A comprehensive bioinformatics tool for querying the Kyoto Encyclopedia of Genes and Genomes to analyze drugs, pathways, and disease targets.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install kegg-query
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install kegg-query using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is KEGG Query?
KEGG Query is a specialized technical integration designed for the Openclaw Skills ecosystem, enabling AI agents to programmatically interface with the Kyoto Encyclopedia of Genes and Genomes (KEGG). It allows developers and researchers to navigate the complex landscape of biological systems, from molecular-level genomic data to high-level systemic functions. By leveraging this skill, users can automate the retrieval of high-fidelity data regarding metabolic pathways, chemical compounds, and therapeutic drugs.
This tool is essential for modern bioinformatics workflows, providing structured access to drug-drug interactions, enzyme annotations, and disease-associated gene sets. Whether you are building a drug discovery pipeline or a research assistant, KEGG Query streamlines the data acquisition process from one of the world's most trusted biological databases.
KEGG Query Use Cases
- Comprehensive drug information lookup including efficacy, metabolism, and targets
- Detailed pathway analysis to map genes, compounds, and signaling modules
- Discovery of disease-associated drugs and therapeutic targets for repurposing research
- Automated biological data enrichment for large-scale genomic datasets
How KEGG Query Works
- The user or agent performs a keyword search using the find operation to identify relevant entry IDs within specific databases like drug, pathway, or disease.
- The skill executes a get request to the KEGG REST API to fetch the full text-based entry for the specified ID.
- A specialized parser processes the raw KEGG response into a structured format, extracting critical metadata such as UniProt accessions, KO numbers, and chemical formulas.
- The processed data is returned as a clean JSON object, ready for integration into downstream bioinformatics models or protein analysis tools.
KEGG Query Setup
To integrate this functionality into your environment, ensure you have the core KEGG API scripts available. This skill utilizes the standard KEGG REST API which does not require an API key for academic use.
# Ensure your environment can make outbound HTTP requests to kegg.jp
# No specialized installation is required beyond standard Python requests libraries
KEGG Query Data Schema & Taxonomy
The skill organizes biological data into three primary schemas to ensure clarity within the Openclaw Skills framework:
| Schema | Primary Fields |
|---|---|
| Drug Entry | ID, Names, Formula, Efficacy, Gene Targets, Pathways, DDI |
| Pathway Entry | ID, Name, Organism, Genes (KO/EC), Compounds, Modules |
| Disease Entry | ID, Name, Category, Associated Genes, Approved Drugs, Pathways |
All entries include cross-reference IDs to external databases like UniProt, NCBI-GeneID, and PubChem for extended research capabilities.
KEGG Query Advanced Features
- Multi-database cross-referencing using the link and conv operations for ID mapping
- Support for organism-specific pathway retrieval (e.g., hsa for humans) vs. reference maps
- Capability to retrieve chemical structure data in MOL format for molecular modeling
- Integration-ready outputs for OpenBioMed tools to convert KEGG IDs into Protein or Molecule objects
- Built-in error handling for rate-limiting and ID verification to ensure stable long-running queries
SKILL.md
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