Agent skill · Databases

gget

CLI/Python toolkit for rapid bioinformatics queries. Preferred for quick BLAST searches. Access to 20+ databases: gene info (Ensembl/UniProt), AlphaFold, ARCHS4, Enrichr, OpenTargets, COSMIC, genome downloads. For advanced BLAST/batch processing, use biopython. For multi-database integration, use bioservices.

FreedomIntelligencegithub.com/FreedomIntelligenceGitHub ↗
claude-codeships scripts
Install
npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill gget --agent claude-code

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

Facts
Files in the skill folder: 7
SKILL.md size: 24 KB
Bundled scripts: yes
Path: skills/gget/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 2,909
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, 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: ```bash # 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: ```bash # Command-line gget <module> [arguments] [options] # Python gget.module(arguments, options) ``` Most modules return: - **Command-line**: JSON (default) or CSV with `-csv` flag - **Python**: DataFrame or dictionary Common flags across modules: - `-o/--out`: Save results to file - `-q/--quiet`: Suppress progress information - `-

What's inside
Steps it walks through
  1. Overview
  2. Installation
  3. Quick Start
  4. Module Categories
  5. 1. Reference & Gene Information
  6. 2. Sequence Analysis & Alignment
  7. 3. Structural & Protein Analysis
  8. 4. Expression & Disease Data
  9. 5. Additional Tools
  10. Common Workflows
  11. Workflow 1: Gene Discovery to Sequence Analysis
  12. Workflow 2: Sequence Alignment and Structure
  13. Workflow 3: Gene Expression and Enrichment
  14. Workflow 4: Disease and Drug Analysis
Ships with 6 files
  • references/database_info.md
  • references/module_reference.md
  • references/workflows.md
  • scripts/batch_sequence_analysis.py
  • scripts/enrichment_pipeline.py
  • scripts/gene_analysis.py
Commands it runs
Using uv (recommended)
uv uv pip install gget
Or using pip
uv pip install --upgrade gget
In Python/Jupyter
import gget
Command-line
gget <module> [arguments] [options]
Python
List available species
More from OpenClaw-Medical-Skills
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About this skill
What does the gget skill do?

CLI/Python toolkit for rapid bioinformatics queries. Preferred for quick BLAST searches. Access to 20+ databases: gene info (Ensembl/UniProt), AlphaFold, ARCHS4, Enrichr, OpenTargets, COSMIC, genome downloads. For advanced BLAST/batch processing, use biopython. For multi-database integration, use bioservices.

How do I install it?

Run `npx skills add FreedomIntelligence/OpenClaw-Medical-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 FreedomIntelligence/OpenClaw-Medical-Skills, a repository with 2,909 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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