tooluniverse — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited tooluniverse (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
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:
Use this skill when:
from tooluniverse import ToolUniverse
# Initialize and load tools
tu = ToolUniverse()
tu.load_tools() # Loads 600+ scientific tools
# Discover tools
tools = tu.run({
"name": "Tool_Finder_Keyword",
"arguments": {
"description": "disease target associations",
"limit": 10
}
})
# Execute a tool
result = tu.run({
"name": "OpenTargets_get_associated_targets_by_disease_efoId",
"arguments": {"efoId": "EFO_0000537"} # Hypertension
})For Claude Desktop/Code integration:
tooluniverse-smcpFind relevant tools for your research task:
Three discovery methods:
Tool_Finder - Embedding-based semantic search (requires GPU)Tool_Finder_LLM - LLM-based semantic search (no GPU required)Tool_Finder_Keyword - Fast keyword searchExample:
# Search by natural language description
tools = tu.run({
"name": "Tool_Finder_LLM",
"arguments": {
"description": "Find tools for RNA sequencing differential expression analysis",
"limit": 10
}
})
# Review available tools
for tool in tools:
print(f"{tool['name']}: {tool['description']}")See `references/tool-discovery.md` for:
Execute individual tools through the standardized interface:
Example:
# Execute disease-target lookup
targets = tu.run({
"name": "OpenTargets_get_associated_targets_by_disease_efoId",
"arguments": {"efoId": "EFO_0000616"} # Breast cancer
})
# Get protein structure
structure = tu.run({
"name": "AlphaFold_get_structure",
"arguments": {"uniprot_id": "P12345"}
})
# Calculate molecular properties
properties = tu.run({
"name": "RDKit_calculate_descriptors",
"arguments": {"smiles": "CCO"} # Ethanol
})See `references/tool-execution.md` for:
Compose multiple tools for complex research workflows:
Drug Discovery Example:
# 1. Find disease targets
targets = tu.run({
"name": "OpenTargets_get_associated_targets_by_disease_efoId",
"arguments": {"efoId": "EFO_0000616"}
})
# 2. Get protein structures
structures = []
for target in targets[:5]:
structure = tu.run({
"name": "AlphaFold_get_structure",
"arguments": {"uniprot_id": target['uniprot_id']}
})
structures.append(structure)
# 3. Screen compounds
hits = []
for structure in structures:
compounds = tu.run({
"name": "ZINC_virtual_screening",
"arguments": {
"structure": structure,
"library": "lead-like",
"top_n": 100
}
})
hits.extend(compounds)
# 4. Evaluate drug-likeness
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:
ToolUniverse supports 600+ tools across major scientific domains:
Bioinformatics:
Cheminformatics:
Structural Biology:
Proteomics:
Genomics:
Medical/Clinical:
See `references/domains.md` for:
This skill includes comprehensive reference files that provide detailed information for specific aspects:
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.
Two executable example scripts demonstrate common use cases:
`scripts/example_tool_search.py` - Demonstrates all three discovery methods:
`scripts/example_workflow.py` - Complete workflow examples:
Run examples to understand typical usage patterns and workflow composition.
Tool_Finder_Keyword for fast searches with known termsTool_Finder_LLM for complex semantic querieslimit parameter (default: 10)load_tools() once at startuptu.run()uv pip install tooluniversetooluniverse-smcp~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.