Reproducibility Checklist — Open Science Best Practices
## Overview
Best use case
Reproducibility Checklist — Open Science Best Practices is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
## Overview
Teams using Reproducibility Checklist — Open Science Best Practices should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/reproducibility-checklist/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How Reproducibility Checklist — Open Science Best Practices Compares
| Feature / Agent | Reproducibility Checklist — Open Science Best Practices | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
## Overview
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Reproducibility Checklist — Open Science Best Practices ## Overview Ensure research is reproducible, transparent, and meets open science standards. ## Pre-Registration - [ ] Study registered on ClinicalTrials.gov, OSF, or AsPredicted before data collection - [ ] Primary outcome and analysis plan pre-specified - [ ] Deviations from pre-registration documented and justified ## Data Availability - [ ] Raw data deposited in domain repository (GEO, PDB, PRIDE, Zenodo, Dryad, Figshare) - [ ] Data dictionary/codebook provided - [ ] Sensitive data: de-identification method documented, access controls described - [ ] DOI assigned to dataset ## Code Availability - [ ] Analysis code in public repository (GitHub, GitLab, Zenodo) - [ ] Environment specification (requirements.txt, conda env, Docker, renv.lock) - [ ] Random seeds fixed and documented - [ ] README with reproduction instructions ## Materials - [ ] Reagent catalog numbers, lot numbers, manufacturer - [ ] Custom materials: synthesis protocol or request process - [ ] Cell lines: STR authentication, mycoplasma testing - [ ] Antibodies: RRID or Antibody Registry ID ## Statistical Reporting - [ ] All statistical tests, parameters, and software versions reported - [ ] Effect sizes and confidence intervals (not just p-values) - [ ] Multiple comparison correction method stated - [ ] Sample size justification (power analysis) ## Reporting Standards - [ ] Applicable guideline followed (STROBE/CONSORT/PRISMA/ARRIVE/MIQE) - [ ] Checklist completed and submitted with manuscript
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