A Swiss Army knife for bioinformatics queries that wraps 20+ genomic databases into a consistent CLI and Python interface. Run BLAST searches, pull gene info from Ensembl and UniProt, grab AlphaFold structures, align sequences with MUSCLE, or download reference genomes without writing custom parsers for each database's API. The modules are tested biweekly and updated when upstream databases change structure, which matters since broken scrapers are usually how these tools die. Solid for exploratory work and one-off queries, though the docs suggest switching to biopython for batch processing or bioservices for complex multi-database workflows. Returns DataFrames in Python or JSON/CSV on the command line.
- skills.sh installs
- 259
- GitHub stars
- 33.2K
- last updated
- Aug 21, 2026
Install
$ npx -y skills add davila7/claude-code-templates --skill gget --agent claude-codeInstalls 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, expression data, and disease associations through a consistent interface. All 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. gget modules are tested automatically on a biweekly basis and updated to match new database structures when necessary.
Installation
Install gget in a clean virtual environment to avoid conflicts:
# Using uv (recommended)
uv uv pip install gget
# Or using pip
uv pip install --upgrade gget
# In Python/Jupyter
import gget
Quick Start
Basic usage pattern for all modules:
# Command-line
gget <module> [arguments] [options]
# Python
gget.module(arguments, options)
Most modules return:
- Command-line: JSON (default) or CSV with
-csvflag - Python: DataFrame or dictionary
Sponsors
Details
- Repository
- github.com/davila7/claude-code-templates
- Categories
- Backend & APIs, Data Science & ML, Automation & Workflows, Python, CLI & Terminal, Databases
- First seen
- Jun 3, 2026
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