Multi-AI Conversation Orchestration
Purpose: Coordinate conversations across multiple AI models with shared context, persistent state, and audit trails.
Why This Matters
Single-AI analysis has blind spots. Multi-model conversations:
- •Catch more issues: Different models flag different concerns
- •Build confidence: Consensus across models = higher confidence
- •Create audit trails: Document which AI said what, when
- •Persist context: Insights survive session restarts
Most users query one AI. This skill teaches systematic multi-model orchestration.
Quick Usage
Inline (Simple)
**Task**: Review auth design **Multi-model check**: - Claude: [your analysis in this conversation] - Gemini: `cat design.md | gemini "Review for security"` - Codex: `cat design.md | codex exec "Review for architecture"` **Consensus**: All flagged token expiry. High confidence issue.
Structured (Complex)
For important decisions, use full orchestration:
- •Create kanban task for shared context
- •Gather perspectives systematically
- •Synthesize with documented rationale
- •Store decision in wicked-mem
The Orchestration Process
Step 1: Establish Shared Context
Create a single source of truth all AIs can reference.
/wicked-kanban:new-task "Design review: Payment API" --priority P0
Add the context document to the task description.
→ See refs/context.md for context management patterns.
Step 2: Gather Perspectives
Query each AI with the same focused prompt for comparable results.
| AI | Command | Strength |
|---|---|---|
| Claude | (in conversation) | Nuanced reasoning |
| Gemini | cat doc | gemini "prompt" | Long context |
| Codex | cat doc | codex exec "prompt" | Code-focused |
| OpenCode | opencode run "prompt" -f doc | Multi-provider |
Add each response as a kanban comment with attribution.
→ See refs/automation.md for scripts.
Step 3: Synthesize Results
Compare perspectives and identify:
| Signal | Meaning | Action |
|---|---|---|
| Consensus (2+ agree) | High confidence issue | Address immediately |
| Unique insight | One AI caught something | Evaluate carefully |
| Disagreement | Genuine tradeoff | Human decides |
| Silence | No AI flagged it | Lower priority |
→ See refs/examples.md for synthesis templates.
Step 4: Record and Persist
Store the decision with full attribution:
/wicked-mem:store "Payment API: Use Stripe webhooks. Consensus: Claude, Gemini, Codex (idempotency critical). Unique: OpenCode flagged circuit breaker need. Decision by: [Human] on [Date]" --type decision --tags payments
→ See refs/auditability.md for audit patterns.
Decision Principles
When to Use Multi-Model
| Situation | Recommendation |
|---|---|
| Architecture decisions | Yes - high impact |
| Security review | Yes - catch blind spots |
| Quick bug fix | No - overhead not worth it |
| Important PRs | Yes - diverse review |
| Routine code | No - single AI sufficient |
How to Weight Perspectives
- •Consensus wins: 2+ models agreeing = high confidence
- •Expertise matters: Codex for code, Gemini for long docs
- •Unique insights valuable: Don't dismiss outliers
- •Human decides conflicts: AI informs, human chooses
When to Start Fresh vs. Continue
| Signal | Action |
|---|---|
| New topic | Fresh context |
| Building on prior | Continue session |
| Context polluted | Summarize and restart |
| Need unbiased view | Fresh, neutral handoff |
→ See refs/context.md for session patterns.
Output Format
For formal multi-model reviews:
## Multi-Model Review: [Topic] **Context**: [What was reviewed] **Models**: Claude, Gemini, Codex, OpenCode ### Consensus (High Confidence) - Issue 1: [flagged by Claude, Gemini, Codex] - Issue 2: [flagged by all] ### Unique Insights - **Gemini**: [insight others missed] - **OpenCode**: [insight others missed] ### Disagreements - [Topic]: Claude says X, Codex says Y ### Decision [What was decided and why] ### Stored - wicked-mem: [memory ID] - wicked-kanban: [task ID]
Quick Reference
| Need | Solution |
|---|---|
| Shared context | wicked-kanban task |
| Persist decisions | wicked-mem |
| Automate gathering | refs/automation.md scripts |
| Audit trail | refs/auditability.md patterns |
| Session management | refs/context.md |
| Templates | refs/examples.md |
References
Process:
- •Context Management - Session state, handoffs, context windows
- •Automation - Scripts for gathering perspectives
Quality:
- •Auditability - Audit trails, compliance, decision tracking
- •Examples - ADR templates, synthesis patterns