Query drug-target relationships from a local TarKG TSV dataset.
npx skills add https://github.com/QSong-github/DrugClaw --skill tarkg
Please help me install this Agent Skill. Skill: TarKG Source: https://github.com/QSong-github/DrugClaw/tree/main/skills/dti/tarkg Command: npx skills add https://github.com/QSong-github/DrugClaw --skill tarkg
TarKG enables querying a canonical drug-target knowledge graph to reveal relationships between drugs and their protein targets, with contextual pathways and disease associations to support analysis and interpretation.
Configure the TarKGSkill with the TSV path and run a query for a drug to discover its targets and context.
High-intent search queries and answers about installing and using this skill.
To query drug-target relationships, configure the retrieval interface with your local TSV path to extract and explore drug, target, and relation triplets. This enables lightweight pharmacology reasoning and hypothesis generation directly from your structured dataset.
Your drug-target knowledge graph dataset must be a local TSV or CSV file containing columns for drug, target, and relation, with optional disease and pathway columns. This structured format supports pathway-informed interpretation and mechanism analysis.
Yes, you can find disease and pathway context alongside drug-target interactions in a single lookup. The retrieval interface returns relation labels, associated diseases, and contextual pathways to support safety assessment and mechanism analysis.
The best way to generate quick drug discovery hypotheses is to run targeted queries for specific drugs or protein targets against your local knowledge graph. This retrieval process rapidly maps contextual pathways and disease associations for interpretation.
No external dependencies or APIs are required to explore drug-target triplets. The skill performs local, lightweight reasoning over a structured TarKG TSV dataset loaded directly from your configured file path.
You should use a local TSV triplet file for drug-target retrieval when you need lightweight, immediate reasoning without external database setup. It is ideal for rapid lookups, mechanism analysis, and pathway-informed interpretation in isolated environments.
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