probabl-skills
Skills to support Machine Learning experimentation using the Python ecosystem.
Open source Open in the app JSON README (API)
About
Skills to support Machine Learning experimentation using the Python ecosystem.
Details
- Kind
- Plugins
- Topic
- No topic detected
- Publisher
- probabl-ai
- Origin
- marketplace
- Category
- ferramentas
- Stars
- 122
- Forks
- 8
- Open pull requests
- 1
- Last push
- 2026-09-11T11:11:54Z
- Repository state
- ativo
- Language
- Python
- License
- BSD-3-Clause
- Added
- 2026-08-30 01:48:58
- Updated
- 2026-08-30 01:48:58
- Origin id
probabl-ai/skills/probabl-skills
README
# 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](https://skore.probabl.ai/) 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: ```text ╭────────────────────────────────────────────────────────────────────────╮ │ > 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](https://github.com/probabl-ai/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: ```bash skore skills install ``` You can use `uvx` or `pixi exec` to install the `skore` CLI and directly run the command in an isolated environment: ```bash uvx --from skore-cli skore skills install ``` or ```bash pixi exec --spec skore-cli skore skills install ``` If you prefer `npx`, then you can use: ```bash 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](https://docs.claude.com/en/docs/claude-code/plugin-marketplaces): ```bash /plugin marketplace add probabl-ai/skills ``` ```bash /plugin install probabl-skills@probabl-skills ``` `/plugin update` pulls new releases. ## Skills in detail ### ML pipeline lifecycle | Skill | Description | | --- | --- | | [explore-ml-data](skills/explore-ml-data/SKILL.md) | Explore the dataset before designing any model. | | [build-ml-pipeline](skills/build-ml-pipeline/SKILL.md) | Build a machine learning pipeline from the data source to the learner, including multi-tables engineering. | | [evaluate-ml-pipeline](skills/evaluate-ml-pipeline/SKILL.md) | Evaluate a complex machine learning pipeline and get structured reports including metrics, plots, and diagnostics. | | [test-ml-pipeline](skills/test-ml-pipeline/SKILL.md) | Make sure that your machine learning pipeline is production-ready statistically and functionally. | | [smoke-test-ml-pipeline](skills/smoke-test-ml-pipeline/SKILL.md) | Stress test your machine learning pipeline on future data to make sure it works. | | [audit-ml-pipeline](skills/audit-ml-pipeline/SKILL.md) | Once testing and the experiment are done, audit the model by loading a skore report and investigate. | ### Iteration loop | Skill | Description | | --- | --- | | [iterate-ml-experiment](skills/iterate-ml-experiment/SKILL.md) | Design, keep track of experiments, and iterate on them. | | [iterate-from-skore](skills/iterate-from-skore/SKILL.md) | Use skore to run diagnostics and checks that can be reported and addressed in the next experiment. | | [iterate-from-user](skills/iterate-from-user/SKILL.md) | 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](skills/organize-ml-workspace/SKILL.md) | An organized workspace to keep track of your experiments. | | [python-code-style](skills/python-code-style/SKILL.md) | Enforce best practices out-of-the-box from the Python ecosystem in your code. | | [python-env-manager](skills/python-env-manager/SKILL.md) | Bootstrapping the experiment setup based on your favorite Python environment manager. | | [data-science-python-stack](skills/data-science-python-stack/SKILL.md) | Opinionated one-library-per-job Python stack, organized into mandatory / user-choice / optional / transitive tiers. | ### API references | Skill | Description | | --- | --- | | [python-api](skills/python-api/SKILL.md) | Discover the public API of any installed Python package to help agents navigate without polluting your workspace. |