from personality_engine.engine import PersonalityEngine

engine = PersonalityEngine(user_id="user@example.com")

# Pass triggers through the engine
should_send, msg = await engine.process_trigger(
    trigger_type="cross_platform",
    raw_message="Divergence: Kalshi 52%, Poly 48%",
    market_data={"spread": 4.0},
    urgency_context={},
)

if should_send:
    send_message(msg.content)

The 6 Systems

System	What It Does
Editorial Voice	Injects opinions that vary by trigger type and market state
Selective Silence	Knows when NOT to talk — skips flat days, stale data, noise
Variable Timing	Urgency scoring (0-1) with time-of-day delivery thresholds
Micro-Initiations	Unprompted ambient pings ("Quiet week. Enjoy it.")
Context Buffer	Daily memory with back-references to earlier messages
Response Tracker	Adapts to user engagement patterns over time

Domain-Agnostic

Default configuration is tuned for prediction market trading, but every system adapts to any domain: personal assistants, DevOps monitors, sales CRM, content management. Swap voice pools, thresholds, and micro-initiation conditions.

Full Documentation

See SKILL.md for complete documentation including per-system architecture, customization guide, integration steps, and domain adaptation table.

Part of the OpenClaw Prediction Market Trading Stack

clawhub install kalshalyst kalshi-command-center polymarket-command-center prediction-market-arbiter xpulse portfolio-drift-monitor market-morning-brief personality-engine

Author: KingMadeLLC