jellycell

What It Is

Reproducible-analysis notebook tool. Notebooks are plain .py files in jupytext percent format; cells are cached by content hash of body + deps= + upstream artifact hashes; reruns hit the cache in milliseconds. A live HTML viewer serves the rendered site; export tearsheet produces committed markdown summaries.

Why It Matters

Jupyter notebooks drift. Re-running a 6-month-old notebook rarely works — outputs depend on kernel state, variable memory, and whichever cells ran in whichever order. jellycell enforces plain-text source, explicit cell dependencies, and a content-addressed artifact store so "run again in six months" is a one-command exercise.

What Ships in 1.4

• jellycell.tearsheets Python API (new in 1.4) — jt.findings() / methodology() / audit() callable from inside a jc.step cell so manuscripts live in the cache graph. Stable template_overrides keep regens byte-identical.
• Plain-text notebooks — jupytext percent format (.py), diff-able in git
• Content-addressed cache — per-cell hash; stale cells re-run, unchanged cells hit cache in < 100 ms
• Cell tags — jc.load, jc.step, jc.table, jc.figure structure the DAG
• HTML viewer — jellycell view with file-watching at localhost:5179
• Tearsheet export — jellycell export tearsheet <notebook> produces a committed markdown summary
• Polyglot-monorepo docs — jellycell prompt --write --nested drops AGENTS.md / CLAUDE.md scoped to the notebook subtree; plays nice with TS/Next.js repos
• Scaffolders — jellycell init for a project, jellycell new for a notebook

Ecosystem Fit

factor-factory >= 1.0 ships a factor_factory.jellycell subtree with canonical cell patterns, figure adapters, and tearsheet payload shapes. Every showcase under packages/python-showcase/ in the blaise-website monorepo is a jellycell project. subway-access >= 0.5 uses jellycell for its engine-audit appendix.