tooluniverse-target-research
This skill assembles comprehensive target intelligence by systematically exploring genetic associations, tissue expression patterns, pathway involvement, protein interactions, variant landscapes, and druggability indicators. It guides you through evidence-graded analysis across nine parallel research streams, producing detailed target characterization reports suitable for drug discovery target validation and competitive landscape assessment.
Tooluniverse-target-research generates full drug-target intelligence reports combining genetic, structural, and druggability evidence.
AI-generated summary based on this skill's SKILL.md
Install
mims-harvard/ToolUniverse/tooluniverse-target-research · repository language: Python
git clone https://github.com/mims-harvard/ToolUniverse
cp -r ToolUniverse/skills/tooluniverse-target-research ~/.claude/skills/tooluniverse-target-researchnpx skillfed install mims-harvard/ToolUniverse/tooluniverse-target-researchFrequently asked questions
AI-generated answers based on this skill's SKILL.md and metadata
What does tooluniverse-target-research do?
tooluniverse-target-research assembles comprehensive target intelligence by systematically exploring genetic associations, tissue expression patterns, pathway involvement, protein interactions, variant landscapes, and druggability indicators. It guides you through evidence-graded analysis across nine parallel research streams, producing detailed target characterization reports suitable for drug discovery target validation and competitive landscape assessment.
Can tooluniverse-target-research tell me about EGFR target?
Yes. tooluniverse-target-research generates comprehensive target intelligence reports for specific proteins like EGFR, including genetic evidence from GWAS and rare variants, structural druggability assessment, tissue expression and safety profiling, protein interactions, variant landscapes, and competitive landscape mapping of existing drugs or clinical programs targeting that protein.
Is this target druggable according to tooluniverse-target-research?
tooluniverse-target-research assesses target druggability by identifying binding pockets for small molecule or biologic engagement, evaluating structural features, and analyzing existing approved drugs or clinical-stage programs. The skill produces druggability scores and binding site predictions to support GO/NO-GO recommendations for drug discovery programs.
What safety profile information does tooluniverse-target-research provide?
tooluniverse-target-research evaluates safety profiles by assessing target essentiality, tissue expression patterns, on-target and off-target toxicity risks, and variant clinical significance. This enables identification of potential safety liabilities early in target validation, supporting risk stratification for drug discovery programs.
How does tooluniverse-target-research analyze genetic evidence?
tooluniverse-target-research links targets to disease through GWAS associations, rare variant analysis, and functional data integration. It grades genetic evidence strength and maps variant landscapes to support target validation decisions in drug discovery.
What competitive landscape information does tooluniverse-target-research map?
tooluniverse-target-research identifies existing approved drugs and clinical-stage programs targeting your protein of interest, enabling competitive landscape assessment and differentiation opportunities for new drug discovery programs.
SKILL.md
rendered from the published skill — quoted content, verbatim
Comprehensive Target Intelligence Gatherer
Gather complete target intelligence by exploring 9 parallel research paths. Supports targets identified by gene symbol, UniProt accession, Ensembl ID, or gene name.
KEY PRINCIPLES:
1. Report-first approach - Create report file FIRST, then populate progressively
2. Tool parameter verification - Verify params via get_tool_info before calling unfamiliar tools
3. Evidence grading - Grade all claims by evidence strength (T1-T4)
4. Citation requirements - Every fact must have inline source attribution
5. Mandatory completeness - All sections must exist with data minimums or explicit "No data" notes
6. Disambiguation first - Resolve all identifiers before research
7.
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skills/tooluniverse-target-research/EVIDENCE_GRADING.md
skills/tooluniverse-target-research/EXAMPLES.md
skills/tooluniverse-target-research/IMPLEMENTATION.md
skills/tooluniverse-target-research/REFERENCE.md
skills/tooluniverse-target-research/REPORT_FORMAT.md
skills/tooluniverse-target-research/SKILL.md