A set of skills to partner with you throughout your machine learning experimentation journey. It helps you with:
- organizing your workspace
- building your machine learning pipeline with the right libraries while ensuring good methodologies
- evaluating and storing your results so you can easily audit and get insights from them
- coupling it with Skore Hub to get a comprehensive view of your experiments and their results
- iterating on your next experiments using insights from Skore diagnostics and your own feedback
So we aim to let you focus on the science, with AI agents handling the implementation, guided by two important ingredients: great libraries for maintainability and good methodologies to run experiments correctly.
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 give 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.
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 installYou 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 installor
pixi exec --spec skore-cli skore skills installIf you prefer npx, then you can use:
npx skills add probabl-ai/skillsIf 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.
| 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 is done, audit by loading a skore report and investigate. |
| 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). |
| Skill | Description |
|---|---|
| organize-ml-workspace | An organized workspace to keep track of your experiments. |
| python-code-style | Enforce good practices out-of-the-box for the Python ecosystem for 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. |
| Skill | Description |
|---|---|
| python-api | Discover the public API of any installed Python package to help agents navigate without polluting your workspace. |