavior and calculate baseline metrics for this new feature."
"What are some potential areas for improvement based on current user journeys?"

Tips & gotchas

Clearly define goals, constraints, and required inputs before using the skill.
The resources/implementation-playbook.md file contains detailed examples and further instructions.
This skill is specifically designed for data-driven feature development; it's not suitable for unrelated tasks or those requiring tools outside its scope.