Agent skill

Kedro Pipeline Guide

by wentorai in wentorai/research-plugins

Build reproducible data science pipelines with Kedro for research projects

MITAuto-check passed

Install Kedro Pipeline Guide

skills CLI

$ npx skills add wentorai/research-plugins --skill kedro-pipeline-guide -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI

$ gh skill install wentorai/research-plugins kedro-pipeline-guide --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy

$ git clone --depth 1 https://github.com/wentorai/research-plugins.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/research/automation/kedro-pipeline-guide .claude/skills/kedro-pipeline-guide && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Paste into Claude Code

Install the "kedro-pipeline-guide" agent skill from https://github.com/wentorai/research-plugins/tree/main/skills/research/automation/kedro-pipeline-guide into .claude/skills/kedro-pipeline-guide/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "kedro-pipeline-guide", then confirm the skill loads.

Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

$skill-installer

$skill-installer install https://github.com/wentorai/research-plugins/tree/main/skills/research/automation/kedro-pipeline-guide

Type this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.

skills CLI

$ npx skills add wentorai/research-plugins --skill kedro-pipeline-guide -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install wentorai/research-plugins kedro-pipeline-guide --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/wentorai/research-plugins.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/research/automation/kedro-pipeline-guide .agents/skills/kedro-pipeline-guide && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "kedro-pipeline-guide" agent skill from https://github.com/wentorai/research-plugins/tree/main/skills/research/automation/kedro-pipeline-guide into .agents/skills/kedro-pipeline-guide/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "kedro-pipeline-guide", then confirm the skill loads.

Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add wentorai/research-plugins --skill kedro-pipeline-guide -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install wentorai/research-plugins kedro-pipeline-guide --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/wentorai/research-plugins.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/research/automation/kedro-pipeline-guide .cursor/skills/kedro-pipeline-guide && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "kedro-pipeline-guide" agent skill from https://github.com/wentorai/research-plugins/tree/main/skills/research/automation/kedro-pipeline-guide into .cursor/skills/kedro-pipeline-guide/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "kedro-pipeline-guide", then confirm the skill loads.

Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

gemini skills install

$ gemini skills install https://github.com/wentorai/research-plugins.git --path skills/research/automation/kedro-pipeline-guide

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add wentorai/research-plugins --skill kedro-pipeline-guide -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install wentorai/research-plugins kedro-pipeline-guide --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/wentorai/research-plugins.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/research/automation/kedro-pipeline-guide .gemini/skills/kedro-pipeline-guide && rm -rf skills-src

Use ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.

Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/

Paste into Gemini CLI

Install the "kedro-pipeline-guide" agent skill from https://github.com/wentorai/research-plugins/tree/main/skills/research/automation/kedro-pipeline-guide into .gemini/skills/kedro-pipeline-guide/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "kedro-pipeline-guide", then confirm the skill loads.

Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

GitHub CLI

$ gh skill install wentorai/research-plugins kedro-pipeline-guide

Installs for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).

skills CLI

$ npx skills add wentorai/research-plugins --skill kedro-pipeline-guide -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/wentorai/research-plugins.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/research/automation/kedro-pipeline-guide .github/skills/kedro-pipeline-guide && rm -rf skills-src

Use ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.

GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/

Paste into GitHub Copilot

Install the "kedro-pipeline-guide" agent skill from https://github.com/wentorai/research-plugins/tree/main/skills/research/automation/kedro-pipeline-guide into .github/skills/kedro-pipeline-guide/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "kedro-pipeline-guide", then confirm the skill loads.

GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add wentorai/research-plugins --skill kedro-pipeline-guide -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install wentorai/research-plugins kedro-pipeline-guide --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/wentorai/research-plugins.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/research/automation/kedro-pipeline-guide .opencode/skills/kedro-pipeline-guide && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "kedro-pipeline-guide" agent skill from https://github.com/wentorai/research-plugins/tree/main/skills/research/automation/kedro-pipeline-guide into .opencode/skills/kedro-pipeline-guide/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "kedro-pipeline-guide", then confirm the skill loads.

OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

Facts

Skill name: kedro-pipeline-guide
GitHub stars: 298
Used in: 1 other repo
Token cost: ~1.8k tokens
SKILL.md length: 436 words
Files: 1
Skills in repo: 405
Repo updated: Jun 19, 2026
First seen: Oct 7, 2026
Licence: MIT

At a glance

Build reproducible data science pipelines with Kedro for research projects

SKILL.md covers Overview, Installation and Setup, Core Concepts and Running and Visualizing…, plus 2 more sections
Calls pip

What it does

Kedro Pipeline Guide is an agent skill from wentorai/research-plugins. Build reproducible data science pipelines with Kedro for research projects

Its SKILL.md is about 1.8k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

The repository describes itself as: 350+ academic research skills, MCP configs, and plugins for Research-Claw and AI agents. The licence is MIT.

Example prompts

“/kedro-pipeline-guide”

Requirements

Python 3

What it can do on your machine

Read from SKILL.md and the folder at commit bf44b3c. It shows what the files ask for, not the result of running them.

Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Shell commands in SKILL.md call:
- pip
From the folder's file list and the shell code blocks in SKILL.md.
Network
Links to these hosts (documentation or services it may open):
- github.com
- docs.kedro.org
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Kedro Pipeline Guide loads about 1.8k tokens when it runs. Until then it costs about 24 tokens; SKILL.md has 436 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~24
When it runs · the whole SKILL.md, loaded when a task matches: ~1.8k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from wentorai/research-plugins at commit bf44b3c, republished under its MIT licence (© wentorai). 436 words, ~1,759 tokens.

Download SKILL.mdSave it as .claude/skills/kedro-pipeline-guide/SKILL.md (or your agent's skills folder).

name: kedro-pipeline-guide
description: Build reproducible data science pipelines with Kedro for research projects

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:

bash

pip install kedro

Create a new project using the Kedro starter:

bash

kedro new --name my-research-project --tools lint,test,docs
cd my-research-project

This generates a standardized project structure:

my-research-project/
  conf/
    base/
      catalog.yml        # Data source definitions
      parameters.yml     # Experiment parameters
    local/               # Local overrides (gitignored)
  src/
    my_research_project/
      pipelines/
        data_processing/
          nodes.py       # Pure Python functions
          pipeline.py    # Pipeline definition
        modeling/
          nodes.py
          pipeline.py
      pipeline_registry.py
  data/                  # Local data directory
  notebooks/             # Jupyter notebooks
  tests/                 # Unit tests

Install project dependencies:

bash

pip install -e ".[dev]"

Core Concepts

Nodes: The fundamental units of computation in Kedro. Each node is a pure Python function with explicitly declared inputs and outputs:

python

# src/my_research_project/pipelines/data_processing/nodes.py

import pandas as pd
from sklearn.preprocessing import StandardScaler

def clean_raw_data(raw_data: pd.DataFrame) -> pd.DataFrame:
    """Remove missing values and outliers from raw experimental data."""
    cleaned = raw_data.dropna(subset=["measurement", "condition"])
    q1 = cleaned["measurement"].quantile(0.01)
    q99 = cleaned["measurement"].quantile(0.99)
    return cleaned[cleaned["measurement"].between(q1, q99)]

def normalize_features(
    cleaned_data: pd.DataFrame, parameters: dict
) -> pd.DataFrame:
    """Standardize feature columns specified in parameters."""
    feature_cols = parameters["feature_columns"]
    scaler = StandardScaler()
    result = cleaned_data.copy()
    result[feature_cols] = scaler.fit_transform(cleaned_data[feature_cols])
    return result

Pipelines: Chains of nodes connected through named datasets:

python

# src/my_research_project/pipelines/data_processing/pipeline.py

from kedro.pipeline import Pipeline, node, pipeline
from .nodes import clean_raw_data, normalize_features

def create_pipeline(**kwargs) -> Pipeline:
    return pipeline([
        node(
            func=clean_raw_data,
            inputs="raw_experiment_data",
            outputs="cleaned_data",
            name="clean_data_node",
        ),
        node(
            func=normalize_features,
            inputs=["cleaned_data", "params:preprocessing"],
            outputs="normalized_data",
            name="normalize_node",
        ),
    ])

Data Catalog: A declarative registry that maps logical dataset names to physical storage:

yaml

# conf/base/catalog.yml
raw_experiment_data:
  type: pandas.CSVDataset
  filepath: data/01_raw/experiment_results.csv

cleaned_data:
  type: pandas.ParquetDataset
  filepath: data/02_intermediate/cleaned.parquet

normalized_data:
  type: pandas.ParquetDataset
  filepath: data/03_primary/normalized.parquet
  versioned: true

The versioned: true flag automatically creates timestamped versions of outputs, enabling exact reproduction of prior runs.

Parameters: Experiment configuration separated from code:

yaml

# conf/base/parameters.yml
preprocessing:
  feature_columns:
    - temperature
    - pressure
    - concentration
  outlier_method: iqr

modeling:
  algorithm: random_forest
  n_estimators: 500
  max_depth: 10
  test_size: 0.2
  random_seed: 42

Show full SKILL.md (186 more words)Show less

Running and Visualizing Pipelines

Execute the full pipeline:

bash

kedro run

Run a specific pipeline or node:

bash

kedro run --pipeline data_processing
kedro run --nodes clean_data_node

Visualize the pipeline dependency graph:

bash

pip install kedro-viz
kedro viz run

This launches an interactive web visualization showing the complete data flow, making it easy to understand and communicate your analytical pipeline to collaborators and reviewers.

Research Workflow Integration

Experiment Reproducibility: Every Kedro run uses explicit parameters and versioned data. Store parameter files in Git alongside code to create a complete record of every experiment configuration.

Reviewer Response: When peer reviewers request additional analyses or modified parameters, change parameters.yml and re-run. The pipeline automatically reprocesses only affected downstream nodes.

Team Collaboration: Multiple researchers can work on different pipeline modules simultaneously. The explicit input/output contracts between nodes prevent integration conflicts.

Scaling Computation: Kedro pipelines can be deployed to distributed computing platforms without code changes using runners:

bash

# Run with parallel execution
kedro run --runner=ParallelRunner

# Deploy to Airflow, Prefect, or other orchestrators
pip install kedro-airflow
kedro airflow create

Integration with Jupyter: Use Kedro notebooks for exploration while maintaining the pipeline for production runs:

bash

kedro jupyter notebook

The Kedro Jupyter integration automatically loads the project catalog and parameters, bridging the gap between interactive exploration and pipeline execution.

References

Kedro repository: https://github.com/kedro-org/kedro
Kedro documentation: https://docs.kedro.org/
Kedro-Viz for pipeline visualization: https://github.com/kedro-org/kedro-viz
QuantumBlack blog on reproducible data science

Files

Just SKILL.md in skills/research/automation/kedro-pipeline-guide of wentorai/research-plugins.

Open the folder on GitHubat commit bf44b3c

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in wentorai/research-plugins, which our catalogue first saw on October 7, 2026.

brycewang-stanford/Auto-Empirical-Research-Skillsskills/43-wentorai-research-plugins/skills/research/automation/kedro-pipeline-guide

Compare with similar skills

Kedro Pipeline Guide next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Kedro Pipeline Guide compared with similar skills
Skill	Stars	Used in	Tokens	Auto-check	Licence	Repo updated
Kedro Pipeline Guide this skillwentorai/research-plugins	298	1 repos	~1.8k	Automated safety check: Pass	MIT	3 mo ago
Reproducible Pipelinesbrycewang-stanford/Auto-Empirical-Research-Skills	4.6k	—	~3.3k	Automated safety check: Pass	Custom licence	5 days ago
Opensource Pipelineaffaan-m/ECC	277k	1 repos	~2.2k	Automated safety check: Notes	MIT	today
Orch Pipelineaffaan-m/ECC	276k	1 repos	~1.6k	Automated safety check: Pass	MIT	yesterday
AI Pipeline Orchestrationsickn33/agentic-awesome-skills	47k	1 repos	~2.5k	Automated safety check: Pass	MIT	2 days ago
Data PipelineRightNow-AI/openfang	18k	—	~847	Automated safety check: Pass	Apache-2.0	3 mo ago

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Questions about Kedro Pipeline Guide

What does Kedro Pipeline Guide do?

Build reproducible data science pipelines with Kedro for research projects. Kedro Pipeline Guide is an agent skill from wentorai/research-plugins.

How do I install Kedro Pipeline Guide in Claude Code?

Run `npx skills add wentorai/research-plugins --skill kedro-pipeline-guide -a claude-code`. Or copy the skill folder (skills/research/automation/kedro-pipeline-guide in wentorai/research-plugins) into .claude/skills/kedro-pipeline-guide in your project. Claude Code loads it when a task matches its description.

How do I install Kedro Pipeline Guide in Codex?

Run `npx skills add wentorai/research-plugins --skill kedro-pipeline-guide -a codex`. Or copy the skill folder (skills/research/automation/kedro-pipeline-guide in wentorai/research-plugins) into .agents/skills/kedro-pipeline-guide in your project. Codex loads it when a task matches its description.

Can I use Kedro Pipeline Guide in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add wentorai/research-plugins --skill kedro-pipeline-guide -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/kedro-pipeline-guide, .gemini/skills/kedro-pipeline-guide, .github/skills/kedro-pipeline-guide and .opencode/skills/kedro-pipeline-guide in your project.

What does Kedro Pipeline Guide need to run?

Going by SKILL.md and its folder, Kedro Pipeline Guide needs the command-line tools its instructions call (pip). Our summary lists: Python 3.

Does Kedro Pipeline Guide access the network?

SKILL.md names 2 domains. As links in the text: github.com and docs.kedro.org. This is read from the text; nothing was executed.

Is Kedro Pipeline Guide safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Kedro Pipeline Guide use?

Kedro Pipeline Guide is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Kedro Pipeline Guide use?

About 1.8k tokens (SKILL.md is roughly 7k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Kedro Pipeline Guide?

Skills that share tags, products or a category with Kedro Pipeline Guide: Reproducible Pipelines (brycewang-stanford/Auto-Empirical-Research-Skills, 4.6k stars), Opensource Pipeline (affaan-m/ECC, 277k stars), Orch Pipeline (affaan-m/ECC, 276k stars) and AI Pipeline Orchestration (sickn33/agentic-awesome-skills, 47k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Kedro Pipeline Guide?

wentorai (a GitHub user) maintains it in wentorai/research-plugins, which has 298 GitHub stars. The repository holds 405 skills in this directory. The repository was last updated on June 19, 2026.

Source: wentorai/research-plugins on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Kedro Pipeline Guide

Install Kedro Pipeline Guide

Facts

At a glance

What it does

Example prompts

Requirements

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

Kedro Pipeline Guide

Overview

Installation and Setup

Core Concepts

Running and Visualizing Pipelines

Research Workflow Integration

References

Files

Used in 1 other repository

Compare with similar skills

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Opensource Pipeline

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AI Pipeline Orchestration

Data Pipeline

Windmill Data Pipeline Author

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Questions about Kedro Pipeline Guide