gget
Skill0 starsChanged 4 months agoMITView on GitHub
gget CLI and Python workflow for quick genomic database queries, sequence lookup, BLAST-style searches, enrichment checks, and reproducible bioinformatics evidence logs.
- Installs packages
14 other projects use a version of this, 2 of them edited.
Adopted by
Most recently taken up by
- BrightGold70/skillstheir own version, first seen Nov 8, 2026
- MarieLynneBlock/arcanum-artifextheir own version, first seen Nov 1, 2026
- maratmu1988-afk/medical-researchtheir own version, first seen Oct 20, 2026
- davidanthonyn/claude-skillstheir own version, first seen Oct 20, 2026
- patoangolano/bio-stacktheir own version, first seen Oct 15, 2026
- Luooops/skillstheir own version, first seen Oct 13, 2026
Other versions14 projects use a version of this.
Word for word · 0
None found.
Edited · 2
- Fmarzochi/EGC★ 6199% same
- SamuelNDCE/claude-super-skill-library★ 199% same
Wrote their own version · 12
- wu-yc/LabClaw★ 1.1ksame name
- LeonChaoX/qinyan-academic-skills★ 910same name
- qinyan-ai/qinyan-academic-skills★ 80same name
- MarieLynneBlock/arcanum-artifex★ 4same name
- BrightGold70/skills★ 1same name
- garri333/Skills★ 1same name
- Luooops/skills★ 1same name
- maratmu1988-afk/medical-research★ 1same name
- uppifyagency/boilerplate-saas-2026★ 1same name
- yf8578/claude-skills-collection★ 1same name
2 more not listed
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
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The file
We split this skill into the rules it gives an agent
10 rules. 0 are common, 6 are distinctive, and 4 are very specific to this project.
0
Common
Most of the 14 edited versions we read keep these.
6
Distinctive
This author's own choices. Worth a look.
4
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Very specific: line 10
Use this skill when a task needs quick bioinformatics lookup across genomic reference databases with the gget CLI or Python package.
- Very specific: line 24
Use a dedicated workflow instead of gget when the task requires regulated clinical interpretation, high-throughput production pipelines, or …
- Distinctive: line 30
Use a clean Python environment.
- Very specific: line 40
If uv is available:
- Very specific: line 48
Before relying on an older environment, upgrade gget and re-check the module docs.
- Distinctive: line 71
Check the current module documentation for arguments.
- Distinctive: line 72
Run a small query first.
- Distinctive: line 78
Use current upstream docs for exact arguments.
- Distinctive: line 94
Do not assume every module supports every Python version or dependency set.
- Distinctive: line 118
Run a small BLAST query:
“Common” is measured: kept by at least 6 in 10 of the 14 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
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 --helpIf uv is available:
uv venv
. .venv/bin/activate
uv pip install ggetBefore 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:
- Identify the species, assembly, gene ID type, and database needed.
- Check the current module documentation for arguments.
- Run a small query first.
- Save output with an explicit filename and date.
- 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 alphafoldandgget pdb: inspect protein-structure references.gget enrichr,gget opentargets,gget archs4,gget bgee,gget cbio, andgget 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.jsonFetch gene metadata:
gget info ENSG00000012048 -o brca1-info.jsonFetch a sequence:
gget seq ENSG00000012048 -o brca1-seq.faRun a small BLAST query:
gget blast "MEEPQSDPSVEPPLSQETFSDLWKLLPEN" -l 10 -o blast-results.jsonPython 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
ggetversion? - 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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