multi-model-analysis

# 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: ```markdown ## 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.