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tooluniverse Use this skill when working with scientific research tools and workflows across bioinformatics, cheminformatics, genomics, structural biology, proteomics, and drug discovery. This skill provides access to 600+ scientific tools including machine learning models, datasets, APIs, and analysis packages. Use when searching for scientific tools, executing computational biology workflows, composing multi-step research pipelines, accessing databases like OpenTargets/PubChem/UniProt/PDB/ChEMBL, performing tool discovery for research tasks, or integrating scientific computational resources into LLM workflows.
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عرض تفاصيل Prompt يتجاوز الأمر المباشر Prompt المخصّص للمراجعة. افحص المصدر قبل تشغيله.
npx skills add https://github.com/jimmc414/Kosmos --skill tooluniverseيبقى الأمر في سطر واحد. مرّر أفقيًا لمراجعته كاملًا قبل النسخ.
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تحميل Zip جاري التحميل... المزيد من هذا المستودع Generate testable hypotheses. Formulate from observations, design experiments, explore competing explanations, develop predictions, propose mechanisms, for scientific inquiry across domains.
Generate testable hypotheses from observations. Use for formulating research questions, designing experiments, exploring competing explanations, developing predictions, and proposing mechanisms. Triggers: generate hypothesis, formulate hypothesis, research question, experimental design, competing explanations, mechanistic hypothesis, testable predictions, scientific inquiry.
Comprehensive citation management for academic research. Search Google Scholar and PubMed for papers, extract accurate metadata, validate citations, and generate properly formatted BibTeX entries. This skill should be used when you need to find papers, verify citation information, convert DOIs to BibTeX, or ensure reference accuracy in scientific writing.
name tooluniverse description Use this skill when working with scientific research tools and workflows across bioinformatics, cheminformatics, genomics, structural biology, proteomics, and drug discovery. This skill provides access to 600+ scientific tools including machine learning models, datasets, APIs, and analysis packages. Use when searching for scientific tools, executing computational biology workflows, composing multi-step research pipelines, accessing databases like OpenTargets/PubChem/UniProt/PDB/ChEMBL, performing tool discovery for research tasks, or integrating scientific computational resources into LLM workflows.
ToolUniverse
Overview
ToolUniverse is a unified ecosystem that enables AI agents to function as research scientists by providing standardized access to 600+ scientific resources. Use this skill to discover, execute, and compose scientific tools across multiple research domains including bioinformatics, cheminformatics, genomics, structural biology, proteomics, and drug discovery.
Key Capabilities:
Access 600+ scientific tools, models, datasets, and APIs
Discover tools using natural language, semantic search, or keywords
Execute tools through standardized AI-Tool Interaction Protocol
Compose multi-step workflows for complex research problems
Integration with Claude Desktop/Code via Model Context Protocol (MCP)
When to Use This Skill
Use this skill when:
Searching for scientific tools by function or domain (e.g., "find protein structure prediction tools")
Executing computational biology workflows (e.g., disease target identification, drug discovery, genomics analysis)
Accessing scientific databases (OpenTargets, PubChem, UniProt, PDB, ChEMBL, KEGG, etc.)
Composing multi-step research pipelines (e.g., target discovery → structure prediction → virtual screening)
Working with bioinformatics, cheminformatics, or structural biology tasks
Analyzing gene expression, protein sequences, molecular structures, or clinical data
Performing literature searches, pathway enrichment, or variant annotation
Building automated scientific research workflows
Quick Start
Basic Setup from tooluniverse import ToolUniverse
tu = ToolUniverse()
tu.load_tools()
tools = tu.run({
"name" : "Tool_Finder_Keyword" ,
"arguments" : {
"description" : "disease target associations" ,
"limit" : 10
}
})
result = tu.run({
"name" : "OpenTargets_get_associated_targets_by_disease_efoId" ,
"arguments" : {"efoId" : "EFO_0000537" }
})
Model Context Protocol (MCP) For Claude Desktop/Code integration:
Core Workflows
1. Tool Discovery Find relevant tools for your research task:
Tool_Finder - Embedding-based semantic search (requires GPU)Tool_Finder_LLM - LLM-based semantic search (no GPU required)Tool_Finder_Keyword - Fast keyword search
tools = tu.run({
"name" : "Tool_Finder_LLM" ,
"arguments" : {
"description" : "Find tools for RNA sequencing differential expression analysis" ,
"limit" : 10
}
})
for tool in tools:
print (f"{tool['name' ]} : {tool['description' ]} " )
See references/tool-discovery.md for:
Detailed discovery methods and search strategies
Domain-specific keyword suggestions
Best practices for finding tools
2. Tool Execution Execute individual tools through the standardized interface:
targets = tu.run({
"name" : "OpenTargets_get_associated_targets_by_disease_efoId" ,
"arguments" : {"efoId" : "EFO_0000616" }
})
structure = tu.run({
"name" : "AlphaFold_get_structure" ,
"arguments" : {"uniprot_id" : "P12345" }
})
properties = tu.run({
"name" : "RDKit_calculate_descriptors" ,
"arguments" : {"smiles" : "CCO" }
})
See references/tool-execution.md for:
Real-world execution examples across domains
Tool parameter handling and validation
Result processing and error handling
Best practices for production use
3. Tool Composition and Workflows Compose multiple tools for complex research workflows:
targets = tu.run({
"name" : "OpenTargets_get_associated_targets_by_disease_efoId" ,
"arguments" : {"efoId" : "EFO_0000616" }
})
structures = []
for target in targets[:5 ]:
structure = tu.run({
"name" : "AlphaFold_get_structure" ,
"arguments" : {"uniprot_id" : target['uniprot_id' ]}
})
structures.append(structure)
hits = []
for structure in structures:
compounds = tu.run({
"name" : "ZINC_virtual_screening" ,
"arguments" : {
"structure" : structure,
"library" : "lead-like" ,
"top_n" : 100
}
})
hits.extend(compounds)
drug_candidates = []
for compound in hits:
props = tu.run({
"name" : "RDKit_calculate_drug_properties" ,
"arguments" : {"smiles" : compound['smiles' ]}
})
if props['lipinski_pass' ]:
drug_candidates.append(compound)
See references/tool-composition.md for:
Complete workflow examples (drug discovery, genomics, clinical)
Sequential and parallel tool composition patterns
Output processing hooks
Workflow best practices
Scientific Domains ToolUniverse supports 600+ tools across major scientific domains:
Sequence analysis, alignment, BLAST
Gene expression (RNA-seq, DESeq2)
Pathway enrichment (KEGG, Reactome, GO)
Variant annotation (VEP, ClinVar)
Molecular descriptors and fingerprints
Drug discovery and virtual screening
ADMET prediction and drug-likeness
Chemical databases (PubChem, ChEMBL, ZINC)
Protein structure prediction (AlphaFold)
Structure retrieval (PDB)
Binding site detection
Protein-protein interactions
Mass spectrometry analysis
Protein databases (UniProt, STRING)
Post-translational modifications
Genome assembly and annotation
Copy number variation
Clinical genomics workflows
Disease databases (OpenTargets, OMIM)
Clinical trials and FDA data
Variant classification
See references/domains.md for:
Complete domain categorization
Tool examples by discipline
Cross-domain applications
Search strategies by domain
Reference Documentation This skill includes comprehensive reference files that provide detailed information for specific aspects:
references/installation.mdreferences/tool-discovery.mdreferences/tool-execution.mdreferences/tool-composition.mdreferences/domains.mdreferences/api_reference.md Workflow: When helping with specific tasks, reference the appropriate file for detailed instructions. For example, if searching for tools, consult references/tool-discovery.md for search strategies.
Example Scripts Two executable example scripts demonstrate common use cases:
scripts/example_tool_search.py
Keyword-based search
LLM-based search
Domain-specific searches
Getting detailed tool information
scripts/example_workflow.py
Drug discovery pipeline (disease → targets → structures → screening → candidates)
Genomics analysis (expression data → differential analysis → pathways)
Run examples to understand typical usage patterns and workflow composition.
Best Practices
Tool Discovery:
Start with broad searches, then refine based on results
Use Tool_Finder_Keyword for fast searches with known terms
Use Tool_Finder_LLM for complex semantic queries
Set appropriate limit parameter (default: 10)
Tool Execution:
Always verify tool parameters before execution
Implement error handling for production workflows
Validate input data formats (SMILES, UniProt IDs, gene symbols)
Check result types and structures
Workflow Composition:
Test each step individually before composing full workflows
Implement checkpointing for long workflows
Consider rate limits for remote APIs
Use parallel execution when tools are independent
Integration:
Initialize ToolUniverse once and reuse the instance
Call load_tools() once at startup
Cache frequently used tool information
Enable logging for debugging
Key Terminology
Tool : A scientific resource (model, dataset, API, package) accessible through ToolUniverseTool Discovery : Finding relevant tools using search methods (Finder, LLM, Keyword)Tool Execution : Running a tool with specific arguments via tu.run()Tool Composition : Chaining multiple tools for multi-step workflowsMCP : Model Context Protocol for integration with Claude Desktop/CodeAI-Tool Interaction Protocol : Standardized interface for LLM-tool communication
Resources 