multi-model-analysis

name	multi-model-analysis
description	Runs 3 AI models in parallel to independently analyze a problem and propose approaches. Compares all proposals against a rubric and selects the best. Can be invoked standalone or called from any agent (issue-resolver, pr-review, etc.).

Skill: Multi-Model Analysis

Analyze a problem or code change using three AI models in parallel, then compare and select the best approach.

When to Use This Skill

• ✅ Validate a proposed fix before implementing
• ✅ Review a PR's code changes from multiple perspectives
• ✅ Get a second opinion on an architectural approach
• ✅ Called from another agent to enrich its analysis

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Inputs Required

The caller (agent or user) must provide:

Input	Description
Subject	What is being analyzed — a proposed fix, a PR diff, a design approach, etc.
Context	Issue/PR description, root cause (if known), affected files and relevant source snippets
Primary approach	The default model's or caller's current proposed approach (optional — omit if no prior proposal exists)

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Step 1: Permission Prompt

Before running, show the user:

🤖 Multi-Model Analysis available.
Subject: [one-line summary of what will be analyzed]

Three AI models will independently analyze this and propose approaches.
All proposals will be compared and the best selected.

Proceed with Multi-Model Analysis? (yes / no)

• NO → Return to the caller with the primary approach unchanged.
• YES → Continue below.

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Step 2: Launch 3 Parallel Agents

Launch all three agents simultaneously using the explore agent type with this prompt:

You are a senior .NET MAUI engineer reviewing code for the Syncfusion Toolkit for .NET MAUI.

Subject: [what is being analyzed]

Context:
[issue/PR description, root cause, affected files]

Source code context:
[relevant source snippets]

[If a primary approach exists:]
Primary approach already proposed:
[paste the primary approach]

Task: Propose your own independent approach. Include:
1. Exact files and methods involved
2. The proposed change (before/after or pseudocode)
3. Why this approach is correct and safe
4. Any risks or edge cases to watch for
[If primary approach exists: 5. Whether you agree or disagree with the primary approach, and why]

Be concise and specific. Do NOT implement — only propose.

Agent	Model
Agent A	claude-sonnet-4.6
Agent B	gpt-5.1
Agent C	gemini-3-pro-preview

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Step 3: Evaluate and Select the Best Approach

Compare all proposals (primary + 3 agents) using this rubric:

Criterion	Weight
Correctness — directly addresses the subject	High
Minimal change — fewest lines/files affected	High
Safety — no regressions, handles edge cases	High
Consistency with existing code style	Medium
Testability — easy to verify	Medium

Select the best approach — one proposal or a synthesis of multiple.

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Step 4: Report the Result

Return a structured summary to the caller:

## Multi-Model Analysis Result
| Model | Approach Summary | Selected? |
|-------|-----------------|-----------|
| Primary / Default | [brief summary or "N/A"] | ✅ / ❌ |
| claude-sonnet-4.6 | [brief summary] | ✅ / ❌ |
| gpt-5.1 | [brief summary] | ✅ / ❌ |
| gemini-3-pro-preview | [brief summary] | ✅ / ❌ |

**Selected Approach**: [model name, or "synthesis of X+Y"]
**Reason**: [why this approach was chosen]
**Next Step**: [what the caller should do with this result]

If invoked from an agent, the agent records this in its state file and proceeds with the selected approach. If invoked directly, present the result to the user.