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multi-model-triangulation

Generate copy-paste prompts to cross-validate decisions across multiple AI models.

3|1|Updated Apr 15, 2026
Search Tags:#code review#multi-model#synthesis#prompt templates#second opinion#architecture decision#triangulation
One-click install
npx skills add https://github.com/burningportra/agent-flywheel-plugin --skill multi-model-triangulation-burningportra
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: multi-model-triangulation
Source: https://github.com/burningportra/agent-flywheel-plugin/tree/main/skills/multi-model-triangulation
Command: npx skills add https://github.com/burningportra/agent-flywheel-plugin --skill multi-model-triangulation-burningportra

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve? Single-model answers carry single-model blind spots, and high-stakes decisions made on one AI's opinion can miss critical flaws. This Skill structures the process of getting second and third opinions from other models (Codex, Gemini, Grok) and synthesizing their responses into one confident recommendation. ## Core Features & Use Cases - Ready-to-Copy Prompt Templates: Structured prompts for idea evaluation, code review, architecture decisions, debugging, risk assessment, performance analysis, and security review. - Synthesis Framework: A consensus/divergence/unique-insights template that merges multiple model responses into a single recommendation with a confidence rating. - Prompt Generator Script: A Python CLI that formats idea, code, architecture, and debug prompts ready to paste into any model. - Use Case: Choosing between GraphQL and REST for a new API — generate the evaluation prompt, paste it into Gemini and Codex, return their answers, and receive a synthesized recommendation explaining where the models agree, where they diverge, and why. ## Quick Start Ask the assistant to triangulate your architecture decision by generating prompts for Codex and Gemini, then paste their responses back for a synthesized recommendation.

Frequently Asked Questions about multi-model-triangulation

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

FAQPage Schema
How do I get a second opinion from another AI model on a decision?▼

Generate a structured prompt describing your decision, paste it into models like Codex, Gemini, or Grok, then return their responses for synthesis. The synthesis template separates consensus points, divergences, and unique insights into one recommendation.

How to cross-validate a code review across multiple AI models?▼

Use the code review prompt template, which asks each model to categorize findings as Critical, Important, or Suggestions with an overall score. Run it through at least two models and compare where they agree on critical issues like injection vulnerabilities or race conditions.

When should I triangulate a decision across multiple models?▼

Triangulate when a decision is hard to reverse or high-impact, such as architecture choices and security reviews. Skip it for quick questions or routine tasks where the coordination overhead outweighs the benefit of extra perspectives.

Which AI models work best for security review triangulation?▼

Use all available models for security reviews, since each has different blind spots. Claude handles nuanced reasoning, Codex covers implementation details, Gemini brings current data, and Grok offers unconventional angles.

What are the limitations of multi-model triangulation?▼

The process is manual: you must copy prompts to other models and paste responses back, since models cannot be called directly. It also adds latency and effort, making it unsuitable for routine low-stakes questions.

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