multi-model-triangulation

name	multi-model-triangulation
description	Cross-validate decisions using multiple AI models (Codex, Gemini, Grok). Use when "get a second opinion", evaluating approaches, or high-stakes decisions.

Multi-Model Triangulation

Core Insight: Different models have different blind spots. Consensus = confidence.

How It Works

You can't directly call other models. Instead:

1. I generate copy-paste prompts for you
2. You paste into Codex/Gemini/Grok/etc
3. You return their responses to me
4. I synthesize into unified recommendation

Claude → generates prompt → You → paste to Model B → You → paste response back → Claude synthesizes

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Quick Start

Tell me what you want triangulated:

Triangulate: [topic]
Context: [relevant details]
Models to use: [Codex, Gemini, Grok, or "all available"]

I'll generate the prompt(s). You copy-paste and return results.

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Ready-to-Copy Prompts

Idea Evaluation

# COPY TO [Model Name]:

Evaluate these ideas. Score 1-10 on Quality/Utility/Feasibility/Risk:

1. [IDEA 1]
2. [IDEA 2]
3. [IDEA 3]

For each: scores, one-sentence rationale, final ranking.
Be critical—don't just agree.

Code Review

# COPY TO [Model Name]:

Review for bugs/security/improvements:

```[lang]
[CODE]

Categorize: Critical (must fix), Important (should fix), Suggestions. Overall score: X/10. Be thorough.

### Architecture Decision

COPY TO [Model Name]:

Choosing between: A: [Option A] B: [Option B] C: [Option C]

Evaluate: complexity, maintainability, performance, scalability. Recommend ONE with reasoning. Be opinionated.

More prompts: [PROMPTS.md](references/PROMPTS.md)

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## Synthesis Template

After I receive responses from multiple models:

```markdown
## Triangulation: [Topic]

### Consensus (High Confidence)
- [Points ALL models agree on]

### Divergence (Investigate)
| Topic | Claude | Model B | Model C |
|-------|--------|---------|---------|
| [X]   | [view] | [view]  | [view]  |

### Unique Insights
- **Claude:** [unique point]
- **Model B:** [unique point]

### Recommendation
[Synthesized recommendation]

### Confidence: [High/Medium/Low]

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Model Strengths

Model	Strengths	Best For
Claude	Nuance, safety, writing	Complex reasoning, docs
GPT/Codex	Code generation, breadth	Implementation details
Gemini	Multimodal, current data	Visual, recent events
Grok	Unconventional takes	Creative alternatives

Tip: For security reviews, use ALL models. For routine code review, 2 is enough.

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When to Triangulate

Decision Type	Triangulate?	Why
High-stakes architecture	Yes	Hard to reverse
Security review	Yes	Blind spots are dangerous
Code review (routine)	Maybe	1-2 models sufficient
Quick question	No	Overhead not worth it
Creative brainstorming	Yes	Different perspectives

Rule: If hard to reverse or high-impact, triangulate.

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Anti-Patterns

Don't	Do
Ask vague questions	Specific, structured prompts
Accept first answer	Get 2-3 perspectives
Ignore disagreements	Investigate WHY models differ
Weight all equally	Consider model strengths
Skip synthesis	Always produce unified view

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Integration

Script Helper

# Generate formatted prompt for ideas
./scripts/format-prompt.py idea "Idea 1" "Idea 2" "Idea 3"

# For code (reads from stdin)
cat code.py | ./scripts/format-prompt.py code

# For architecture
./scripts/format-prompt.py arch "Use Redis" "Use PostgreSQL"

With Other Skills

Combine with...	For...
ux-audit	Get multiple UX perspectives
multi-pass-bug-hunting	Cross-validate bug findings
idea-wizard	Score generated ideas

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References

Topic	File
Full prompt library	PROMPTS.md
Real examples	EXAMPLES.md

Scripts

Script	Purpose
scripts/format-prompt.py	Generate copy-paste ready prompts