Package completed analyses with environment freezing and reproducibility validation.
npx skills add https://github.com/tim-krausz/mlstack --skill ship-analysis
Please help me install this Agent Skill. Skill: ship-analysis Source: https://github.com/tim-krausz/mlstack/tree/main/ship-analysis Command: npx skills add https://github.com/tim-krausz/mlstack --skill ship-analysis
This Skill streamlines shipping a completed analysis into a reproducible, shareable package, ensuring results can be reproduced, environments frozen, and artifacts archived.
Run the /ship-analysis workflow to validate reproducibility, freeze the environment, and package a deliverable.
High-intent search queries and answers about installing and using this skill.
To package a completed data analysis into a reproducible deliverable, the workflow automates pre-flight artifact discovery, environment capture, reproducibility validation, and final archival into a shareable package.
An analysis environment freeze captures dependencies and validates artifacts across notebooks, data outputs, and scripts. This process ensures reproducibility checks pass before generating an executive summary and packaging the deliverable.
Yes, you can use this workflow to run pre-flight checks and generate an executive summary for your data science project. It validates discovered artifacts, verifies reproducibility, and packages the summary alongside the frozen environment.
Yes, the packaging process includes a git commit and push step for collaboration. After verifying reproducibility and freezing the environment, the workflow commits the final artifacts and summary to your repository.
The best way to archive data science artifacts and ensure reproducibility is applying a structured workflow that validates outputs, freezes the environment, and runs reproducibility checks before final archival.
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