notebooks

notebooks is a skill for Claude Code, Codex from fmschulz/omics-skills. It costs 34 tokens per session (3,600 once invoked), scanned A, original, MIT.

A workflow for creating, running, checking, and converting reproducible marimo or Jupyter notebooks. These notebooks combine analysis code, written explanation, and results in one file.

In plain words
What is it for?
Use it to build analysis notebooks, extend existing Jupyter files, convert between notebook formats, and deliver validated results.
Why use it?
It makes sure the delivered notebook has been run from start to finish on a fresh kernel and that its figures are included.

Skill for Claude CodeCodex

Part of the omics-skills plugin — 35 skills, 4 agents shipped together

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/fmschulz/omics-skills/notebooks
Any agent
npx skills add fmschulz/omics-skills --skill notebooks
Clone the repo
git clone --depth 1 https://github.com/fmschulz/omics-skills

Made for: Claude Code, Codex.

Or install omics-skills, the plugin that ships this one along with the rest of its 35 skills, 4 agents.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for notebooks

README.md
[![agentmods](https://agentmods.dev/badge/skills/fmschulz/omics-skills/notebooks.svg)](https://agentmods.dev/skills/fmschulz/omics-skills/notebooks)
Your own site
<a href="https://agentmods.dev/skills/fmschulz/omics-skills/notebooks"><img src="https://agentmods.dev/badge/skills/fmschulz/omics-skills/notebooks.svg" alt="Measured on agentmods" height="20"></a>
Per session 34 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,600 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00034 $0.03600
Opus 5 $0.00017 $0.01800
Sonnet 5 $0.00007 $0.00720
Haiku 4.5 $0.00003 $0.00360

Measured 4d ago against content hash b33601307463, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

notebooks scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.

The scan reads SKILL.md. This mod also ships 6 executable files (fixtures/simple_marimo.py, scripts/convert_notebook.py, scripts/execute_notebook.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/notebooks/SKILL.md · 233 lines

How it starts

The opening of the file, as written. The whole thing — 233 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Notebooks

A single skill for authoring, validating, and delivering reproducible analysis notebooks. Marimo is the default format; Jupyter is supported for existing .ipynb files and when a downstream tool requires JSON. Conversion between the two formats is part of this skill.

A notebook is not "done" until it has been executed end-to-end on a fresh kernel and every figure is embedded in the delivered file.

Instructions

  1. Pick the format.

    • New notebook: write a marimo .py notebook. Use the canonical cell layout (one concept per cell, final expression renders, no if guards around outputs, no try/except for control flow).
    • Existing .ipynb to extend or polish: keep it as Jupyter unless the user asks to convert.
    • Conversion: see "Convert between marimo and Jupyter" below.
  2. Outline before coding. Write the notebook plan (purpose, data sources, analysis steps, expected outputs/plots) as the first markdown cell, then implement against that plan.

  3. Keep marimo cells clean. These are hard rules for every .py notebook:

    • Markdown cells use one plain triple-quoted string: mo.md(r"""...""") or mo.md(f"""...""") only when interpolation is required. Put the prose directly inside the string; never paste quoted string fragments such as " ... " lines inside the markdown body.
    • Do not leave empty generated cells, whitespace-only cells, or @app.cell def _(): return placeholders. Remove them before final verification.
    • Do not accept a marimo "fix" prompt blindly. If one is accepted during interactive editing, inspect the diff immediately and remove unintended PEP 723/header/cell churn.
  4. Set up the kernel and dependencies.

    • Marimo. Pin dependencies in the PEP 723 script header at the top of the .py file:
      # /// script
      # requires-python = ">=3.12"
      # dependencies = [
      #     "marimo",
      #     "polars",
      #     "duckdb",
      #     "matplotlib",
      #     # ... add every import used in the notebook
      # ]
      # ///
      
      Run with uvx marimo run --sandbox <notebook.py> or edit interactively with uvx marimo edit --sandbox <notebook.py>. The sandbox reads the header and resolves the notebook environment.
    • Jupyter. Register a named ipykernel for the project's pixi env before the first execution and pin the kernel in the notebook metadata. The kernel name is mandatory — the generic python3 kernel leaks the system interpreter:
      pixi run python -m ipykernel install --user --name <project> --display-name "<project> (pixi)"
      
      Then in <notebook>.ipynb confirm:
      "kernelspec": {"name": "<project>", "display_name": "<project> (pixi)"}
      
      Add every import used in the notebook to pixi.toml so the kernel can resolve it from a clean install.

Read the full file on GitHub · 233 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 4d ago First seen · 233 lines · 34 tokens per session scan A b33601307463

Subscribe to this mod's changes

notebooks is a skill published in the GitHub repository fmschulz/omics-skills (7 stars, last pushed 10d ago), licensed MIT. It adds 34 tokens to every session and 3,600 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.