gget
Fast CLI/Python queries to 20+ bioinformatics databases. Use for quick lookups: gene info, BLAST searches, AlphaFold structures, enrichment analysis. Best for interactive exploration, simple queries. For batch processing or advanced BLAST use biopython; for multi-database Python workflows use bioservices.
npx skills add LeonChaoX/qinyan-academic-skills --skill gget --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
What it does
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.
How it works
Install instructions show usage patterns:
- Command-line: "gget <module> [arguments] [options]"
- Python: "gget.module(arguments, options)" Most modules return JSON (default) or CSV with -csv for command-line, and DataFrame or dictionary in Python. Common flags across modules include -o/--out, -q/--quiet, and -csv for command-line.
Module examples outline functions such as:
- gget ref with parameters like species, -w/--which, -r/--release, -l/--list_species, -ftp, -d/--download
- gget search with searchwords, -s/--species, -r/--release, -t/--id_type, -ao/--andor, -l/--limit
- gget info with ens_ids, -n/--ncbi, -u/--uniprot, -pdb
- gget seq with ens_ids, -t/--translate, -iso/--isoforms
- gget blast with sequence, -p/--program, -db/--database, -l/--limit, -e/--expect, -lcf/--low_comp_filt, -mbo/--megablast_off
- gget blat with sequence, -st/--seqtype, -a/--assembly
- gget muscle with fasta, -s5/--super5
- gget diamond with query and --reference, --sensitivity, --threads, --diamond_db, --translated
- gget pdb with pdb_id, -r/--resource, -i/--identifier
- gget alphafold with sequence, -mr/--multimer_recycles, -mfm/--multimer_for_monomer, -r/--relax, plot, show_sidechains
- gget elm with sequence, -u/--uniprot, -e/--expand, -s/--sensitivity, -t/--threads
- gget archs4, gget cellxgene, gget enrichr, gget bgee, gget opentargets, gget cbio, gget coio (presumably typo) and more
When to use it
Not specified as triggers beyond general descriptions; intended for quick lookups and interactive exploration, or simple queries across many databases. For batch processing or advanced BLAST, it recommends biopython; for multi-database Python workflows, bioservices.
What it can touch
Declared tools: claude-code The content describes commands, modules, parameters, and outputs for various functions. It indicates that modules can run as CLI tools and Python functions, with options to save outputs (-o/--out) and to return JSON, CSV, DataFrame, or dictionaries. No external touch points beyond standard CLI/Python interfaces are specified here.
Caveats
Databases are continuously updated, which can change structure; gget modules are tested biweekly and updated to match database structures when necessary. No licensing conflicts stated beyond license: BSD-2-Clause license in metadata. The overview notes that for certain tasks (batch BLAST or multi-database workflows) other tools are recommended.
# 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 - `-
- Overview
- Installation
- Quick Start
- Module Categories
- 1. Reference & Gene Information
- 2. Sequence Analysis & Alignment
- 3. Structural & Protein Analysis
- 4. Expression & Disease Data
- 5. Additional Tools
- Common Workflows
- Workflow 1: Gene Discovery to Sequence Analysis
- Workflow 2: Sequence Alignment and Structure
- Workflow 3: Gene Expression and Enrichment
- Workflow 4: Disease and Drug Analysis
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
What does the gget skill do?
Fast CLI/Python queries to 20+ bioinformatics databases. Use for quick lookups: gene info, BLAST searches, AlphaFold structures, enrichment analysis. Best for interactive exploration, simple queries. For batch processing or advanced BLAST use biopython; for multi-database Python workflows use bioservices.
How do I install it?
Run `npx skills add LeonChaoX/qinyan-academic-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 LeonChaoX/qinyan-academic-skills, a repository with 759 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.