| 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
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) |
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.
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 |
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.
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.
