Agent skill

gget

快速CLI/Python查询20+生物信息学数据库。用于快速查找：基因信息、BLAST搜索、AlphaFold结构、富集分析。最适合交互式探索、简单查询。批处理或高级BLAST请使用biopython；多数据库Python工作流请使用bioservices。

majiayu000github.com/majiayu000GitHub ↗
claude-codeMIT
Install
npx skills add majiayu000/claude-skill-registry --skill gget-victory-hugo-s2-agent-skill --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 2
SKILL.md size: 25 KB
Bundled scripts: none
Path: skills/analysis/gget-victory-hugo-s2-agent-skill/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 534
Language: HTML

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

Review
written from the skill's own SKILL.md · Aug 5, 2026

What it does

gget enables querying a wide range of bioinformatics resources via a single interface. It supports command-line usage and Python functions, allowing users to fetch gene information, perform sequence retrieval, run BLAST/BLAT searches, access protein structures, run enrichment analyses, and more across multiple databases. Outputs flow as JSON or CSV for the CLI and as DataFrames or dictionaries in Python.

How it works

  • The tool is a command-line interface and Python package where each module corresponds to a biological task (e.g., ref, search, info, seq, blast, blat, muscle, diamond, pdb, alphafold, elm, archs4, cellxgene, enrichr, bgee, opentargets, cbio, bgee, opentargets, cbio).
  • For a given module, the user runs a command like: gget <module> [arguments] [options] or calls the Python function gget.<module>(...).
  • Modules return structured results: CLI typically JSON (default) or CSV with -csv; Python returns a DataFrame or dictionary.
  • Common options across modules include -o/--out to save results, -q/--quiet to suppress progress, and -csv to request CSV output (CLI).
  • The tool emphasizes that databases are updated over time; modules are tested automatically biweekly and updated to match changes.

When to use it

  • When you need quick, interactive exploration or simple queries across 20+ bioinformatics databases.
  • When you want a unified interface for tasks like gene lookup (ref, search, info), sequence retrieval (seq), BLAST/BLAT searches (blast, blat), structure prediction (alphafold), motif prediction (elm), or enrichment and disease associations (enrichr, opentargets, cbio, archs4, etc.).

What it can touch

  • Commands and Python functions named after modules (e.g., gget.ref, gget.search, gget.info, gget.blast, gget.alphafold, gget.enrichr, gget.cellxgene, etc.).
  • Outputs are saved or returned in standard formats; specifics depend on module (JSON/CSV for CLI; DataFrame/dict for Python).

Caveats

  • License is BSD-2-Clause; deployment should respect license terms.
  • Databases queried are continuously updated, which can change query results or metadata structure; users may need to rerun with updated modules after updates.
From the SKILL.md

# 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 - `-

What's inside
Steps it walks through
  1. Overview
  2. Installation
  3. Quick Start
  4. Module Categories
  5. 1. Reference & Gene Information
  6. 2. Sequence Analysis & Alignment
  7. 3. Structural & Protein Analysis
  8. 4. Expression & Disease Data
  9. 5. Additional Tools
  10. Common Workflows
  11. Workflow 1: Gene Discovery to Sequence Analysis
  12. Workflow 2: Sequence Alignment and Structure
  13. Workflow 3: Gene Expression and Enrichment
  14. Workflow 4: Disease and Drug Analysis
Ships with 1 file
  • metadata.json
Commands it runs
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
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About this skill
What does the gget skill do?

快速CLI/Python查询20+生物信息学数据库。用于快速查找：基因信息、BLAST搜索、AlphaFold结构、富集分析。最适合交互式探索、简单查询。批处理或高级BLAST请使用biopython；多数据库Python工作流请使用bioservices。

How do I install it?

Run `npx skills add majiayu000/claude-skill-registry --skill gget-victory-hugo-s2-agent-skill --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 majiayu000/claude-skill-registry, a repository with 534 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.

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