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RatnaAtika
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notebook-authoring

Scaffold, run, lint, and ship reproducible Jupyter notebooks for analysis and demos.

Updated May 29, 2026
Search Tags:#data analysis#jupyter#reproducibility#ci#notebook#papermill#nbstripout
One-click install
npx skills add https://github.com/RatnaAtika/Indonesian-ASR-11-Class-Dataset --skill notebook-authoring-ratnaatika
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: notebook-authoring
Source: https://github.com/RatnaAtika/Indonesian-ASR-11-Class-Dataset/tree/main/.agents/skills/notebook-authoring
Command: npx skills add https://github.com/RatnaAtika/Indonesian-ASR-11-Class-Dataset --skill notebook-authoring-ratnaatika

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Jupyter notebooks often become unreproducible, unreviewable, and unsafe to commit—hidden state, unpinned dependencies, leaked secrets, and messy diffs undermine analysis and research work. This Skill provides a disciplined workflow for authoring, running, reviewing, and shipping notebooks that stay reproducible. ## Core Features & Use Cases - Reproducible scaffolding: Set up projects with uv or poetry, pinned dependencies, locked environments, seeds, and version-printing first cells. - Headless CI execution: Run notebooks with papermill, export to HTML/PDF via nbconvert, and keep notebooks tested on every pull request. - Review and hygiene tooling: Use nbstripout, nbqa ruff, nbqa mypy, and nbdime for clean diffs, linting, and typechecking. - Use Case: A data scientist needs a quarterly report notebook that runs nightly in CI, exports HTML for stakeholders, and passes code review with readable diffs—this Skill defines the exact layout, tooling, and verification checklist. ## Quick Start Ask the assistant to scaffold a reproducible Jupyter notebook project with pinned dependencies, papermill CI execution, and output-stripping pre-commit hooks.

Frequently Asked Questions about notebook-authoring

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I run a Jupyter notebook headlessly in CI?▼

Use papermill to execute the notebook with parameterized inputs, then nbconvert to export the result to HTML. This lets CI run the notebook on every pull request and produce a shareable report artifact.

How do I make Jupyter notebooks reproducible?▼

Pin dependencies with a committed lockfile, set random seeds at the top, use immutable input snapshots, and print library versions in the first cell. Always restart the kernel and run all cells top-to-bottom before committing.

What tools lint and typecheck code inside notebook cells?▼

nbqa runs standard Python tools against notebook cells, including ruff for linting and mypy for typechecking. Combine these with nbstripout as pre-commit hooks to keep committed notebooks clean.

How do I review notebook changes in pull requests?▼

Use nbdime to generate human-readable diffs of notebook JSON, and review the rendered notebook on GitHub. Stripping outputs with nbstripout beforehand keeps diffs focused on code changes.

When should I not use a Jupyter notebook?▼

Avoid notebooks for long-lived production logic or anything containing secrets in cells. Extract stable logic into a tested Python module and keep the notebook as a thin demo of that module.

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