Probabl Skills

A set of skills to steer your AI-assisted machine learning experiments. The skills help you:

• build your machine learning pipeline with core data science libraries (e.g. scikit-learn, skrub, skore, pandas, polar) while ensuring your agent follows correct methodologies
• evaluate and store your results so you can easily audit and get insights from them
• connect your agent to Skore Hub to get a comprehensive view of your experiments and results
• iterate on your next experiments using insights from Skore diagnostics and your own feedback
• organize your workspace according to best practices for data science projects (e.g. cookiecutter template)

Probabl skills let you focus on the science while AI agents handle the implementation, guided by two important ingredients: core data science libraries for maintainability and methodological best practices for running your machine learning experiments properly.

In practice, from a prompt such as:

╭────────────────────────────────────────────────────────────────────────╮
│ > Given the context in the file `data/README.md` and the data located  │
│   in `data/`, let's build a first machine learning pipeline that will  │
│   serve as baseline for the next experiments that we are going to run  │
│   together.                                                            │
╰────────────────────────────────────────────────────────────────────────╯

you can expect your agent to start experimenting with you. The skills work well with models such as Claude Opus and Sonnet and produce great results with smaller models such as Qwen 3.6 30B or DeepSeek v4 Flash.

As for agent harnesses, we tested them with Claude Code, OpenCode, Cursor, and GitHub Copilot and found no significant difference in terms of skill invocation.

Install

You can install the skills using the skore CLI that you can install from PyPI or from conda-forge and run the following command.

First install skore-cli:

# with pip
pip install skore-cli
# with uv
uv tool install skore-cli
# with pixi
pixi global install skore-cli

Then run the following command:

skore skills install

You can use uvx or pixi exec to install the skore CLI and directly run the command in an isolated environment:

uvx --from skore-cli skore skills install

or

pixi exec --spec skore-cli skore skills install

If you prefer npx, then you can use:

npx skills add probabl-ai/skills

Alternative — Claude Code plugin marketplace

If you only use Claude Code and prefer the native plugin flow, this repo is also a Claude Code plugin marketplace:

/plugin marketplace add probabl-ai/skills

/plugin install probabl-skills@probabl-skills

/plugin update pulls new releases.

Skills in detail

ML pipeline lifecycle

Skill	Description
explore-ml-data	Explore the dataset before designing any model.
build-ml-pipeline	Build a machine learning pipeline from the data source to the learner, including multi-tables engineering.
evaluate-ml-pipeline	Evaluate a complex machine learning pipeline and get structured reports including metrics, plots, and diagnostics.
test-ml-pipeline	Make sure that your machine learning pipeline is production-ready statistically and functionally.
smoke-test-ml-pipeline	Stress test your machine learning pipeline on future data to make sure it works.
audit-ml-pipeline	Once testing and the experiment are done, audit the model by loading a skore report and investigate.

Iteration loop

Skill	Description
iterate-ml-experiment	Design, keep track of experiments, and iterate on them.
iterate-from-skore	Use skore to run diagnostics and checks that can be reported and addressed in the next experiment.
iterate-from-user	As a user, be in the loop and propose new experiments — free-text, a scientific article URL, or a resource link (GitHub issue / spec / reference repo).

Workspace and tooling

Skill	Description
organize-ml-workspace	An organized workspace to keep track of your experiments.
python-code-style	Enforce best practices out-of-the-box from the Python ecosystem in your code.
python-env-manager	Bootstrapping the experiment setup based on your favorite Python environment manager.
data-science-python-stack	Opinionated one-library-per-job Python stack, organized into mandatory / user-choice / optional / transitive tiers.

API references

Skill	Description
python-api	Discover the public API of any installed Python package to help agents navigate without polluting your workspace.