Multi-Model Ensemble Analysis

Orchestrate multiple AI models to analyze complex problems from different perspectives. This skill launches parallel agents to gather diverse viewpoints, then synthesizes findings into actionable recommendations.

When to Use

Architectural decisions with significant trade-offs
Complex debugging where root cause is unclear
Design reviews where you want diverse perspectives
Technology selection decisions
Code review for critical or safety-sensitive changes
Any problem where multiple viewpoints add value

Workflow

Phase 1: Problem Identification

Extract the core problem from the conversation:

Identify the decision/challenge - What specific question needs analysis?
Gather context - Read CLAUDE.md, relevant source files, and conversation history
Note constraints - Time, resources, compatibility requirements, etc.

Summarize in 2-3 clear sentences.

Phase 2: Create Unified Prompt

Create ONE prompt that will be sent to all models. The prompt must be:

Self-contained - Include all necessary context directly (don't rely on agents having access to files)
Unbiased - Do NOT pre-bias external models with your opinions
Structured - Clear sections for problem, context, constraints, and what you want analyzed

Prompt Template:

## Problem
[2-3 sentence description of the decision/challenge]

## Context
[Relevant technical details, code snippets, architecture info]
[Inject content from CLAUDE.md or relevant files directly]

## Constraints
[Time, compatibility, performance, safety requirements]

## Analysis Requested
Please analyze this problem and provide:
1. Your recommended approach with rationale
2. Key risks or concerns
3. Alternative approaches considered
4. Implementation considerations

Phase 3: Parallel Model Execution

Launch ALL THREE Task tools in a SINGLE response so they run in parallel:

1. Claude Critic (Sonnet)

Task tool with subagent_type: "general-purpose"
model: "sonnet"
prompt: [Your unified prompt + "Focus on critical analysis, edge cases, and potential failure modes."]

2. Gemini Analysis

Task tool with subagent_type: "Bash"
prompt: "Run: gemini '[Your unified prompt]' and return the full response"

3. Codex Analysis

Task tool with subagent_type: "Bash"
prompt: "Run: codex '[Your unified prompt]' and return the full response"

Important:

All three must be launched in a SINGLE message with multiple tool calls
Inject context directly into prompts - don't assume agents can read files
Keep prompts identical for fair comparison

Phase 4: Synthesis

After all models respond, synthesize findings:

1. Consensus Points

Where do all models agree?
These are likely strong recommendations

2. Disagreements

Where do models diverge?
Analyze WHY they disagree
Your role: decide which perspective is stronger given your full context

3. Blind Spots

What did one model catch that others missed?
These often surface valuable edge cases

4. Final Recommendation

Integrate findings into a clear recommendation
Acknowledge trade-offs
Be specific about next steps

Error Handling

Models may fail due to:

Authentication - Gemini/Codex may not be configured
Timeouts - Long prompts may exceed limits
Unavailability - External services may be down

When errors occur:

Document which models succeeded/failed
Continue analysis with available responses
Note if a failed model might have provided unique perspective

Example

User asks: "Should we use WebSockets or SSE for real-time updates in the console?"

Phase 1 - Problem: "Deciding between WebSockets and Server-Sent Events for streaming rover telemetry to the depot console. Need bidirectional communication for teleop commands but also want simplicity for status updates."

Phase 2 - Prompt:

## Problem
Choosing between WebSockets and SSE for real-time communication in a robotics fleet management console. Need to stream telemetry from rovers and send teleop commands.

## Context
- React frontend with Zustand state management
- Rust backend services using Tokio
- Current: WebSocket for teleop, considering SSE for status updates
- ~10 rovers max per depot initially
- Telemetry at 10-30Hz, commands at 50Hz

## Constraints
- Must work through corporate proxies
- Need reconnection handling
- Mobile browser support required
- Latency-sensitive for teleop

## Analysis Requested
1. Recommended approach with rationale
2. Key risks or concerns
3. Alternative approaches
4. Implementation considerations

Phase 3 - Launch all three agents in parallel

Phase 4 - Synthesis: "All models agree WebSockets are necessary for teleop commands. Gemini suggested a hybrid approach: WebSocket for teleop, SSE for status monitoring. Codex raised proxy traversal concerns that weren't in the other responses. Recommendation: Keep WebSocket for all real-time communication for consistency, but implement proper reconnection handling as all models flagged this."

Best Practices

Be specific - Vague problems get vague answers
Include code - When relevant, include actual code snippets in prompts
State constraints clearly - Models can't guess your requirements
Don't over-synthesize - Preserve disagreements when they represent genuine trade-offs
Act on findings - The value is in the decision, not the analysis

Limitations

External models (Gemini, Codex) require CLI tools to be installed and authenticated
Very long prompts may need to be trimmed
Models may have different knowledge cutoffs
Cost: Running three models is 3x the compute

ensemble