Reproducible Analysis Modules

Reproducible Analysis Modules is a Data skill for Claude Code, published by xuzhougeng in wisp-science.

1K stars106 forkson xuzhougeng/wisp-scienceAdded 2026/08/21+1% in starsRepository updated 2026/08/21

agent-skillsai-agentai-assistantai-for-scienceai4sciencebioinformaticscomputational-biologydesktop-appllmlocal-firstmcpmodel-context-protocolpythonreproducible-researchresearch-assistantrstatsrustscientific-computingscientific-workflowtauri

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Install Reproducible Analysis Modules

Copy Reproducible Analysis Modules into your Claude Code skills folder. Run the command in your terminal, or review the source on GitHub before installing.

terminal

npx degit https://github.com/xuzhougeng/wisp-science/tree/main/skills/analysis-workflow ~/.claude/skills/analysis-workflow

Requires Node.js. Downloads this skill only — not the rest of the repository — into your Claude Code skills folder.

Without Node.js

git clone https://github.com/xuzhougeng/wisp-science.git

Clones the whole repository, then copy the skill’s own directory into your skills folder yourself.

In this catalog

Source file: skills/analysis-workflow/SKILL.md in xuzhougeng/wisp-science
Installs to: ~/.claude/skills/analysis-workflow
Collection: One of 25 skills cataloged from this repository
Category: Data — 668 skills

What Reproducible Analysis Modules does

Reproducible Analysis Modules organizes multi-step scientific analyses into self-contained modules with scripts, inputs, figures, tables, and methods. Use it for workflows like QC→PCA→DEG→GSEA when you need traceable outputs and recorded versions.

Reproducible Analysis Modules is cataloged under Data on DirSkills. Reproducible Analysis Modules comes from a repository tagged agent-skills, ai-agent, ai-assistant, ai-for-science and ai4science.

Documentation

README

Excerpt

Reproducible Analysis Modules

Use this skill for a scientific workflow with two or more analysis stages or when a stage produces scripts plus result files. It defines project organization and methods capture; load figure-style as well whenever a stage creates or revises a plot.

1. Plan module boundaries

Before writing outputs, list the modules and the dependency edges between them. Use stable ASCII names. Conventional acronyms such as QC, PCA, DEG, and GSEA may stay uppercase; otherwise prefer a short kebab-case name.

Respect a compatible layout that already exists. Do not reorganize unrelated user files merely to impose this convention.

This is the opening of the README. Read the full README on GitHub.

Frequently asked about Reproducible Analysis Modules

What else does xuzhougeng publish alongside Reproducible Analysis Modules?
Reproducible Analysis Modules is one of 25 skills that DirSkills catalogs from xuzhougeng/wisp-science, the repository it ships in. Its siblings there include Agent Infini, Bear Counter and Bear Map. Each one is a separate skill with its own page in this directory, installs the same way Reproducible Analysis Modules does, and is maintained by xuzhougeng in that same repository. The rest of the collection is listed on the xuzhougeng/wisp-science page.
How does Reproducible Analysis Modules compare to other Data skills?
Reproducible Analysis Modules ranks #451 by stars among the 668 Data skills in this catalog. The most-starred ones next to it are Benchmark Methodology, Jupyter Notebook and Solana. DirSkills ranks by the star count of the repository each skill ships in, so that order reflects how popular those repositories are rather than any review of Reproducible Analysis Modules against them. Open each page to compare what they document and how they install.