Menu

Individual skill

kedro-pipeline-guide

Build reproducible data science pipelines with Kedro for research projects

skills/research/automation/kedro-pipeline-guide/SKILL.md

Read pinned source

Source commit bf44b3cd617fa94c8a1b254c5d1987142ca3d631

What was checked

Matches the observed repository HEAD
Yes
Scanner rules are current
No
Repository scan complete
No
Skill scan complete
No
Observed behavior
Not checked yet
Last checked
Nov 5, 2026

These are observations at the recorded commit, not a guarantee of safety. Review the source before running it.

Install this skill

Choose an agent in your Stack or copy a command below. Commands fetch the pinned commit and copy only this skill directory. Existing destinations are refused. Copying a command does not install anything.

An install command is unavailable: current rules, complete repository and skill scans, an eligible behavior level and a permitted license are required.

Repository context

Repository stars (not skill ratings)
310
Repository last updated
Jun 19, 2026
License
MIT

Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.

Files and excerpt

File metadata and permitted, redacted excerpts come from the recorded scan. Source links are pinned to that commit.

Browse files
Read excerpt

# Kedro Pipeline Guide

## Overview

Kedro is an open-source Python framework for creating reproducible, maintainable, and modular data science pipelines. Developed originally at McKinsey's QuantumBlack labs, Kedro provides an opinionated project structure and a set of conventions that transform ad-hoc analysis scripts into production-quality code that can be tested, versioned, and shared across research teams.

In academic research, reproducibility is both a scientific imperative and a practical challenge. Jupyter notebooks and standalone scripts often become tangled webs of dependencies that are difficult to re-run months later when responding to reviewer comments or extending prior work. Kedro addresses this by separating data processing logic from data access, enforcing explicit pipeline definitions, and providing built-in data versioning and experiment tracking.

With over 11,000 GitHub stars, Kedro has gained adoption across industry and academia. Its design philosophy aligns naturally with the needs of computational research: clear data lineage, parameterized experiments, and the ability to scale from a laptop to a cluster without rewriting code.

## Installation and Setup

Install Kedro via pip: