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bioinformatics-project-setup

Create bioinformatics project skeletons with separated data, code, and results directories.

Updated Aug 23, 2026
Search Tags:#templates#bioinformatics#setup#project-structure#git-init#consensus-workflow
One-click install
npx skills add https://github.com/kstawiski/claude-skills --skill bioinformatics-project-setup
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: bioinformatics-project-setup
Source: https://github.com/kstawiski/claude-skills/tree/main/bioinformatics-project-setup
Command: npx skills add https://github.com/kstawiski/claude-skills --skill bioinformatics-project-setup

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires git, and includes scripts (resource) components.

What problem does it solve?

This skill provides a standardized scaffold for new bioinformatics, genomics, imaging, or computational biology projects, ensuring clear separation of raw data, code, analysis workspace, and results, with a ready-to-share structure.

Core Features & Use Cases

  • Directory scaffolding: Creates data_bucket (read-only mount point), metadata, plan, analysis, results, code, work, and temp directories.
  • CLAUDE.md / AGENTS.md generation: Produces documentation to guide agents and collaborators; enables consistent onboarding.
  • Git initialization: Initializes a git repository in the code/ directory with a template README and .gitignore to manage code and pipelines.
  • Consensus-ready templates: Generates a plan/plan.md template for the consensus workflow and a basic analysis scaffold.

Quick Start

Run the setup script to scaffold a new project. For example: bash scripts/setup.sh /path/to/project 'Study Title'

Frequently Asked Questions about bioinformatics-project-setup

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I set up a reproducible bioinformatics project structure?▼

A bioinformatics project skeleton separates raw data, metadata, code, analysis, and results directories. It enforces a read-only data_bucket mount and a git-tracked code directory to ensure reproducible structures for genomics and computational biology projects.

What's the best way to organize genomics data and code directories?▼

Organize genomics data and code by creating separate directories: a read-only data_bucket mount for raw data, a git-initialized code directory for pipelines, plus isolated metadata, analysis, results, work, and temp folders for complete project separation.

Do I need git installed to scaffold a computational biology project?▼

Yes, git is a required dependency. The setup process initializes a git repository in the code/ directory with a template README and .gitignore to manage bioinformatics pipelines and ensure version-controlled reproducibility for computational biology projects.

How do I create a consensus workflow plan for bioinformatics analysis?▼

The scaffold generates a plan/plan.md template within the project structure to support consensus workflows. It also creates CLAUDE.md and AGENTS.md files to guide agents and collaborators through the bioinformatics analysis process.

Can I use this project scaffold for imaging and computational biology teams?▼

Yes, the scaffold explicitly targets genomics, imaging, and computational biology projects. It provides a standardized, ready-to-share structure that ensures smooth team onboarding and reproducible directory layouts across diverse computational biology workflows.

Why separate raw data and code in a bioinformatics project directory?▼

Separating raw data and code prevents accidental modification of source data by enforcing a read-only data_bucket mount. Code remains isolated in a git-tracked directory, ensuring reproducible analysis workflows and clean project onboarding for computational biology teams.

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