notebook-to-pipeline

Your agent turns a messy Jupyter notebook into a tested, reproducible pipeline, and proves the outputs didn't change.

uvx notebook-to-pipeline analyze analysis.ipynb
uvx notebook-to-pipeline capture analysis.ipynb
uvx notebook-to-pipeline verify --pipeline src/analysis/pipeline.py:run --reference analysis.ipynb

Not on PyPI yet. Until the first release, run it straight from GitHub by replacing uvx notebook-to-pipeline with uvx --from git+https://github.com/Abelo9996/notebook-to-pipeline notebook-to-pipeline. setup registers uvx notebook-to-pipeline mcp, so it works once the package is on PyPI.

analyze reads the notebook and finds hidden-state problems. capture runs it top to bottom in a fresh kernel and records what it produces. verify runs the refactored pipeline and compares every output: exact for integers, strings and hashes, with a tolerance for floats, and column by column for DataFrames. The short command is nb2p.

A real run

The "Importance of Feature Scaling" notebook from the scikit-learn 1.9.1 example gallery, captured with nb2p capture plot_scaling_importance.ipynb --out evidence/reference --repeat 2 (output trimmed in the artifact list only):

Python 3.12.13: ~/Downloads/notebook-to-pipeline/examples/.venv/bin/python (virtualenv found at ~/Downloads/notebook-to-pipeline/examples/.venv)
Top-to-bottom run: OK, 7/7 code cells in 7.111 s
Captured 26 artifacts:
  X                            dataframe  a676a863527a
  y                            series     55c53e167556
  ...
  y_proba                      ndarray    260abb10bbbb
  y_proba_scaled               ndarray    71b105b5e066
[warning] shared_object: After the run, `pca`, `unscaled_clf[0]`, `scaled_clf[1]` are one and the same sklearn.decomposition._pca.PCA object. Fitting or changing it through one name changed it for all of them.
[info] identical_artifacts: `pca`, `scaled_pca` have identical content after the run. If the notebook treats them as different results, check for shared objects or a step that was meant to differ.
Determinism check (2 runs): every artifact reproduced
Reference: evidence/reference

The notebook builds its "unscaled" and "standardized" pipelines around the same PCA object, so fitting the second one refits the PCA inside the first. A hand-written pipeline that keeps this behavior verifies as EQUIVALENT on all 26 artifacts. Giving the unscaled pipeline its own PCA is a deliberate change, and verify shows exactly what it touches (3 of the 26 rows shown):

pca                      estimator  FAIL differs    first difference at pca['fitted']['components_'][0,0]: reference 0.13443022714615663, candidate 0.001763429172014044, 46 differences in total
y_pred                   ndarray    FAIL differs    first difference at y_pred[2]: reference 0, candidate 1, 34 differences in total
y_pred_scaled            ndarray    PASS identical  hash match
Verdict: DIFFERS (4 of 26 compared outputs differ)

The unscaled test accuracy the example prints, 35.19%, becomes 74.07% with its own PCA; the standardized pipeline stays at 96.30%. The mechanical first draft from nb2p scaffold also verifies as EQUIVALENT on this notebook (26 of 26) and on the pandas-cookbook one (6 of 6). All of this, plus a notebook broken by hidden state, is in examples/ with the full reports.

How it works

• analyze parses each cell with Python's ast after IPython's own input transformer (so %magics and !shell lines are understood), and builds a def/use graph across cells, including in-place mutation (df.dropna(inplace=True), model.fit(...), x.append(...), item and attribute assignment) and mutation through functions defined in the notebook. Findings: use before definition, names that only a deleted cell defined, out-of-order and hidden executions from the saved execution counts, saved outputs computed from a different definition than a clean run would use, cross-cell mutation, estimators shared between scikit-learn pipelines, unseeded randomness, network and shell access. It proposes a split into load, clean, features, train, evaluate and report with each stage's inputs and outputs.
• capture starts a new Jupyter kernel (nbclient + ipykernel, over a Unix socket on macOS and Linux) on your project's interpreter, runs every cell in order and stops at the first error. It then saves the chosen variables with a content hash and a summary (shape, dtypes, column stats, fitted attributes), records files the notebook wrote, compares the saved text outputs with the fresh run, reports objects reachable under several names, and with --repeat N reruns to find outputs that change between runs.
• verify runs the pipeline in the same interpreter (file.py:func or module:func returning a dict, or a script whose globals hold the results), saves the same artifacts and compares them in that interpreter, so pandas, numpy and scikit-learn objects load with the versions that made them. Fitted estimators are compared by parameters and fitted attributes. Every failure shows the first differences with their path, for example temperature.index[0] or pca['fitted']['components_'][0,0].
• scaffold writes src/<package>/ with one module per proposed stage (the notebook code pasted into functions as a first draft), pipeline.py:run(), tests/test_equivalence.py, a Makefile, a pyproject.toml pinned to the captured versions and a GitHub Actions workflow.
• report writes report.md and report.json: notebook hash, interpreter and package versions, the top-to-bottom result, hidden-state findings, the per-artifact table, the verdict and the limits.

The interpreter is chosen in this order: --python, $NB2P_PYTHON, a .venv next to the notebook or in a parent directory, then the one running nb2p. With no project venv, uvx --with pandas --with scikit-learn notebook-to-pipeline capture ... works too.

Exit codes: capture 0 when the notebook runs, 3 when it fails. verify 0 equivalent, 1 differs, 3 pipeline failed, 4 reference invalid.

Setup for agents

uvx notebook-to-pipeline setup          # shows what it would change
uvx notebook-to-pipeline setup --yes    # applies it

It detects Claude Code, Codex and Cursor and registers the MCP server (uvx notebook-to-pipeline mcp) with each: claude mcp add --scope user, a [mcp_servers.notebook-to-pipeline] table in ~/.codex/config.toml, an entry in ~/.cursor/mcp.json. It copies the agent skill to ~/.claude/skills/ and ~/.codex/skills/. Files are backed up before they are edited and a second run changes nothing. --project DIR writes a project .mcp.json instead.

MCP tools: analyze_notebook, capture_reference, verify_pipeline, scaffold_pipeline, write_report. The skill (skills/notebook-to-pipeline/SKILL.md) tells the agent to capture before changing anything, verify after every step, and never change logic to make outputs match without saying so.

What it can't do

• It compares variables and files, not plots. Figure files are listed but not compared, and a value that was only displayed, never stored in a variable, is not compared.
• Static analysis does not follow exec, eval, %run, imports of local modules or aliases (b = a; b.append(1)). Shared objects of that kind are caught at runtime only if both names are captured.
• Equivalence is shown for this data in this environment. A different input file or library version can still change results.
• Outputs that change from run to run (unseeded randomness, timings) cannot be verified. --repeat finds them; it does not fix them.
• The stage proposal is a heuristic starting point and scaffold produces a mechanical draft. The refactor itself is the agent's job.
• Plain Python kernels only. No R or Julia notebooks, no Spark or remote kernels.

Privacy and safety

Everything runs on your machine. The tool makes no network calls and calls no LLM; the agent you already use does the refactoring. capture and verify execute the notebook and the pipeline with your user's permissions, exactly as running them yourself would. References are stored as pickles, so only verify against capture directories you created (see SECURITY.md). Evidence files replace your home directory with ~.

License

MIT. The example notebooks keep their own licenses (CC BY-SA 4.0 and BSD 3-Clause), noted in each example's NOTICE.txt.