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tooluniverse-[domain-name]

Aggregate domain queries across biomedical databases into phased Markdown reports.

Updated Apr 18, 2026
Search Tags:#analysis#reporting#pipeline#multi-database#biomedical#tooluniverse#python-sdk
One-click install
npx skills add https://github.com/Centaurioun/osteogenesis_imperfecta --skill tooluniverse-domain-name
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: tooluniverse-[domain-name]
Source: https://github.com/Centaurioun/osteogenesis_imperfecta/tree/main/.agents/skills/create-tooluniverse-skill/assets/skill_template
Command: npx skills add https://github.com/Centaurioun/osteogenesis_imperfecta --skill tooluniverse-domain-name

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Provides a reusable, tested template for running domain-specific multi-database analyses that aggregate, compare, and summarize results into a single progressive Markdown report. It reduces friction for researchers and analysts who must query multiple specialized databases, handle differing response formats, apply fallback strategies, and produce reproducible, publication-ready outputs.

Core Features & Use Cases

  • Python SDK pipeline that orchestrates ToolUniverse tools, queries multiple databases, and generates phased report sections with source attribution.
  • MCP (Model Context Protocol) conversational and direct-tool examples for running analyses interactively or programmatically, including guidance for SOAP tools that require explicit operation parameters.
  • Robust workflow patterns and decision logic: primary/fallback database queries, graceful handling of empty results, error capture, and summary/context aggregation useful for cross-database comparisons and manuscript-ready outputs.
  • Test suite and example usages to validate end-to-end behavior and ensure reproducibility across analysis runs.

Quick Start

Ask Claude to perform a ToolUniverse domain analysis for a given input and produce a phased markdown report comparing results across integrated databases.

Frequently Asked Questions about tooluniverse-[domain-name]

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

FAQPage Schema
How do I run reproducible multi-database biomedical analyses with cross-database evidence synthesis?▼

Reproducible multi-database biomedical analyses use a Python SDK pipeline to orchestrate ToolUniverse tools, query multiple databases, and generate phased Markdown reports with source attribution and cross-database comparison.

What is the best way to generate publication-ready Markdown reports from multiple specialized database queries?▼

Generating publication-ready Markdown reports involves aggregating domain queries across multiple databases, applying fallback strategies, capturing errors gracefully, and consolidating results into progressive report sections with source attribution.

How does fallback querying work when a primary biomedical database returns empty results?▼

Fallback querying handles empty primary database results by automatically executing secondary database queries, capturing any errors, and aggregating available context to ensure cross-database comparisons remain intact within the Markdown report.

Can I use the ToolUniverse Python SDK with SOAP tools that require explicit operation parameters?▼

The ToolUniverse Python SDK supports SOAP tools requiring explicit operation parameters through both conversational and direct-tool examples, enabling interactive or programmatic biomedical analyses via the Model Context Protocol.

Do I need the Model Context Protocol to run domain-specific multi-database analyses interactively?▼

The Model Context Protocol provides interactive and direct-tool access for running domain-specific multi-database analyses, while the Python SDK pipeline offers a programmatic alternative for reproducible automated workflows.

What are the limitations of cross-database evidence synthesis when handling differing database response formats?▼

Cross-database evidence synthesis limitations include handling differing response formats across specialized databases, managing empty results through fallback strategies, and ensuring error capture and source attribution remain consistent throughout the Markdown report.

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