---
title: "Jupyter notebooks committed with outputs | Metacenta"
description: "A notebook committed with its outputs is the file most likely to hold real customer rows in git. How we spot them from file size alone, without opening them."
url: "https://metacenta.com/rules/ai-notebook-outputs-committed"
updated: "2026-10-14"
tokens: 526
---

AI lifecycle · rule ai-notebook-outputs-committed

# Jupyter notebooks committed with outputs

A Metacenta review checks this under the rule **Notebooks are committed without their outputs**. Everything below applies whether or not you ever commission one.

## What this rule checks

This rule flags Jupyter notebooks in the repository of 512 KB or more. A code-only notebook is tens of kilobytes, so at that size its outputs are committed with it. We never open the file.

## Why it matters

Notebook outputs are whatever the last cell printed, so this is the file most likely to hold real customer rows. They stay in git history, readable by everyone with access, and every diff becomes unreviewable.

## How to fix it

**Strip outputs from committed notebooks.** Strip outputs on commit with `nbstripout` as a git filter, or `jupyter nbconvert --clear-output`. Removing them from the working copy does not remove them from history, so clean the history too if they held personal data.

## When it is fine to leave

A notebook published as a rendered example, such as a tutorial whose charts are the point, may keep its outputs. Make sure they were generated from synthetic or public data.

## What we need to check it

Read-only repository access for the file tree: paths and sizes. If the tree comes back truncated, the rule declines, because a missing notebook cannot be told from an unlisted one.

[Ask about a review](https://metacenta.com/enquire?src=rule-ai-notebook-outputs-committed&via=md)

## Related pages

- [Unpinned AI and ML library versions](https://metacenta.com/rules/ai-dependency-unpinned)
- [Model training with no experiment tracking](https://metacenta.com/rules/ai-training-without-tracking)
- [LLM apps with no evaluation suite](https://metacenta.com/rules/ai-llm-without-evaluation)
- [LLM calls with no tracing or cost tracking](https://metacenta.com/rules/ai-llm-without-observability)
- [A sample review, as a client reads it](https://metacenta.com/sample)
- [All published rules](https://metacenta.com/rules)
- [How a review is assessed](https://metacenta.com/standard)