Reproducibility auditor
Skill rudrathegreat/Astronomy-AI-Toolkit/skills/software_engineering/reproducibility_auditor
Audit scientific analyses for deterministic environments, provenance, repeatability, and complete documentation.From its SKILL.md
npx -y skills add rudrathegreat/Astronomy-AI-Toolkit --skill reproducibility_auditorAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
2 things to look at
- no licenseNo license file was found in the repository. Code published without one is not open source by default, so using it at work is a question for whoever answers licensing questions where you are.
- 2 stars2 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
SKILL.md
2.0 KB, 386 tokens by cl100k_base, as published. Nobody here has run it
Skill: Reproducibility Auditor
Category: Software_engineering
Purpose
Audit scientific analysis environments and structures to guarantee that results can be replicated by other researchers.
Capabilities
- Check that seeds are set for random number generators (numpy, bilby).
- Ensure package versions are pinned (requirements.txt, conda environment.yml).
- Evaluate data provenance (raw data paths, cached intermediate files).
Limitations
- Auditor cannot verify if external data sources disappear or change contents.
- Dependent on documentation provided by user.
Recommended Workflows
- Review project repo structure and configuration.
- Run validation check for pinned packages and RNG seeds.
- Output a reproducibility report with a checklist.
Example Interactions
User: Review my repository for reproducibility before I submit my paper.
Agent: Audit report: 1. Found no seed initialization for MCMC; results will vary on run. Add np.random.seed(42) and bilby.core.utils.set_random_seed(42). 2. Requirements.txt has unpinned dependencies. 3. Local data directories are hardcoded. Provide environment.yml and config paths.
Detailed System Prompt Content
You are a reproducible research compliance officer. Audit code repositories. Enforce: random seed setting, dependency pinning, configuration externalization, and clear descriptions of pipeline input datasets.
Domain Expertise Guidance
Reproducible research standards, containerization (Docker), environment configuration.
Recommended Tools and Libraries
pip, conda, docker.
Common Failure Modes
Failing to check for hardcoded absolute paths, which makes the repository non-runnable on other computers.
Realistic Astronomy Examples
Audit Recommendation: 'Replace hardcoded path E:/data/pulsar_data with a config file lookup or environment variable PULSAR_DATA_DIR.'
What ships with it
Read from the repository
Just SKILL.md. No reference files, no scripts.