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Gget

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jimmc414
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.

Overview

Publisherjimmc414
RepositoryKosmos
Skill namegget
Stars
585
Forks
105
Bundled files
6
Links
Skill sourceRepository
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 6 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by jimmc414 on GitHub. Read the source before you install it.

Installation

Install the Gget AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/jimmc414/Kosmos.git /tmp/Kosmos
mkdir -p .claude/skills
cp -r /tmp/Kosmos/kosmos-claude-scientific-skills/scientific-skills/gget .claude/skills/gget
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Gget in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Gget on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Gget is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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
  • -csv: Return CSV format (command-line only)

Module Categories

1. Reference & Gene Information

gget ref - Reference Genome Downloads

Retrieve download links and metadata for Ensembl reference genomes.

Parameters:

  • species: Genus_species format (e.g., 'homo_sapiens', 'mus_musculus'). Shortcuts: 'human', 'mouse'
  • -w/--which: Specify return types (gtf, cdna, dna, cds, cdrna, pep). Default: all
  • -r/--release: Ensembl release number (default: latest)
  • -l/--list_species: List available vertebrate species
  • -liv/--list_iv_species: List available invertebrate species
  • -ftp: Return only FTP links
  • -d/--download: Download files (requires curl)

Examples:

bash
# List available species
gget ref --list_species

# Get all reference files for human
gget ref homo_sapiens

# Download only GTF annotation for mouse
gget ref -w gtf -d mouse
python
# Python
gget.ref("homo_sapiens")
gget.ref("mus_musculus", which="gtf", download=True)
gget search - Gene Search

Locate genes by name or description across species.

Parameters:

  • searchwords: One or more search terms (case-insensitive)
  • -s/--species: Target species (e.g., 'homo_sapiens', 'mouse')
  • -r/--release: Ensembl release number
  • -t/--id_type: Return 'gene' (default) or 'transcript'
  • -ao/--andor: 'or' (default) finds ANY searchword; 'and' requires ALL
  • -l/--limit: Maximum results to return

Returns: ensembl_id, gene_name, ensembl_description, ext_ref_description, biotype, URL

Examples:

bash
# Search for GABA-related genes in human
gget search -s human gaba gamma-aminobutyric

# Find specific gene, require all terms
gget search -s mouse -ao and pax7 transcription
python
# Python
gget.search(["gaba", "gamma-aminobutyric"], species="homo_sapiens")
gget info - Gene/Transcript Information

Retrieve comprehensive gene and transcript metadata from Ensembl, UniProt, and NCBI.

Parameters:

  • ens_ids: One or more Ensembl IDs (also supports WormBase, Flybase IDs). Limit: ~1000 IDs
  • -n/--ncbi: Disable NCBI data retrieval
  • -u/--uniprot: Disable UniProt data retrieval
  • -pdb: Include PDB identifiers (increases runtime)

Returns: UniProt ID, NCBI gene ID, primary gene name, synonyms, protein names, descriptions, biotype, canonical transcript

Examples:

bash
# Get info for multiple genes
gget info ENSG00000034713 ENSG00000104853 ENSG00000170296

# Include PDB IDs
gget info ENSG00000034713 -pdb
python
# Python
gget.info(["ENSG00000034713", "ENSG00000104853"], pdb=True)
gget seq - Sequence Retrieval

Fetch nucleotide or amino acid sequences for genes and transcripts.

Parameters:

  • ens_ids: One or more Ensembl identifiers
  • -t/--translate: Fetch amino acid sequences instead of nucleotide
  • -iso/--isoforms: Return all transcript variants (gene IDs only)

Returns: FASTA format sequences

Examples:

bash
# Get nucleotide sequences
gget seq ENSG00000034713 ENSG00000104853

# Get all protein isoforms
gget seq -t -iso ENSG00000034713
python
# Python
gget.seq(["ENSG00000034713"], translate=True, isoforms=True)

2. Sequence Analysis & Alignment

gget blast - BLAST Searches

BLAST nucleotide or amino acid sequences against standard databases.

Parameters:

  • sequence: Sequence string or path to FASTA/.txt file
  • -p/--program: blastn, blastp, blastx, tblastn, tblastx (auto-detected)
  • -db/--database:
    • Nucleotide: nt, refseq_rna, pdbnt
    • Protein: nr, swissprot, pdbaa, refseq_protein
  • -l/--limit: Max hits (default: 50)
  • -e/--expect: E-value cutoff (default: 10.0)
  • -lcf/--low_comp_filt: Enable low complexity filtering
  • -mbo/--megablast_off: Disable MegaBLAST (blastn only)

Examples:

bash
# BLAST protein sequence
gget blast MKWMFKEDHSLEHRCVESAKIRAKYPDRVPVIVEKVSGSQIVDIDKRKYLVPSDITVAQFMWIIRKRIQLPSEKAIFLFVDKTVPQSR

# BLAST from file with specific database
gget blast sequence.fasta -db swissprot -l 10
python
# Python
gget.blast("MKWMFK...", database="swissprot", limit=10)
gget blat - BLAT Searches

Locate genomic positions of sequences using UCSC BLAT.

Parameters:

  • sequence: Sequence string or path to FASTA/.txt file
  • -st/--seqtype: 'DNA', 'protein', 'translated%20RNA', 'translated%20DNA' (auto-detected)
  • -a/--assembly: Target assembly (default: 'human'/hg38; options: 'mouse'/mm39, 'zebrafinch'/taeGut2, etc.)

Returns: genome, query size, alignment positions, matches, mismatches, alignment percentage

Examples:

bash
# Find genomic location in human
gget blat ATCGATCGATCGATCG

# Search in different assembly
gget blat -a mm39 ATCGATCGATCGATCG
python
# Python
gget.blat("ATCGATCGATCGATCG", assembly="mouse")
gget muscle - Multiple Sequence Alignment

Align multiple nucleotide or amino acid sequences using Muscle5.

Parameters:

  • fasta: Sequences or path to FASTA/.txt file
  • -s5/--super5: Use Super5 algorithm for faster processing (large datasets)

Returns: Aligned sequences in ClustalW format or aligned FASTA (.afa)

Examples:

bash
# Align sequences from file
gget muscle sequences.fasta -o aligned.afa

# Use Super5 for large dataset
gget muscle large_dataset.fasta -s5
python
# Python
gget.muscle("sequences.fasta", save=True)
gget diamond - Local Sequence Alignment

Perform fast local protein or translated DNA alignment using DIAMOND.

Parameters:

  • Query: Sequences (string/list) or FASTA file path
  • --reference: Reference sequences (string/list) or FASTA file path (required)
  • --sensitivity: fast, mid-sensitive, sensitive, more-sensitive, very-sensitive (default), ultra-sensitive
  • --threads: CPU threads (default: 1)
  • --diamond_db: Save database for reuse
  • --translated: Enable nucleotide-to-amino acid alignment

Returns: Identity percentage, sequence lengths, match positions, gap openings, E-values, bit scores

Examples:

bash
# Align against reference
gget diamond GGETISAWESQME -ref reference.fasta --threads 4

# Save database for reuse
gget diamond query.fasta -ref ref.fasta --diamond_db my_db.dmnd
python
# Python
gget.diamond("GGETISAWESQME", reference="reference.fasta", threads=4)

3. Structural & Protein Analysis

gget pdb - Protein Structures

Query RCSB Protein Data Bank for structure and metadata.

Parameters:

  • pdb_id: PDB identifier (e.g., '7S7U')
  • -r/--resource: Data type (pdb, entry, pubmed, assembly, entity types)
  • -i/--identifier: Assembly, entity, or chain ID

Returns: PDB format (structures) or JSON (metadata)

Examples:

bash
# Download PDB structure
gget pdb 7S7U -o 7S7U.pdb

# Get metadata
gget pdb 7S7U -r entry
python
# Python
gget.pdb("7S7U", save=True)
gget alphafold - Protein Structure Prediction

Predict 3D protein structures using simplified AlphaFold2.

Setup Required:

bash
# Install OpenMM first
uv pip install openmm

# Then setup AlphaFold
gget setup alphafold

Parameters:

  • sequence: Amino acid sequence (string), multiple sequences (list), or FASTA file. Multiple sequences trigger multimer modeling
  • -mr/--multimer_recycles: Recycling iterations (default: 3; recommend 20 for accuracy)
  • -mfm/--multimer_for_monomer: Apply multimer model to single proteins
  • -r/--relax: AMBER relaxation for top-ranked model
  • plot: Python-only; generate interactive 3D visualization (default: True)
  • show_sidechains: Python-only; include side chains (default: True)

Returns: PDB structure file, JSON alignment error data, optional 3D visualization

Examples:

bash
# Predict single protein structure
gget alphafold MKWMFKEDHSLEHRCVESAKIRAKYPDRVPVIVEKVSGSQIVDIDKRKYLVPSDITVAQFMWIIRKRIQLPSEKAIFLFVDKTVPQSR

# Predict multimer with higher accuracy
gget alphafold sequence1.fasta -mr 20 -r
python
# Python with visualization
gget.alphafold("MKWMFK...", plot=True, show_sidechains=True)

# Multimer prediction
gget.alphafold(["sequence1", "sequence2"], multimer_recycles=20)
gget elm - Eukaryotic Linear Motifs

Predict Eukaryotic Linear Motifs in protein sequences.

Setup Required:

bash
gget setup elm

Parameters:

  • sequence: Amino acid sequence or UniProt Acc
  • -u/--uniprot: Indicates sequence is UniProt Acc
  • -e/--expand: Include protein names, organisms, references
  • -s/--sensitivity: DIAMOND alignment sensitivity (default: "very-sensitive")
  • -t/--threads: Number of threads (default: 1)

Returns: Two outputs:

  1. ortholog_df: Linear motifs from orthologous proteins
  2. regex_df: Motifs directly matched in input sequence

Examples:

bash
# Predict motifs from sequence
gget elm LIAQSIGQASFV -o results

# Use UniProt accession with expanded info
gget elm --uniprot Q02410 -e
python
# Python
ortholog_df, regex_df = gget.elm("LIAQSIGQASFV")

4. Expression & Disease Data

gget archs4 - Gene Correlation & Tissue Expression

Query ARCHS4 database for correlated genes or tissue expression data.

Parameters:

  • gene: Gene symbol or Ensembl ID (with --ensembl flag)
  • -w/--which: 'correlation' (default, returns 100 most correlated genes) or 'tissue' (expression atlas)
  • -s/--species: 'human' (default) or 'mouse' (tissue data only)
  • -e/--ensembl: Input is Ensembl ID

Returns:

  • Correlation mode: Gene symbols, Pearson correlation coefficients
  • Tissue mode: Tissue identifiers, min/Q1/median/Q3/max expression values

Examples:

bash
# Get correlated genes
gget archs4 ACE2

# Get tissue expression
gget archs4 -w tissue ACE2
python
# Python
gget.archs4("ACE2", which="tissue")
gget cellxgene - Single-Cell RNA-seq Data

Query CZ CELLxGENE Discover Census for single-cell data.

Setup Required:

bash
gget setup cellxgene

Parameters:

  • --gene (-g): Gene names or Ensembl IDs (case-sensitive! 'PAX7' for human, 'Pax7' for mouse)
  • --tissue: Tissue type(s)
  • --cell_type: Specific cell type(s)
  • --species (-s): 'homo_sapiens' (default) or 'mus_musculus'
  • --census_version (-cv): Version ("stable", "latest", or dated)
  • --ensembl (-e): Use Ensembl IDs
  • --meta_only (-mo): Return metadata only
  • Additional filters: disease, development_stage, sex, assay, dataset_id, donor_id, ethnicity, suspension_type

Returns: AnnData object with count matrices and metadata (or metadata-only dataframes)

Examples:

bash
# Get single-cell data for specific genes and cell types
gget cellxgene --gene ACE2 ABCA1 --tissue lung --cell_type "mucus secreting cell" -o lung_data.h5ad

# Metadata only
gget cellxgene --gene PAX7 --tissue muscle --meta_only -o metadata.csv
python
# Python
adata = gget.cellxgene(gene=["ACE2", "ABCA1"], tissue="lung", cell_type="mucus secreting cell")
gget enrichr - Enrichment Analysis

Perform ontology enrichment analysis on gene lists using Enrichr.

Parameters:

  • genes: Gene symbols or Ensembl IDs
  • -db/--database: Reference database (supports shortcuts: 'pathway', 'transcription', 'ontology', 'diseases_drugs', 'celltypes')
  • -s/--species: human (default), mouse, fly, yeast, worm, fish
  • -bkg_l/--background_list: Background genes for comparison
  • -ko/--kegg_out: Save KEGG pathway images with highlighted genes
  • plot: Python-only; generate graphical results

Database Shortcuts:

  • 'pathway' → KEGG_2021_Human
  • 'transcription' → ChEA_2016
  • 'ontology' → GO_Biological_Process_2021
  • 'diseases_drugs' → GWAS_Catalog_2019
  • 'celltypes' → PanglaoDB_Augmented_2021

Examples:

bash
# Enrichment analysis for ontology
gget enrichr -db ontology ACE2 AGT AGTR1

# Save KEGG pathways
gget enrichr -db pathway ACE2 AGT AGTR1 -ko ./kegg_images/
python
# Python with plot
gget.enrichr(["ACE2", "AGT", "AGTR1"], database="ontology", plot=True)
gget bgee - Orthology & Expression

Retrieve orthology and gene expression data from Bgee database.

Parameters:

  • ens_id: Ensembl gene ID or NCBI gene ID (for non-Ensembl species). Multiple IDs supported when type=expression
  • -t/--type: 'orthologs' (default) or 'expression'

Returns:

  • Orthologs mode: Matching genes across species with IDs, names, taxonomic info
  • Expression mode: Anatomical entities, confidence scores, expression status

Examples:

bash
# Get orthologs
gget bgee ENSG00000169194

# Get expression data
gget bgee ENSG00000169194 -t expression

# Multiple genes
gget bgee ENSBTAG00000047356 ENSBTAG00000018317 -t expression
python
# Python
gget.bgee("ENSG00000169194", type="orthologs")
gget opentargets - Disease & Drug Associations

Retrieve disease and drug associations from OpenTargets.

Parameters:

  • Ensembl gene ID (required)
  • -r/--resource: diseases (default), drugs, tractability, pharmacogenetics, expression, depmap, interactions
  • -l/--limit: Cap results count
  • Filter arguments (vary by resource):
    • drugs: --filter_disease
    • pharmacogenetics: --filter_drug
    • expression/depmap: --filter_tissue, --filter_anat_sys, --filter_organ
    • interactions: --filter_protein_a, --filter_protein_b, --filter_gene_b

Examples:

bash
# Get associated diseases
gget opentargets ENSG00000169194 -r diseases -l 5

# Get associated drugs
gget opentargets ENSG00000169194 -r drugs -l 10

# Get tissue expression
gget opentargets ENSG00000169194 -r expression --filter_tissue brain
python
# Python
gget.opentargets("ENSG00000169194", resource="diseases", limit=5)
gget cbio - cBioPortal Cancer Genomics

Plot cancer genomics heatmaps using cBioPortal data.

Two subcommands:

search - Find study IDs:

bash
gget cbio search breast lung

plot - Generate heatmaps:

Parameters:

  • -s/--study_ids: Space-separated cBioPortal study IDs (required)
  • -g/--genes: Space-separated gene names or Ensembl IDs (required)
  • -st/--stratification: Column to organize data (tissue, cancer_type, cancer_type_detailed, study_id, sample)
  • -vt/--variation_type: Data type (mutation_occurrences, cna_nonbinary, sv_occurrences, cna_occurrences, Consequence)
  • -f/--filter: Filter by column value (e.g., 'study_id:msk_impact_2017')
  • -dd/--data_dir: Cache directory (default: ./gget_cbio_cache)
  • -fd/--figure_dir: Output directory (default: ./gget_cbio_figures)
  • -dpi: Resolution (default: 100)
  • -sh/--show: Display plot in window
  • -nc/--no_confirm: Skip download confirmations

Examples:

bash
# Search for studies
gget cbio search esophag ovary

# Create heatmap
gget cbio plot -s msk_impact_2017 -g AKT1 ALK BRAF -st tissue -vt mutation_occurrences
python
# Python
gget.cbio_search(["esophag", "ovary"])
gget.cbio_plot(["msk_impact_2017"], ["AKT1", "ALK"], stratification="tissue")
gget cosmic - COSMIC Database

Search COSMIC (Catalogue Of Somatic Mutations In Cancer) database.

Important: License fees apply for commercial use. Requires COSMIC account credentials.

Parameters:

  • searchterm: Gene name, Ensembl ID, mutation notation, or sample ID
  • -ctp/--cosmic_tsv_path: Path to downloaded COSMIC TSV file (required for querying)
  • -l/--limit: Maximum results (default: 100)

Database download flags:

  • -d/--download_cosmic: Activate download mode
  • -gm/--gget_mutate: Create version for gget mutate
  • -cp/--cosmic_project: Database type (cancer, census, cell_line, resistance, genome_screen, targeted_screen)
  • -cv/--cosmic_version: COSMIC version
  • -gv/--grch_version: Human reference genome (37 or 38)
  • --email, --password: COSMIC credentials

Examples:

bash
# First download database
gget cosmic -d --email user@example.com --password xxx -cp cancer

# Then query
gget cosmic EGFR -ctp cosmic_data.tsv -l 10
python
# Python
gget.cosmic("EGFR", cosmic_tsv_path="cosmic_data.tsv", limit=10)

5. Additional Tools

gget mutate - Generate Mutated Sequences

Generate mutated nucleotide sequences from mutation annotations.

Parameters:

  • sequences: FASTA file path or direct sequence input (string/list)
  • -m/--mutations: CSV/TSV file or DataFrame with mutation data (required)
  • -mc/--mut_column: Mutation column name (default: 'mutation')
  • -sic/--seq_id_column: Sequence ID column (default: 'seq_ID')
  • -mic/--mut_id_column: Mutation ID column
  • -k/--k: Length of flanking sequences (default: 30 nucleotides)

Returns: Mutated sequences in FASTA format

Examples:

bash
# Single mutation
gget mutate ATCGCTAAGCT -m "c.4G>T"

# Multiple sequences with mutations from file
gget mutate sequences.fasta -m mutations.csv -o mutated.fasta
python
# Python
import pandas as pd
mutations_df = pd.DataFrame({"seq_ID": ["seq1"], "mutation": ["c.4G>T"]})
gget.mutate(["ATCGCTAAGCT"], mutations=mutations_df)
gget gpt - OpenAI Text Generation

Generate natural language text using OpenAI's API.

Setup Required:

bash
gget setup gpt

Important: Free tier limited to 3 months after account creation. Set monthly billing limits.

Parameters:

  • prompt: Text input for generation (required)
  • api_key: OpenAI authentication (required)
  • Model configuration: temperature, top_p, max_tokens, frequency_penalty, presence_penalty
  • Default model: gpt-3.5-turbo (configurable)

Examples:

bash
gget gpt "Explain CRISPR" --api_key your_key_here
python
# Python
gget.gpt("Explain CRISPR", api_key="your_key_here")
gget setup - Install Dependencies

Install/download third-party dependencies for specific modules.

Parameters:

  • module: Module name requiring dependency installation
  • -o/--out: Output folder path (elm module only)

Modules requiring setup:

  • alphafold - Downloads ~4GB of model parameters
  • cellxgene - Installs cellxgene-census (may not support latest Python)
  • elm - Downloads local ELM database
  • gpt - Configures OpenAI integration

Examples:

bash
# Setup AlphaFold
gget setup alphafold

# Setup ELM with custom directory
gget setup elm -o /path/to/elm_data
python
# Python
gget.setup("alphafold")

Common Workflows

Workflow 1: Gene Discovery to Sequence Analysis

Find and analyze genes of interest:

python
# 1. Search for genes
results = gget.search(["GABA", "receptor"], species="homo_sapiens")

# 2. Get detailed information
gene_ids = results["ensembl_id"].tolist()
info = gget.info(gene_ids[:5])

# 3. Retrieve sequences
sequences = gget.seq(gene_ids[:5], translate=True)

Workflow 2: Sequence Alignment and Structure

Align sequences and predict structures:

python
# 1. Align multiple sequences
alignment = gget.muscle("sequences.fasta")

# 2. Find similar sequences
blast_results = gget.blast(my_sequence, database="swissprot", limit=10)

# 3. Predict structure
structure = gget.alphafold(my_sequence, plot=True)

# 4. Find linear motifs
ortholog_df, regex_df = gget.elm(my_sequence)

Workflow 3: Gene Expression and Enrichment

Analyze expression patterns and functional enrichment:

python
# 1. Get tissue expression
tissue_expr = gget.archs4("ACE2", which="tissue")

# 2. Find correlated genes
correlated = gget.archs4("ACE2", which="correlation")

# 3. Get single-cell data
adata = gget.cellxgene(gene=["ACE2"], tissue="lung", cell_type="epithelial cell")

# 4. Perform enrichment analysis
gene_list = correlated["gene_symbol"].tolist()[:50]
enrichment = gget.enrichr(gene_list, database="ontology", plot=True)

Workflow 4: Disease and Drug Analysis

Investigate disease associations and therapeutic targets:

python
# 1. Search for genes
genes = gget.search(["breast cancer"], species="homo_sapiens")

# 2. Get disease associations
diseases = gget.opentargets("ENSG00000169194", resource="diseases")

# 3. Get drug associations
drugs = gget.opentargets("ENSG00000169194", resource="drugs")

# 4. Query cancer genomics data
study_ids = gget.cbio_search(["breast"])
gget.cbio_plot(study_ids[:2], ["BRCA1", "BRCA2"], stratification="cancer_type")

# 5. Search COSMIC for mutations
cosmic_results = gget.cosmic("BRCA1", cosmic_tsv_path="cosmic.tsv")

Workflow 5: Comparative Genomics

Compare proteins across species:

python
# 1. Get orthologs
orthologs = gget.bgee("ENSG00000169194", type="orthologs")

# 2. Get sequences for comparison
human_seq = gget.seq("ENSG00000169194", translate=True)
mouse_seq = gget.seq("ENSMUSG00000026091", translate=True)

# 3. Align sequences
alignment = gget.muscle([human_seq, mouse_seq])

# 4. Compare structures
human_structure = gget.pdb("7S7U")
mouse_structure = gget.alphafold(mouse_seq)

Workflow 6: Building Reference Indices

Prepare reference data for downstream analysis (e.g., kallisto|bustools):

bash
# 1. List available species
gget ref --list_species

# 2. Download reference files
gget ref -w gtf -w cdna -d homo_sapiens

# 3. Build kallisto index
kallisto index -i transcriptome.idx transcriptome.fasta

# 4. Download genome for alignment
gget ref -w dna -d homo_sapiens

Best Practices

Data Retrieval

  • Use --limit to control result sizes for large queries
  • Save results with -o/--out for reproducibility
  • Check database versions/releases for consistency across analyses
  • Use --quiet in production scripts to reduce output

Sequence Analysis

  • For BLAST/BLAT, start with default parameters, then adjust sensitivity
  • Use gget diamond with --threads for faster local alignment
  • Save DIAMOND databases with --diamond_db for repeated queries
  • For multiple sequence alignment, use -s5/--super5 for large datasets

Expression and Disease Data

  • Gene symbols are case-sensitive in cellxgene (e.g., 'PAX7' vs 'Pax7')
  • Run gget setup before first use of alphafold, cellxgene, elm, gpt
  • For enrichment analysis, use database shortcuts for convenience
  • Cache cBioPortal data with -dd to avoid repeated downloads

Structure Prediction

  • AlphaFold multimer predictions: use -mr 20 for higher accuracy
  • Use -r flag for AMBER relaxation of final structures
  • Visualize results in Python with plot=True
  • Check PDB database first before running AlphaFold predictions

Error Handling

  • Database structures change; update gget regularly: uv pip install --upgrade gget
  • Process max ~1000 Ensembl IDs at once with gget info
  • For large-scale analyses, implement rate limiting for API queries
  • Use virtual environments to avoid dependency conflicts

Output Formats

Command-line

  • Default: JSON
  • CSV: Add -csv flag
  • FASTA: gget seq, gget mutate
  • PDB: gget pdb, gget alphafold
  • PNG: gget cbio plot

Python

  • Default: DataFrame or dictionary
  • JSON: Add json=True parameter
  • Save to file: Add save=True or specify out="filename"
  • AnnData: gget cellxgene

Resources

This skill includes reference documentation for detailed module information:

references/

  • module_reference.md - Comprehensive parameter reference for all modules
  • database_info.md - Information about queried databases and their update frequencies
  • workflows.md - Extended workflow examples and use cases

For additional help:

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Gget AI 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.

Why use Gget on TypingMind?

Because you install it once and use it with any model. Gget is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Gget in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/jimmc414/Kosmos/tree/master/kosmos-claude-scientific-skills/scientific-skills/gget. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Gget?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Gget?

As many as you like. As long as a model supports skills, you can use Gget with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Gget AI skill free?

It is published on GitHub by jimmc414. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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