mikeparcewski/multi-model

Multi-Model Collaboration Skill

Orchestrate multi-model AI collaboration using external LLM CLIs. Each council member is a different model provider for genuine perspective diversity.

How It Works

The multi-model system uses external LLM CLIs discovered at runtime:

1. CLI Discovery — which codex copilot gemini opencode pi aider llm aichat goose detects installed CLIs
2. Quorum Check — Council requires 2+ external CLIs; 0 = refuse, 1 = warn
3. Question Scaffold — All models answer the same fixed 4-question set
4. Parallel Dispatch — Each CLI receives the scaffold via stdin pipe, runs independently
5. Synthesis — Claude synthesizes all perspectives with model attribution

Quick Start

# Council mode — dispatches to external CLIs in parallel
/jam:council "Should we use JWT or sessions for auth?"

# Quick jam — single model, 4 personas, fast
/jam:quick "How should we improve the visual design?"

# Full brainstorm — single model, 4-6 personas, 2-3 rounds
/jam:brainstorm "Architecture for the notification system"

Supported CLIs

| CLI | Install | Model / Provider | |-----|---------|------------------| | codex | brew install codex | OpenAI Codex | | copilot | brew install copilot-cli | GitHub Copilot | | gemini | npm i -g @google/gemini-cli | Google Gemini | | opencode | brew install opencode | Configurable (OpenAI/Anthropic/local) | | pi | npm i -g @mariozechner/pi-coding-agent | Configurable (Google default, OpenAI/Anthropic/etc.) | | aider | brew install aider | Configurable — code-editor orientation | | llm | brew install llm | Multi-provider aggregator (OpenAI, Anthropic, Mistral, local via plugins) | | aichat | brew install aichat | Multi-provider aggregator | | goose | brew install block-goose-cli | Block Goose agent (configurable provider) |

Claude always participates as a council member alongside the external CLIs.

Architecture

┌─────────────────────────────────────────────────────┐
│  /jam:council Command                                │
│  - Parses topic, options, criteria                   │
│  - Dispatches to council agent                       │
├─────────────────────────────────────────────────────┤
│  Council Agent (agents/jam/council.md)               │
│  - Detects CLIs via `which`                          │
│  - Builds question scaffold (4 fixed questions)      │
│  - Pipes scaffold to each CLI in parallel            │
│  - Claude answers the same scaffold independently    │
├─────────────────────────────────────────────────────┤
│  External CLI Dispatch                               │
│  - cat scaffold.md | codex exec "..."                │
│  - cat scaffold.md | gemini "..."                    │
│  - cat scaffold.md | opencode run "..."              │
│  - pi -p "..." @scaffold.md                          │
│  - aider --message-file scaffold --no-git --yes     │
│  - cat scaffold.md | llm "..."                       │
│  - cat scaffold.md | aichat "..."                    │
│  - cat scaffold.md | goose run -i -                  │
├─────────────────────────────────────────────────────┤
│  Synthesis (3-stage)                                 │
│  - Stage 1: Raw responses per model                  │
│  - Stage 2: Synthesis matrix + risk convergence      │
│  - Stage 3: Verdict (consensus or fault lines)       │
└─────────────────────────────────────────────────────┘

Synthesis Framework

After gathering perspectives from different models, synthesize using:

| Signal | Meaning | Action | |--------|---------|--------| | Consensus (2+ models agree) | High confidence issue | Address immediately | | Unique insight | One model caught it | Evaluate carefully | | Disagreement | Genuine tradeoff | Human decides | | Silence | No model flagged it | Lower priority |

Persistence

Automatic (via save_transcript.py)

Council responses are persisted as transcript entries:

• Each model's response stored with persona_type: council
• Synthesis appended as entry_type: synthesis
• Retrievable via wicked-brain:query for past session context

Manual (via wicked-brain:memory)

Store decisions with full attribution:

Skill(skill="wicked-brain:memory", args="store \"Auth: JWT with 15min/7day expiry.\nConsensus: Claude, Gemini, Codex (idempotency critical).\nUnique: Gemini flagged session store scaling concern.\nDissent: none.\" --type decision --tags auth,multi-model-review")

When to Use Multi-Model

| Situation | Recommendation | |-----------|----------------| | Architecture decisions | Yes — high impact, catch blind spots | | Security review | Yes — different models flag different risks | | Important PRs | Yes — diverse review perspectives | | Visual/UX design | Yes — different aesthetic sensibilities | | Quick bug fix | No — overhead not worth it | | Routine code | No — single AI sufficient |

Fallback Behavior

If no external CLIs are detected, council refuses and suggests /jam:brainstorm (single-model, multi-persona) as an alternative. With only 1 CLI, it runs as "brainstorm with external guest" with a warning.

References

Orchestration:

• Orchestration Patterns — CLI dispatch, parallel execution, synthesis
• Context Management — Session state, cross-AI handoffs, context windows

CLI Providers:

• Codex | Copilot | Gemini | OpenCode | Pi
• Aider | llm | aichat | Goose

Quality:

• Auditability — Audit trails, compliance, decision tracking
• Examples — ADR templates, synthesis patterns, review templates