How does it help ensure scientific reproducibility?

The skill enforces structured sections for data sources, tool versions, and exact parameters, and uses an interactive checklist to ensure every procedural detail is documented.

How do I start a new experiment with this skill?

You can trigger the process by saying 'start experiment', 'create lab notebook', or using the specific command '/research-exp'.

Can I export my research notes for sharing?

Yes, it includes a built-in workflow to export notebooks to PDF using Pandoc and Typst, allowing you to generate professional documents for collaborators.

Is the documentation process fully automated?

It uses interactive dialogue. Rather than just filling in blanks, Claude asks probing questions about your goals and observations to help you write high-quality narrative text.

Does this skill support Python-based research?

Yes, it provides dedicated templates and management workflows for Jupyter notebooks (.ipynb), which are the standard for Python-based bioinformatics and data science.

Lab Notebook Manager

Name: Lab Notebook Manager
Author: dakesan

bydakesan

Data Science & ML

Manages structured bioinformatics lab notebooks through interactive dialogue to ensure high-quality, reproducible research documentation.

About

The Lab Notebook Manager skill transforms Claude into a research assistant that guides scientists through the entire lifecycle of a bioinformatics experiment. By utilizing interactive dialogue rather than static templates, it helps researchers articulate testable hypotheses, document precise methods, and synthesize complex observations into coherent narrative results and discussions. It supports both Jupyter and Markdown formats, enforces rigorous scientific quality standards, and provides seamless export options to PDF via Typst, making it an essential tool for maintaining professional and reproducible research logs.

Key Features

Automated PDF export using Pandoc and Typst integration
Interactive narrative experiment creation (Purpose, Hypothesis, Methods)
Support for Jupyter (.ipynb) and Markdown (.md) notebook formats
Mandatory post-execution checklists for quality control and results documentation
Standardized naming conventions and research workflow integration
0 GitHub stars

Use Cases

Starting a new bioinformatics analysis with a structured, testable hypothesis
Documenting step-by-step procedures and software versions for reproducible science
Generating professional PDF reports from experimental observations and discussions

About

Key Features

Automated PDF export using Pandoc and Typst integration
Interactive narrative experiment creation (Purpose, Hypothesis, Methods)
Support for Jupyter (.ipynb) and Markdown (.md) notebook formats
Mandatory post-execution checklists for quality control and results documentation
Standardized naming conventions and research workflow integration
0 GitHub stars

Use Cases

Starting a new bioinformatics analysis with a structured, testable hypothesis
Documenting step-by-step procedures and software versions for reproducible science
Generating professional PDF reports from experimental observations and discussions