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Agent skill

Gget

by aipoch in aipoch/medical-research-skills

Unified CLI/Python interface for querying genomic, proteomic, structure, and expression data across 20+ bioinformatics databases; use when you need fast, scriptable retrieval by gene/protein IDs or…

MITAuto-check passedResearch & Science

Install Gget

skills CLI
$ npx skills add aipoch/medical-research-skills --skill gget -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install aipoch/medical-research-skills gget --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/aipoch/medical-research-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/'scientific-skills/Evidence Insight/gget' .claude/skills/gget && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
gget
GitHub stars
2k
Token cost
~816 tokens
SKILL.md length
291 words
Files
4 (incl. scripts, references)
Skills in repo
567
Repo updated
First seen
Licence
MIT

At a glance

Unified CLI/Python interface for querying genomic, proteomic, structure, and expression data across 20+ bioinformatics databases; use when you need fast, scriptable retrieval by gene/protein IDs or…

  • Works in 4 steps: Search for genes by keyword → Retrieve gene information by Ensembl ID → Fetch sequences by Ensembl ID → …
  • Scriptable retrieval by gene/protein IDs
  • SKILL.md covers When to Use, Key Features, Dependencies and Example Usage, plus 1 more section
  • Runs Python scripts from its folder; calls python and uv

What it does

Gget is an agent skill from aipoch/medical-research-skills. Unified CLI/Python interface for querying genomic, proteomic, structure, and expression data across 20+ bioinformatics databases; use when you need fast, scriptable retrieval by gene/protein IDs or keywords.

Its SKILL.md is about 820 tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including scripts and reference files (for example `gget_audit_result_v1.json`, `references/module_reference.md` and `scripts/wrapper.py`).

It sits in Research & Science, covering Bioinformatics and Protein structure and design. It works with Python, Ensembl, AlphaFold and NCBI. The repository describes itself as: Hundreds of agent skills for medical research, including protocol design, data analysis, evidence insights, and academic writing. The licence is MIT.

When your agent uses it

  • Scriptable retrieval by gene/protein IDs
  • Tasks that involve Bioinformatics
  • Tasks that involve Protein structure and design

Example prompts

  • “/gget”

Requirements

  • Python 3

Workflow steps

4 steps, taken from the step headings in SKILL.md.

  1. Search for genes by keyword
  2. Retrieve gene information by Ensembl ID
  3. Fetch sequences by Ensembl ID
  4. Predict protein structure with AlphaFold (optional plotting)

What it can do on your machine

Read from SKILL.md and the folder at commit 686e09d. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 1 file in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python
    • uv

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use uv, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Gget loads about 816 tokens when it runs, and up to ~1.2k if it reads all its reference files. Until then it costs about 53 tokens; SKILL.md has 291 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~53
When it runs · the whole SKILL.md, loaded when a task matches
~816
With references · SKILL.md plus every file in references/, read only if the agent opens them
~1.2k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from aipoch/medical-research-skills at commit 686e09d, republished under its MIT licence (© aipoch). 291 words, ~816 tokens.

Download SKILL.mdSave it as .claude/skills/gget/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
gget
description
Unified CLI/Python interface for querying genomic, proteomic, structure, and expression data across 20+ bioinformatics databases; use when you need fast, scriptable retrieval by gene/protein IDs or keywords.
license
MIT
author
AIPOCH

Source: https://github.com/aipoch/medical-research-skills

When to Use

  • You need to search genes/proteins by keyword and species across common databases (e.g., Ensembl/UniProt/NCBI).
  • You want to fetch detailed metadata for one or many Ensembl/UniProt/NCBI identifiers.
  • You need to retrieve nucleotide/protein sequences for downstream analysis or pipelines.
  • You want to obtain or predict protein structures (PDB download or AlphaFold prediction) from a sequence.
  • You need to query expression resources (e.g., ARCHS4, CELLxGENE, Bgee) or run enrichment analysis (Enrichr).

Key Features

  • Unified wrapper (scripts/wrapper.py) exposing multiple gget subcommands through a consistent interface.
  • Gene/protein search and identifier resolution across multiple databases.
  • Rich gene/protein information retrieval (annotations and metadata).
  • Sequence retrieval for provided IDs.
  • Structure workflows: PDB retrieval and AlphaFold-based prediction (optional plotting).
  • Expression querying across popular expression atlases.
  • Enrichment analysis via Enrichr.
  • Backed by the upstream gget Python library.

Dependencies

  • Python 3.9+ (recommended)
  • gget (latest compatible version)
  • pandas (latest compatible version)

Install:

bash
uv pip install gget pandas

Example Usage

The skill is accessed via the unified wrapper script:

1) Search for genes by keyword
bash
python scripts/wrapper.py search --keywords "insulin" --species "human"
2) Retrieve gene information by Ensembl ID
bash
python scripts/wrapper.py info --ids "ENSG00000034713"
3) Fetch sequences by Ensembl ID
bash
python scripts/wrapper.py seq --ids "ENSG00000034713"
4) Predict protein structure with AlphaFold (optional plotting)
bash
python scripts/wrapper.py alphafold --sequence "MKWMFK..." --plot

Implementation Details

  • Wrapper entrypoint: scripts/wrapper.py acts as a dispatcher that maps subcommands (e.g., search, info, seq, alphafold) to the corresponding gget library functions, normalizing CLI arguments and output behavior.
  • Supported modules/functions:
    • ref: Download reference genomes/annotations.
    • search: Keyword-based gene/protein lookup (Ensembl/UniProt/NCBI).
    • info: Detailed gene/protein metadata retrieval for one or multiple IDs.
    • seq: Nucleotide/protein sequence retrieval for provided IDs.
    • structure: Structure retrieval (PDB) and AlphaFold prediction.
    • expression: Expression queries (ARCHS4, CELLxGENE, Bgee).
    • enrichment: Enrichr-based enrichment analysis.
  • Notes on AlphaFold: The alphafold subcommand requires additional setup depending on the environment (e.g., model/data availability). Use --plot to request visualization output when supported.
  • Further reference: See references/module_reference.md for detailed module-level documentation and parameters.

© aipoch, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 3 other files (scripts, references) in scientific-skills/Evidence Insight/gget of aipoch/medical-research-skills.

  • SKILL.md
  • gget_audit_result_v1.json
  • references/module_reference.md
  • scripts/wrapper.py

Open the folder on GitHubat commit 686e09d

Compare with similar skills

Gget next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Gget compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Gget this skillaipoch/medical-research-skills2k—~816Automated safety check: PassMIT
Ggetdavila7/claude-code-templates32k11 repos~6.3kAutomated safety check: PassMIT
Database Lookupmajiayu000/claude-skill-registry6661 repos~7kAutomated safety check: NotesMIT
Uniprot Protein Databasejaechang-hits/SciAgent-Skills3701 repos~3.4kAutomated safety check: PassCC-BY-4.0
Bio DB ToolsDrugClaw/DrugClaw125—~1.4kAutomated safety check: PassApache-2.0
Biopythonlamm-mit/scienceclaw244—~3.9kAutomated safety check: PassApache-2.0

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Works with

Categories

Questions about Gget

What does Gget do?

Unified CLI/Python interface for querying genomic, proteomic, structure, and expression data across 20+ bioinformatics databases; use when you need fast, scriptable retrieval by gene/protein IDs or…. Gget is an agent skill from aipoch/medical-research-skills. Unified CLI/Python interface for querying genomic, proteomic, structure, and expression data across 20+ bioinformatics databases; use when you need fast, scriptable retrieval by gene/protein IDs or keywords.

When should I use Gget?

Gget fits situations like: scriptable retrieval by gene/protein IDs; tasks that involve Bioinformatics; tasks that involve Protein structure and design.

How do I install Gget in Claude Code?

Run `npx skills add aipoch/medical-research-skills --skill gget -a claude-code`. Or copy the skill folder (scientific-skills/Evidence Insight/gget in aipoch/medical-research-skills) into .claude/skills/gget in your project. Claude Code loads it when a task matches its description.

How do I install Gget in Codex?

Run `npx skills add aipoch/medical-research-skills --skill gget -a codex`. Or copy the skill folder (scientific-skills/Evidence Insight/gget in aipoch/medical-research-skills) into .agents/skills/gget in your project. Codex loads it when a task matches its description.

Can I use Gget in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add aipoch/medical-research-skills --skill gget -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/gget, .gemini/skills/gget, .github/skills/gget and .opencode/skills/gget in your project.

What does Gget need to run?

Going by SKILL.md and its folder, Gget needs Python for the scripts in its folder and the command-line tools its instructions call (python and uv). Our summary lists: Python 3.

Does Gget access the network?

SKILL.md contains no URLs. Its commands use uv, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Gget safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Gget use?

Gget is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Gget use?

About 816 tokens (SKILL.md is roughly 3.3k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 400 tokens, read only when the agent opens those files.

What are the alternatives to Gget?

Skills that share tags, products or a category with Gget: Gget (davila7/claude-code-templates, 32k stars), Database Lookup (majiayu000/claude-skill-registry, 666 stars), Uniprot Protein Database (jaechang-hits/SciAgent-Skills, 370 stars) and Bio DB Tools (DrugClaw/DrugClaw, 125 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Gget?

aipoch (a GitHub organization) maintains it in aipoch/medical-research-skills, which has 1,974 GitHub stars. The repository holds 567 skills in this directory. The repository was last updated on September 17, 2026.

Source: aipoch/medical-research-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.