Dual-Brain Skill
What it does: Provides cognitive diversity by automatically generating perspectives from a secondary LLM (different from your primary model) for every user message.
For Agents
Before responding to any user message, check for a dual-brain perspective:
cat ~/.dual-brain/perspectives/{your-agent-id}-latest.md
If the file exists and contains a recent perspective (check timestamp in comment), consider it alongside your own reasoning. The perspective may highlight:
Angles you might miss
Alternative approaches
Important things to verify
Integration:
Perspectives are generated automatically by a background daemon
No manual action required on your part
Simply read the file before forming your response
Synthesize both viewpoints (yours + the secondary LLM's)
Example workflow:
User sends message
Daemon intercepts, sends to secondary LLM (e.g., Kimi, GPT-4, Llama)
Secondary LLM generates 2-3 sentence perspective
Written to
~/.dual-brain/perspectives/{agent-id}-latest.mdYou (primary agent) read it before responding
You synthesize both perspectives into your answer
For Humans
Setup:
npm install -g openclaw-dual-brain
dual-brain setup # Interactive configuration
dual-brain start # Start daemon
Providers:
ollama- Local models (zero cost, requires Ollama)moonshot- Kimi/Moonshot API (Chinese LLM, fast)openai- GPT-4o, GPT-4-turbo, etc.groq- Fast inference with Llama models
Commands:
dual-brain setup- Configure provider, model, API keydual-brain start- Run daemon (foreground)dual-brain stop- Stop daemondual-brain status- Check running statusdual-brain logs- View recent activitydual-brain install-daemon- Install as system service
Config location: ~/.dual-brain/config.json
Perspectives location: ~/.dual-brain/perspectives/
Architecture
User Message → OpenClaw Session (JSONL)
↓
Dual-Brain Daemon (polling)
↓
Secondary LLM Provider
(ollama/moonshot/openai/groq)
↓
Perspective Generated (2-3 sentences)
↓
~/.dual-brain/perspectives/{agent}-latest.md
↓
Primary Agent reads & synthesizes
↓
Response to User
Benefits
Cognitive diversity - Two AI models = broader perspective
Bias mitigation - Different training data/approaches
Quality assurance - Second opinion catches issues
Zero agent overhead - Runs in background, <1s latency
Provider flexibility - Choose cost vs. quality tradeoff
Optional: Engram Integration
If Engram (semantic memory) is running on localhost:3400, perspectives are also stored as memories for long-term recall.