Default filters

These rules run before anything else, on every conversion. Your project's .gitignore is applied next, and a .repo2nbignore can override any of it — use !pattern to force-include something listed here. Every rule below comes from the same source of truth the tool itself uses.

Datasets

  • *.csvdataset file

Dependency folders

  • node_modules/dependency folder
  • site-packages/dependency folder
  • vendor/dependency folder

Other

  • .venv/virtualenv folder
  • venv/virtualenv folder

Caches and build artifacts

  • *.egg-info/package build metadata
  • __pycache__/cache
  • .pytest_cache/cache
  • .mypy_cache/cache
  • .ruff_cache/cache
  • .hypothesis/cache
  • .parcel-cache/cache
  • .next/build output
  • dist/build output
  • build/build output
  • .turbo/build output

Model checkpoints

  • checkpoints/model checkpoint directory

Tooling internals

  • .git/version control internals
  • .github/not needed at runtime
  • .vscode/editor config
  • .idea/editor config

Environment and secrets

  • .env*environment / secrets

Precedence

  1. repo2nb defaults (this page)
  2. your project's .gitignore
  3. your project's .repo2nbignore
  4. checkbox toggles in the tree preview

each layer overrides the one above it

Questions

No. Your files exist only in the request body and process memory for the duration of a single conversion call. Nothing is written to disk, nothing is kept, and there is no account system because there is nothing to attach your files to.

Dependency folders (node_modules, .venv), caches, build artifacts, model checkpoints, datasets and other large binaries: plus whatever your project's .gitignore excludes. Every rule is listed on the Filters page, and each excluded file shows its reason right in the tree.

That's the point. You pick a folder from your own machine: repo2nb never sees a repo URL and has no way to reach into anything you didn't explicitly hand it.

Direct conversion is capped at 4 MB of filtered content so the tool stays fast for everyone. Datasets and model weights should be attached on Kaggle/Colab directly anyway: exclude them and let the notebook fetch or reference them.

Yes: pick the target before generating. The notebook's first cells contain step-by-step setup instructions for that platform, including how to enable GPU acceleration.