# gget

> Fast CLI/Python queries to 20+ bioinformatics databases — use for quick lookups of gene info, BLAST searches, AlphaFold structures, and enrichment analysis. Best for interactive exploration and simple queries.

**Use case**: Quick gene and sequence lookups across 20+ bio databases

**Canonical URL**: https://agentcookbooks.com/skills/gget/

**Topics**: claude-code, skills, science, bioinformatics

**Trigger phrases**: "look up this gene", "gget search", "AlphaFold structure", "gene enrichment analysis", "find gene info"

**Source**: [K-Dense AI](https://github.com/K-Dense-AI/scientific-agent-skills/tree/main/scientific-skills/gget)

**License**: MIT

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## What it does

`gget` is a Claude Code skill from K-Dense AI's [scientific-agent-skills repo](https://github.com/K-Dense-AI/scientific-agent-skills). It turns Claude into a gget user for fast, interactive queries across 20+ bioinformatics databases — gene search and metadata (`gget.search`, `gget.info`), BLAST searches, AlphaFold structure retrieval (`gget.alphafold`), enrichment analysis (`gget.enrichr`), sequence alignment (`gget.muscle`), and ARCHS4 expression data.

A session produces Python code or CLI commands that return structured gene information, protein structures, or enrichment results in seconds — without writing API integration code.

## When to use it

Reach for it when:

- You need gene metadata (gene name, synonyms, Ensembl ID, description, associated diseases) for a list of gene symbols
- You want to fetch an AlphaFold structure for a protein without navigating the AlphaFold database web UI
- You're doing a quick enrichment analysis on a gene list (GO terms, KEGG, WikiPathways) interactively

When *not* to reach for it:

- Batch processing or complex multi-step pipelines where output needs to be parsed and integrated — use `biopython` for more control
- Multi-database programmatic integration — use `bioservices`

## Install

Copy the `SKILL.md` from K-Dense AI's [gget folder](https://github.com/K-Dense-AI/scientific-agent-skills/tree/main/scientific-skills/gget) into `.claude/skills/gget/` in your project.

Trigger phrases: "look up this gene", "gget search", "AlphaFold structure", "gene enrichment analysis".

## What a session looks like

A typical session has three phases:

1. **Query specification.** Provide gene symbols, Ensembl IDs, sequences, or terms to search. Claude selects the appropriate `gget` function and species parameter.
2. **gget call.** The function runs and returns a pandas DataFrame or file (PDB for AlphaFold) with structured results — no manual parsing required.
3. **Downstream use.** Claude helps interpret or format the results: filtering enrichment terms by adjusted p-value, extracting specific metadata fields, or visualizing the structure path for PyMOL/ChimeraX.

## Receipts

**Where it works well:**
- Gene symbol to Ensembl ID conversion for a list of genes — `gget.info()` handles this for multiple species with a single call
- AlphaFold structure retrieval — `gget.alphafold()` downloads the predicted structure file ready for visualization without navigating the web UI

**Where it backfires:**
- gget's database queries depend on external service availability; intermittent failures from Ensembl, NCBI, or other upstream APIs need error handling that gget's simple interface doesn't always surface clearly
- Very large gene lists may hit rate limits or timeouts on some gget backends — split into batches for reliability

**Pattern that works:** use `gget.search()` to find the right Ensembl IDs first, then pass those IDs to `gget.info()` for structured metadata — more reliable than searching by gene symbol alone when gene names are ambiguous across species.

## Source and attribution

Originally authored by [K-Dense Inc.](https://github.com/K-Dense-AI). The canonical SKILL.md lives in the [`gget` folder](https://github.com/K-Dense-AI/scientific-agent-skills/tree/main/scientific-skills/gget) of their public scientific-agent-skills repository.

License: MIT. Install, adapt, and redistribute with attribution preserved.

This page documents the skill from a practitioner's perspective. For the formal spec and any updates, defer to the source repo.