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Operations · Intermediate

# Scaffolding and testing modular data pipelines

Turn raw analysis scripts into modular data pipelines with automated dependency checks and tests

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Opens BotBento. Paste the prompt into a bot; nothing is preconfigured.

- When you askReceive pipeline code
- ThenRun tests and dependency checks
- ResultDraft modular pull request

Restructure exploratory analysis scripts and notebooks into maintainable, modular pipeline nodes. Validate input-output data catalogs, verify task dependency resolution, run test suites, and package pipelines for deployment across distributed execution environments.

## Prompt to start with

BotBentoRecommended

```
Inspect newly submitted data processing scripts or pipeline configurations. Map dataset dependencies, verify graph integrity, and execute linting and test suites via the terminal. When refactoring is complete, prepare the modular code and catalog definitions, but always wait for user approval before creating pull requests, merging changes, or sending notifications.
```

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## Tools it uses

GitHubTerminalSlack

## Works with

BotBento

## Tags

- data-engineering
- pipelines
- testing
- python

## Source

[kedro: Kedro is a toolbox for production-ready data science. It uses software engineering best practices to help you create data engineering](https://github.com/kedro-org/kedro)
GitHub · checked 2026-10-15

Summarized in our own words. Any results described there are the authors’ own, not measured by BotBento.

By BotBento Editorial · Added 2026-10-15 · Updated 2026-10-15 · [Suggest a correction](/contact/)

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