- name
- council
- description
- Your personal board of AI advisors — the only skill that uses truly different AI models (not one model role-playing). Get better answers to hard questions by having 3-5 models from different providers analyze independently, then synthesizing consensus, disagreements, and action items. Each model brings genuinely different training and reasoning — catching blind spots that same-model approaches miss. Zero external dependencies: uses native OpenClaw sub-agents, no Python scripts, no API keys beyond your existing model config. Use when: user says /council, or asks for multi-model analysis of a decision, architecture, strategy, or any complex problem.
Consilium — True Multi-Model Deliberation
Ask a hard question → 3-5 AI models from different providers analyze it independently → you get a synthesis with consensus, disagreements, action items, and minority opinions.
Unlike other council skills: this uses genuinely different models (Anthropic + OpenAI + Google + others), not one model playing multiple roles. Different training data = different blind spots = better coverage.
Always respond in the same language as the user's question.
Examples
/council Should we migrate from monolith to microservices given our 4-person team?/council --profile fast Evaluate the risks of this investment strategy/council How to resolve a complex equity dispute with my co-founder?- After results: *"Tell me more about what Gemini said on point 3"* (follow-up with specific panelist)
Requirements
- Minimum 3 models from different providers in
agents.defaults.modelsallowlist - Tools:
sessions_spawn,subagents,sessions_history(enabled by default) - Each council run = 3-5 API calls (one per model) + synthesis
- No additional API keys, Python scripts, or external dependencies
Privacy & Data
- Your question is sent to each model provider in your panel. Only use models/providers you trust.
council-panel.json(saved to workspace root) contains only model names and slot assignments, not queries or responses.- Panelist responses exist only in sub-agent session memory and are auto-archived per your OpenClaw settings.
- No data is sent to external services beyond your configured model providers.
Panel
On first use, check available models and ask the user to confirm the panel. Save to workspace root as council-panel.json for reuse. User can re-run panel selection anytime with --models.
Slot roles (fill from available models)
| Slot | Role | Good candidates |
|---|---|---|
| Deep thinker | Nuance, system thinking | Claude Opus, GPT-5, Gemini Pro |
| Pragmatist | Concise, actionable | Claude Sonnet, GPT-mini, Gemini Flash |
| Broad analyst | Wide knowledge, structure | GPT-5, Gemini Pro, Claude Opus |
| Technical | Rigor, edge cases | Gemini Pro, Claude Sonnet, GLM |
| Contrarian | Challenge assumptions | GLM, any model with contrarian lens |
Rules: Each slot = different model. Prefer different providers. Min 3 models to run. If fewer than 3 available, inform user.
Example council-panel.json
{
"panel": [
{ "slot": "deep_thinker", "model": "anthropic/claude-opus-4-6", "lens": "Deep analysis" },
{ "slot": "pragmatist", "model": "anthropic/claude-sonnet-4-5", "lens": "Pragmatic" },
{ "slot": "broad_analyst", "model": "github-copilot/gpt-5.2", "lens": "Broad knowledge" }
],
"confirmed": "2026-02-24"
}Profiles
- thorough (default): All panel slots, quorum = max(slots - 2, 2)
- balanced: 3 strongest slots, quorum 2
- fast: 2 fastest slots, quorum 2
Workflow
- Dispatch — spawn panelists in parallel (
sessions_spawn, mode=run, timeout 120s). Assign unique lens per slot. Detect question language, hardcode in prompt. Tell user: "Panel dispatched, ~60s. Send a follow-up when ready." - Collect — on user's follow-up:
subagents list→sessions_history. Synthesize when quorum met. - Debate (only if
--rounds 2) — anonymized digest → rebuttals. Seereferences/PROTOCOL.md. - Synthesize — produce output below.
Output Format
## Council of Experts
**Question:** ... | **Panel:** ... | **Profile:** ...
---
### Positions
**{Model}** ({lens}) — {2-3 sentence summary}
### ✅ Consensus
### ⚡ Disagreements
### 🗣️ Minority opinions
### 🎯 Synthesis
Agreement: 🟢 strong (4-5) | 🟡 mixed (3) | 🔴 split
### 📋 Action Items
1. **{Highest priority}** — {effort/time estimate}
2. **{Next action}** — {estimate}
3. **{Next action}** — {estimate}Randomize position order. Quote with attribution. Preserve minority views. Never fabricate consensus. Section headers and content in user's language.
Follow-up
After synthesis, the user can drill deeper with a specific panelist:
- *"Tell me more about what GPT said on point 2"*
- *"I want the contrarian's take on the action items"*
Use sessions_history to retrieve that panelist's full response, then expand on the specific point in that model's perspective.
Flags
--profile thorough|balanced|fast · --models <list> · --skip <model> · --rounds 2 · --quorum N · --timeout N · --lens "..." · --lenses "a,b,c"
Prompt templates, debate mechanics, error handling → references/PROTOCOL.md