Agent skill · Databases

gget

Fast CLI/Python queries to 20+ bioinformatics databases. Use for quick lookups: gene info, BLAST/BLAT, viral sequence downloads, AlphaFold structures, enrichment analysis, OpenTargets, COSMIC, CELLxGENE, and 8cube mouse specificity/expression data. Best for interactive exploration and simple queries. For batch processing or advanced BLAST use biopython; for multi-database Python workflows use bioservices.

K-Dense-AIgithub.com/K-Dense-AIGitHub ↗
claude-codecan modify filesships scriptsMIT
Install
npx skills add K-Dense-AI/scientific-agent-skills --skill gget --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 9
SKILL.md size: 7 KB
Bundled scripts: yes
Version: 1.4
Allowed tools: ReadWriteEditBash
Requires: Requires Python >=3.8 and gget 0.30.5-compatible APIs. Optional setup modules may install scientific dependencies that…
Path: skills/gget/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 32,619
Language: Python
Read our review of the source →

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# 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 0.30.5 (PyPI current as of 2026-06-07). For reproducible work, pin `gget==0.30.5`; for broken upstream database adapters, update gget after checking release notes. ## Installation Install gget in a clean virtual environment to avoid conflicts: ```bash # Reproducible install targeting this skill uv venv .venv source .venv/bin/activate uv pip install "gget==0.30.5" # In Python/Jupyter import gget ``` ## Quick Start Basic usage pattern for all modules: ```bash # Command-line gget <module> [arguments] [options] # Python gget.module(arguments, options) ``` Most modules return: - **Command-line**: JSON (default) or CSV with `-csv` flag -

What's inside
Steps it walks through
  1. Overview
  2. Installation
  3. Quick Start
  4. Module Categories
  5. Common Workflows
  6. Best Practices
  7. Data Retrieval
  8. Sequence Analysis
  9. Expression and Disease Data
  10. Structure Prediction
  11. Viral Data
  12. Error Handling
  13. Output Formats
  14. Command-line
Ships with 8 files
  • references/common_workflows.md
  • references/database_info.md
  • references/module_catalog.md
  • references/module_reference.md
  • references/workflows.md
  • scripts/batch_sequence_analysis.py
  • scripts/enrichment_pipeline.py
  • scripts/gene_analysis.py
Commands it runs
Reproducible install targeting this skill
uv venv .venv
source .venv/bin/activate
uv pip install "gget==0.30.5"
In Python/Jupyter
import gget
Command-line
gget <module> [arguments] [options]
Python
More from scientific-agent-skills
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About this skill
What does the gget skill do?

Fast CLI/Python queries to 20+ bioinformatics databases. Use for quick lookups: gene info, BLAST/BLAT, viral sequence downloads, AlphaFold structures, enrichment analysis, OpenTargets, COSMIC, CELLxGENE, and 8cube mouse specificity/expression data. Best for interactive exploration and simple queries. For batch processing or advanced BLAST use biopython; for multi-database Python workflows use bioservices.

How do I install it?

Run `npx skills add K-Dense-AI/scientific-agent-skills --skill gget --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From K-Dense-AI/scientific-agent-skills, a repository with 32,619 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

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