majiayu000 Multi-AI Orchestration
When to Use
Trigger conditions:
- Stuck after 2+ failed attempts
- Architecture decisions with multiple valid approaches
- Code review before major commits
- Debugging with no clear root cause
- Need validation from different reasoning styles
Quick Tools (Zero Setup)
| Tool | Models | Best For |
|---|---|---|
| chat.lmstudio.ai | Grok, Claude, Gemini, GPT-4o, Llama | Parallel responses in 10 seconds |
| poe.com Multi-bot | 10+ models | Custom chains (Claude → Grok → Gemini) |
| ChatHub extension | All major models | One browser tab, one hotkey |
Orchestration Patterns
Pattern 1: Parallel Fan-Out (Fastest)
When you need multiple perspectives quickly:
User problem → [Claude, Grok, Gemini] → Compare answers → Pick best
Use for: Architecture decisions, debugging hypotheses, API design choices
Pattern 2: Sequential Review Chain
When you need validation of work:
Claude writes code → Grok reviews → Gemini tests edge cases → Final version
Use for: Production code, critical bug fixes, security-sensitive changes
Pattern 3: Specialist Routing
Route based on problem type:
| Problem Type | Best Model |
|---|---|
| Complex reasoning | Claude, o1 |
| Code generation | Grok, Claude |
| Quick lookups/facts | Gemini, GPT-4o |
| Math/logic puzzles | o1, DeepSeek-R1 |
| Creative solutions | Claude, Grok |
Pattern 4: Debate Protocol
When models disagree:
- State the disagreement clearly
- Ask each model to steelman the other's position
- Ask for synthesis: "Given both perspectives, what's the best approach?"
- Human decides based on combined insight
Context Handoff Template
When passing context between models:
## Context
[Brief problem description - 2-3 sentences]
## What's Been Tried
- Approach 1: [result]
- Approach 2: [result]
## Current Best Hypothesis
[What the previous model concluded]
## What I Need From You
[Specific ask: validate, find holes, alternative approach, etc.]
Automation Options
10-minute setup:
- Poe.com custom multi-bot
- ChatHub browser extension
30-minute setup (fully automatic):
- LangGraph / CrewAI (Python)
- LazyLLM local orchestrator
Example CrewAI flow:
crew = Crew(
agents=[
Agent("Planner", model="grok"),
Agent("Coder", model="claude"),
Agent("Reviewer", model="gemini"),
Agent("Tester", model="gpt-4o")
],
process="sequential" # or "parallel"
)
Anti-Patterns
| Don't | Do Instead |
|---|---|
| Copy-paste everything manually | Use multi-chat tools |
| Ask same question to all models | Specialize the ask per model |
| Ignore disagreements | Use debate protocol |
| Over-orchestrate simple problems | Single model is fine for routine tasks |
When NOT to Use
- Simple, well-defined tasks
- Tasks one model handles confidently
- Time-sensitive with clear solution path
- Model-specific features (Claude artifacts, GPT DALL-E, etc.)
Integration with Other Skills
- systematic-debugging: Fan out hypotheses to multiple models
- react-architecture-enforcer: Get extraction plan validation
- brainstorming: Parallel ideation across models
- code-review: Multi-model review before merge