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multi-model-research

Orchestrate parallel queries to multiple frontier LLMs and synthesize a definitive report.

2|3|Updated Oct 20, 2025
Search Tags:#research#ai#multi-model#synthesis#perplexity#gpt#llm council
One-click install
npx skills add https://github.com/psd401/psd-claude-plugins --skill multi-model-research-psd401
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: multi-model-research
Source: https://github.com/psd401/psd-claude-plugins/tree/main/plugins/psd-productivity/skills/multi-model-research
Command: npx skills add https://github.com/psd401/psd-claude-plugins --skill multi-model-research-psd401

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires httpx, pyyaml, python-dotenv, python-frontmatter, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill tackles complex research questions by leveraging the collective intelligence of multiple advanced AI models, ensuring comprehensive analysis and cross-validation of information.

Core Features & Use Cases

  • Multi-Model Synthesis: Orchestrates queries across leading LLMs (Claude, GPT-5.1, Gemini, Perplexity, Grok) for diverse perspectives.
  • LLM Council Pattern: Implements a peer-review and chairman synthesis process for robust, high-quality output.
  • Use Case: When researching a nuanced topic like "Compare the AI strategies of OpenAI, Anthropic, and Google," this Skill gathers insights from each model, facilitates peer review, and synthesizes a definitive report.

Quick Start

Use the multi-model research skill to conduct a deep dive into the latest advancements in quantum computing.

Frequently Asked Questions about multi-model-research

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

FAQPage Schema
How does multi-model research synthesis improve AI analysis?▼

An LLM Council pattern improves research by facilitating peer review among multiple AI models and using chairman synthesis to resolve conflicts, yielding a cross-validated, comprehensive report.

Do I need Python API keys to run multi-model AI research?▼

Yes, you need Python API keys for orchestration. The Skill requires Python scripts to coordinate API calls to models like Perplexity Sonar and Grok 4.1, utilizing dependencies like httpx for execution.

How do I use an LLM council to compare AI strategies?▼

You use an LLM council to compare AI strategies by querying multiple models, facilitating peer review of their responses, and applying chairman synthesis to generate a definitive, cross-validated report.

What's the best way to cross-validate research using multiple LLMs?▼

The best way to cross-validate research using multiple LLMs is the LLM Council pattern, which orchestrates parallel queries, facilitates peer review, and synthesizes a definitive report.

When should I avoid using a multi-model AI council for research?▼

Avoid using a multi-model AI council for simple queries, as the overhead of orchestrating parallel queries, peer review, and chairman synthesis is designed for complex, nuanced research topics.

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