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affaan-m/gget

gget CLI and Python workflow for quick genomic database queries, sequence lookup, BLAST-style searches, enrichment checks, and reproducible bioinformatics evidence logs. Use when a task needs quick bioinformatics lookup across genomic reference databases with the gget CLI or Python package.

NewUpdated Sep 9, 2026

gget

Use this skill when a task needs quick bioinformatics lookup across genomic reference databases with the gget CLI or Python package.

When to Use

  • Finding Ensembl IDs, gene metadata, transcript details, or sequences.
  • Running quick BLAST or BLAT lookups without building a full local pipeline.
  • Fetching reference genome links and annotations from Ensembl.
  • Querying protein structure, pathway, cancer, expression, or disease-association modules through a single interface.
  • Creating a reproducible first-pass evidence log before moving to heavier tools such as Biopython, Snakemake, Nextflow, BLAST+, or database-specific clients.

Use a dedicated workflow instead of gget when the task requires regulated clinical interpretation, high-throughput production pipelines, or fine-grained control over database versions and local indexes.

Installation

Use a clean Python environment.

python -m venv .venv
. .venv/bin/activate
python -m pip install --upgrade pip
python -m pip install --upgrade gget
gget --help

If uv is available:

uv venv
. .venv/bin/activate
uv pip install gget

Before relying on an older environment, upgrade gget and re-check the module docs. The upstream databases queried by gget change over time.

Basic Patterns

CLI shape:

gget <module> [arguments] [options]

Python shape:

import gget

result = gget.search(["BRCA1"], species="human")
print(result)

Common workflow:

  1. Identify the species, assembly, gene ID type, and database needed.
  2. Check the current module documentation for arguments.
  3. Run a small query first.
  4. Save output with an explicit filename and date.
  5. Record module name, version, arguments, and database assumptions.

Common Modules

Use current upstream docs for exact arguments. These modules are common first choices:

  • gget search: find Ensembl IDs from search terms.
  • gget info: retrieve metadata for Ensembl, UniProt, or related IDs.
  • gget seq: fetch nucleotide or amino-acid sequences.
  • gget ref: retrieve reference genome download links.
  • gget blast: run a quick BLAST query.
  • gget blat: locate a sequence against supported genome assemblies.
  • gget muscle: run multiple sequence alignment.
  • gget diamond: run local sequence alignment against reference sequences.
  • gget alphafold and gget pdb: inspect protein-structure references.
  • gget enrichr, gget opentargets, gget archs4, gget bgee, gget cbio, and gget cosmic: explore enrichment, target, expression, cancer, and disease association data.

Do not assume every module supports every Python version or dependency set. Some optional scientific dependencies have narrower version support than the core package.

Quick Examples

Find genes:

gget search -s human brca1 dna repair -o brca1-search.json

Fetch gene metadata:

gget info ENSG00000012048 -o brca1-info.json

Fetch a sequence:

gget seq ENSG00000012048 -o brca1-seq.fa

Run a small BLAST query:

gget blast "MEEPQSDPSVEPPLSQETFSDLWKLLPEN" -l 10 -o blast-results.json

Python example:

import gget

genes = gget.search(["BRCA1", "DNA repair"], species="human")
info = gget.info(["ENSG00000012048"])
sequence = gget.seq("ENSG00000012048")

Reproducibility Log

For scientific outputs, include enough metadata to replay the query.

| Date | gget version | Module | Query | Species/assembly | Output | Notes |
| --- | --- | --- | --- | --- | --- | --- |
| 2026-05-11 | `gget --version` | search | `BRCA1 DNA repair` | human | `brca1-search.json` | Docs checked before run |

Also record:

  • Python version and environment manager.
  • Any optional dependency installed through gget setup.
  • Database-specific identifiers returned by the query.
  • Whether output is JSON, CSV, FASTA, or a DataFrame export.
  • Any failures that were resolved by upgrading gget.

Review Checklist

  • Did you upgrade or verify the installed gget version?
  • Did you check the current upstream module docs before using arguments?
  • Is the species or assembly explicit?
  • Are identifiers preserved exactly, including Ensembl/UniProt prefixes?
  • Is the result labeled as database output rather than clinical interpretation?
  • Is the query reproducible from the saved command or Python snippet?
  • Are optional dependencies installed in an isolated environment?

References

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Overall Score

87/100

Grade

A

Excellent

Grades are signals, not a certification. Always review a skill yourself before use.

Safety

88

Quality

87

Clarity

89

Completeness

81

Summary

gget is a Python package and CLI skill for rapid genomic database queries, enabling researchers to look up gene metadata, sequences, BLAST results, and enrichment data from public bioinformatics databases like Ensembl without building full production pipelines. It provides a reproducible first-pass analysis interface with clear scoping against heavier tools and explicit guidance on when NOT to use it.

Detected Capabilities

package installation (pip/uv)CLI execution (gget commands)Python library import and function callsfile output (JSON, FASTA, CSV)network requests to public genomic databases (Ensembl, UniProt)version checking

Trigger Keywords

Phrases that agents use to match this skill to user intent.

gene lookupsequence fetchBLAST searchgenomic database queryensembl lookupreproducible bioinformaticsenrichment analysisprotein structure

Risk Signals

INFO

Network requests to external genomic databases (Ensembl, UniProt)

Common Modules section, Quick Examples
INFO

File output to project directory (JSON, FASTA, CSV)

Quick Examples, Reproducibility Log section
INFO

Python package installation with pip/uv

Installation section

Referenced Domains

External domains referenced in skill content, detected by static analysis.

doi.orggithub.compachterlab.github.io

Use Cases

  • Query Ensembl gene IDs and metadata from gene names or identifiers
  • Fetch nucleotide or protein sequences for genes of interest
  • Run quick BLAST or BLAT similarity searches against genomic databases
  • Retrieve reference genome download links and annotations
  • Explore gene enrichment, expression, and disease association data
  • Create reproducible evidence logs for scientific analysis with documented query parameters
  • Prototype bioinformatics workflows before implementing in production pipelines

Quality Notes

  • Excellent scoping: explicitly states when NOT to use the skill (regulated clinical work, production pipelines, local indexes)
  • Strong reproducibility guidance with a detailed checklist for recording metadata (version, database, query parameters, environment)
  • Clear module reference with common use cases and examples for both CLI and Python
  • Comprehensive review checklist ensures users verify versions and understand output limitations
  • Good edge case coverage: warns about Python version and dependency compatibility differences across modules
  • Well-documented common patterns with CLI and Python examples side-by-side
  • References upstream documentation and encourages users to check current docs before running queries
  • Practical guidance on environment isolation (venv/uv) reduces risk of version conflicts
Model: claude-haiku-4-5-20251001Analyzed: Sep 9, 2026

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Version History

  1. v2.0

    Contract changed: description

    ✦ AISkill activation description expanded to clarify gget use for quick bioinformatics lookups across genomic reference databases.

    triggering2026-09-09

    LATEST
  2. v1.1

    Content updated

    ✦ AINo behavioral changes detected.

    2026-07-14

    View This Version
  3. v1.0

    2026-05-15

    View This VersionInitial version

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