jupyter-reproducibility-checker
Skill for checking and ensuring Jupyter notebook reproducibility
npx skills add a5c-ai/babysitter --skill jupyter-reproducibility-checker --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Jupyter Reproducibility Checker Skill ## Purpose Check and ensure reproducibility of Jupyter notebooks including cell execution order, environment dependencies, and output consistency. ## Capabilities - Verify execution order - Check dependencies - Test reproducibility - Clear and rerun notebooks - Document environments - Generate requirements ## Usage Guidelines 1. Load notebook 2. Check execution order 3. Identify dependencies 4. Test fresh execution 5. Document environment 6. Generate reports ## Process Integration Works within scientific discovery workflows for: - Reproducibility audits - Notebook cleanup - Environment documentation - Quality assurance ## Configuration - Check criteria - Execution settings - Environment capture - Report formatting ## Output Artifacts - Reproducibility reports - Dependency lists - Environment files - Cleaned notebooks
- Purpose
- Capabilities
- Usage Guidelines
- Process Integration
- Configuration
- Output Artifacts
What does the jupyter-reproducibility-checker skill do?
Skill for checking and ensuring Jupyter notebook reproducibility
How do I install it?
Run `npx skills add a5c-ai/babysitter --skill jupyter-reproducibility-checker --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From a5c-ai/babysitter, a repository with 1,642 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.