Gget

This is a unified interface to 20+ bioinformatics databases that works both as a CLI and Python package. You can pull gene info from Ensembl, run BLAST searches, grab AlphaFold structures, and do enrichment analysis without memorizing different API formats for each database. The modules are tested biweekly since the underlying databases change their schemas regularly, which matters if you're automating anything. It's genuinely useful for quick interactive lookups and exploratory work, but if you're doing heavy batch processing or need more complex BLAST configurations, you'll still want biopython. The consistent interface across modules is the real win here. Install it in a clean virtual environment because bioinformatics dependencies clash constantly.

skills.sh installs: 2K
GitHub stars: 56.6K
last updated: Aug 21, 2026

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Install

$ npx -y skills add k-dense-ai/scientific-agent-skills --skill gget --agent claude-code

Installs into .claude/skills of the current project.

Files

gget

Overview

gget is a command-line bioinformatics tool and Python package providing unified access to 20+ genomic databases and analysis methods. Query gene information, sequence analysis, protein structures, viral sequences, expression data, disease associations, and mouse tissue/cell specificity metrics through a consistent interface. Most gget modules work both as command-line tools and as Python functions.

Important: The databases queried by gget are continuously updated, which sometimes changes their structure. Guidance here targets gget 0.30.8 (reviewed 2026-09-30). For reproducible work, pin gget==0.30.8; for broken upstream database adapters, update gget after checking release notes.

Installation

Install gget in a clean virtual environment to avoid conflicts:

# Reproducible install targeting this skill
uv venv --python 3.13 .venv
source .venv/bin/activate
uv pip install "gget==0.30.8"

import gget

Quick Start

Inspect module-specific syntax before querying:

gget info --help
gget pdb --help

Most modules return:

Command-line: JSON (default) or CSV with -csv flag
Python: DataFrame or dictionary

SKILL.md on GitHub

Details

Repository: github.com/k-dense-ai/scientific-agent-skills
Categories: Backend & APIs, AI & Agent Building, Data Science & ML, Automation & Workflows, Python, Sales & Marketing, CLI & Terminal, Databases
First seen: Jun 3, 2026