Manage reproducible ML Python environments with Pixi and pixi.lock.
npx skills add https://github.com/nishide-dev/claude-code-ml-research --skill tool-pixi
Please help me install this Agent Skill. Skill: tool-pixi Source: https://github.com/nishide-dev/claude-code-ml-research/tree/main/skills/tool-pixi Command: npx skills add https://github.com/nishide-dev/claude-code-ml-research --skill tool-pixi
It solves the dependency-hell problem in ML projects by helping you create deterministic, hardware-aware Python environments that are consistent across machines and teams.
Example use case: You want a training environment that matches a specific CUDA version for GPU runs, while keeping a separate CPU/MPS-friendly setup for local development and CI—using one manifest and locked outputs.
Use the tool-pixi skill to set up a new ML project environment by initializing pixi in your repository, adding both conda and PyPI dependencies, and installing to generate a reproducible pixi.lock file.
High-intent search queries and answers about installing and using this skill.
Reproducible ML environments with CUDA-aware installs are created using Pixi to resolve dependency and runtime compatibility across local, CI, and GPU servers. You initialize pixi in your repository, add dependencies, and install to generate a deterministic pixi.lock file.
Mixing conda-based system libraries with PyPI packages within one Pixi manifest is supported for smoother ML setup. This unified workflow allows you to manage both dependency sources together and lock exact package versions in a pixi.lock file.
Managing separate CPU and GPU configurations is best done by defining multi-environment setups for dev, test, and prod in one pixi.toml manifest. Pixi lets you install or run the exact environment you need from this single configuration file.
Pixi checks GPU and CUDA compatibility by defining minimum system requirements and using virtual package checks during install and run. This mechanism selects compatible binaries based on your hardware validation before runtime failures occur.
A pixi.lock file is needed for deterministic environment replication because it locks exact package versions for repeatable experiments and deployments. It solves the dependency-hell problem by ensuring consistent, hardware-aware Python environments across machines and teams.
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