Skills

Quickgo Database

Query the QuickGO and Evidence & Conclusion Ontology (ECO) REST API.

Install

npx skills add google-deepmind/science-skills --skill quickgo-database -g -a claude-code -y
Category
Engineering
Updated
12d ago
License
Apache-2.0
Context cost
Lightabout 1,306 tokens
Runs scripts
Yes, review before use

What it does

Query the QuickGO and Evidence & Conclusion Ontology (ECO) REST API. Use this when you need to map genes to biological processes, molecular functions, or cellular components, find genes associated with a specific pathway/GO term, or explore the Gene Ontology hierarchy. Do not use for querying drug targets (use OpenTargets) or mechanistic signaling pathway diagrams (use KEGG).

QuickGO Database Skill

GO (Gene Ontology) annotations are one of the main ways to label a gene's function. QuickGO is a fast, web-based browser for the GO and Evidence & Conclusion Ontology (ECO), maintained by the Gene Ontology Annotation (GOA) group at EMBL-EBI.

It provides a centralised resource to explore the functional attributes of gene products (proteins, RNA, and complexes). It is a primary tool for functional annotation mapping since it allows you to link a gene (e.g., USH2A) to its specific biological processes (e.g. sensory perception of light stimulus), molecular functions, and cellular components.

Prerequisites

  1. uv: Read the uv skill and follow its Setup instructions to ensure uv is installed and on PATH.
  2. User Notification: If .licenses/quickgo_database_LICENSE.txt does not already exist in the workspace root directory then (1) prominently notify the user to check the terms at https://www.ebi.ac.uk/QuickGO/ and https://www.ebi.ac.uk/QuickGO/api/index.html, then (2) create the file recording the notification text and timestamp.

Usage

This skill provides a Python CLI wrapper scripts/quickgo_tool.py that queries the QuickGO REST API. It handles formatting the requests, respecting rate limits, and safely storing the potentially large JSON responses.

Core Rules

Read the whole SKILL.md on GitHub

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