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mims-harvard
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tooluniverse-sdk

Access 1000++ scientific tools programmatically via the ToolUniverse Python SDK.

1.6k|244|Updated Mar 3, 2025
Search Tags:#automation#python#sdk#workflows#drug-discovery#tooluniverse#protein-analysis
One-click install
npx skills add https://github.com/mims-harvard/ToolUniverse --skill tooluniverse-sdk
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: tooluniverse-sdk
Source: https://github.com/mims-harvard/ToolUniverse/tree/main/skills/tooluniverse-sdk
Command: npx skills add https://github.com/mims-harvard/ToolUniverse --skill tooluniverse-sdk

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill enables AI agents to access 1000++ scientific tools programmatically via the ToolUniverse Python SDK, enabling automated research workflows.

Core Features & Use Cases

  • Discovery, execution, and workflow composition of tools within Python pipelines.
  • Seamless integration for drug discovery, protein analysis, genomics, and literature research.
  • Example: Build a short pipeline that loads tools, discovers candidates for a target, and executes a sequence of analyses.

Quick Start

Install the ToolUniverse package, initialize ToolUniverse, and demonstrate a sample workflow by loading tools and running a simple entry retrieval.

Frequently Asked Questions about tooluniverse-sdk

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I automate scientific workflows for drug discovery and protein analysis in Python?▼

You can automate scientific workflows by using the ToolUniverse Python SDK to access 1000+ tools programmatically. It provides patterns for tool discovery, execution modes, batch execution, and caching to build research pipelines.

What is the best way to discover scientific tools programmatically for genomics research?▼

The best way to discover scientific tools for genomics research is using the ToolUniverse Python SDK, which supports keyword, LLM, and embedding discovery patterns. This allows AI agents to find relevant tools across multiple scientific domains.

Do I need an OpenAI API key to run automated research pipelines with this Python SDK?▼

You need an OpenAI API key specifically for LLM-driven discovery within your research pipelines. Standard Python packaging and environment configuration are required, while the API key enables the LLM-based tool discovery pattern.

Can I execute batch analyses and cache results when building biology and chemistry pipelines?▼

Yes, you can execute batch analyses and cache results when building biology and chemistry pipelines. The SDK provides two execution modes alongside batch execution and caching capabilities for automated research workflows.

How does workflow composition work for literature research and drug discovery tasks?▼

Workflow composition works by loading tools, discovering candidates for a target, and executing a sequence of analyses within Python pipelines. This allows AI agents to seamlessly integrate literature research with drug discovery and genomics tasks.

Are there limitations when integrating automated pipelines with other software engineering tools?▼

Limitations depend on standard Python packaging compatibility and environment configuration within your software engineering setup. The SDK targets researchers and developers building automated pipelines, requiring proper environment setup for seamless workflow integration.

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