Export Ml Notebook

Organization

probabl-ai

export-ml-notebook

Write a jupytext percent file out as a notebook with empty outputs. `data_analysis/`, `experiments/`, and `audit/` are filled with empty outputs. Pass `--html` when that notebook should also become a viewer on the site. Kernel `notebook convert` stays for a source that is itself the execution. Trigger on notebook, ipynb, HTML, or executed-report requests, or when export-ml-project dispatches notebooks.

Overview

Publisher	probabl-ai
Repository	`skore-skills`
Skill name	`export-ml-notebook`
Stars	144
Forks	9
Bundled files	1
License	BSD-3-Clause
Links	Skill source Repository Homepage

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
1 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by probabl-ai on GitHub. Read the source before you install it.

Installation

Install the Export Ml Notebook AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/probabl-ai/skore-skills/tree/main/skills/export-ml-notebook
Enable the skill in any chat where you want it available.

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/probabl-ai/skore-skills.git /tmp/skore-skills
mkdir -p .claude/skills
cp -r /tmp/skore-skills/skills/export-ml-notebook .claude/skills/export-ml-notebook

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Export Ml Notebook in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Export Ml Notebook on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Export Ml Notebook is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Export ML Notebook

Source of truth stays the # %% .py. This skill only writes derived .ipynb (and optional .nb.html). Do not rewrite the .py from the notebook. cells run is not a substitute.

Human-facing prose

Details: setup-workspace references/human_facing_prose.md. Tell the user the written .ipynb (and HTML viewer) paths. Do not quote notebook convert, --html, or site build as something they should run.

While policy.notebooks is true, explore-ml-data fills the analysis file it wrote. model-ml-pipeline and evaluate-ml-pipeline fill the experiment script and, when it exists, audit/<stem>.py, with empty outputs. audit-ml-pipeline fills audit/<stem>.py only on a direct close. This skill owns on-demand fills, other sources, and turning the flag on when it is null or false. A data-analysis, experiment, or audit source uses notebook fill, not notebook convert.

Workflow checkpoint execution

Stage owners read policy before executing a human # %% source. Only policy.notebooks true enables automatic notebook materialization; null and false keep the stage's non-notebook execution and do not change policy.

When true, exploratory analysis uses materialize.py as its one run, then notebook fill writes the .ipynb with empty outputs. Add --html only when policy.site is also true. An experiment or audit file is the same: notebook fill writes the .ipynb with empty outputs, and materialize.py is the run that writes the digest and the HTML viewers. Do not notebook convert those files and do not cells run them. Kernel notebook convert stays only for a source that is itself the execution (the unfitted experiment snapshot). A generated notebook records its source fingerprint; loop notebooks fills or converts again only when the source or required HTML changed.

Lookup

Run status and read policy.notebooks. Take the first row that matches. Fill leaves code-cell outputs empty. Do not stop after reading the table.

`policy.notebooks`	Request	Then
`null`	any	step 2, then match this table again. Do not take the convert row only because it is listed first.
`false`	any	step 3. Do not fill until the policy is `true`.
`true`	the user asked for HTML, a site viewer, or the notebook on the site	step 4 `notebook fill --html`, then step 5. `data_analysis/`, `experiments/<stem>.py`, and `audit/<stem>.py` stay `notebook fill`, not `notebook convert`.
`true`	the source is itself the execution (unfitted experiment snapshot, before `skore.evaluate`) and the user did not ask for HTML or a site viewer	kernel `notebook convert`, not `notebook fill`
`true`	notebook, ipynb, or convert, with no HTML and no site viewer	step 4 `notebook fill` only. No `--html`. No `site build`.

Sequence

python -m skore_skills status. Read policy.notebooks and skills.
If policy.notebooks is null: persist python -m skore_skills policy set notebooks true. Do not AskUserQuestion. Then load add-python-package for jupytext and nbformat (agent) and continue.
If policy.notebooks is false: say notebooks are off; offer to turn them on. Do not fill until the policy is true.
Fill the requested percent file (default data_analysis/data_analysis.py when the user did not name one):
```
bash
python -m skore_skills notebook fill data_analysis/data_analysis.py
```
Fill does not execute the file and leaves code-cell outputs empty. It needs jupytext and nbformat, not nbclient, ipywidgets, or IPython. Do not start EDA, an evaluation, or an audit from this skill to produce those outputs. experiments/<stem>.py and audit/<stem>.py are filled the same way. Missing jupytext / nbformat / nbconvert stays a one-line skip.

If the user wants the notebook on the site, load add-python-package for nbconvert (agent) and pass --html (writes a self-contained <stem>.nb.html next to the .py). The site links that file from the associated exploratory data analysis or design report. It opens as a page of its own.

Optional --out path.ipynb. Keep *.ipynb gitignored unless the user asks setup-git to track them.

Fill-only requests (notebook / ipynb / convert, no HTML or site viewer): run only that notebook fill line, with no --html. Tell the user the written .ipynb path. Do not mention --html or site build as an optional aside.

Kernel notebook convert is only for a source that is itself the execution: the unfitted experiment snapshot, before skore.evaluate exists. Convert injects %matplotlib inline for that kernel run so seaborn / matplotlib last expressions emit image/png, then strips that setup cell from the written notebook. Do not put %matplotlib inline in the .py (style / ruff would reject it). If that convert fails because ipywidgets is missing, load add-python-package for it (agent) and convert again.
After --html, if policy.site is true, export-ml-site is installed, run python -m skore_skills site build --if-stale so the viewer is packaged. Run --html and site build only when the user asked for HTML or a site viewer. Fill without --html does not rebuild the site. Skip in one line otherwise. If site build errors with mkdocs-material is required, load add-python-package for mkdocs-material (agent) and build once more. Do not pixi add / uv add. If that skill is missing, or the retry still fails, name the error in one line. Name a build error; do not fail the fill, and do not claim the website was updated. When the build ran, say the website has been updated and paste the file:// line from that output verbatim. Do not name the markdown file or the HTML viewer path. Do not quote the CLI.

Stop conditions

Do not git commit or git end-turn.
Do not run cells run as a substitute for fill.
Do not pixi add / uv add; load add-python-package.
Missing skill or missing source → one-line skip.
Do not name --html or python -m skore_skills site build unless the user asked for HTML or a site viewer. Tell the user paths, not the CLI.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

evals/evals.json10 KB

Frequently asked questions

What does the Export Ml Notebook AI skill do?

Why use Export Ml Notebook on TypingMind?

Because you install it once and use it with any model. Export Ml Notebook is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Export Ml Notebook in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/probabl-ai/skore-skills/tree/main/skills/export-ml-notebook. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Export Ml Notebook?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Export Ml Notebook?

As many as you like. As long as a model supports skills, you can use Export Ml Notebook with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Export Ml Notebook AI skill free?

Yes. It is published on GitHub by probabl-ai under the BSD-3-Clause license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Export ML Notebook

Human-facing prose

Workflow checkpoint execution

Lookup

Sequence

Stop conditions

Bundled files

Frequently asked questions

Related AI Skills

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