Personality Engine — 6-System Behavior Framework

Goal: Make any AI agent feel alive — opinions, awareness, judgment, memory, timing sense, and engagement sensitivity. Works with trading agents, notification systems, personal assistants, or any proactive agent. Not just data delivery.

Architecture Overview

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Plus two ambient systems:

• - microinitiations: Unprompted pings when conditions are met (quiet market, good streak, absence detected)
• responsetracker: Monitors engagement; adjusts urgency + suggests tuning

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System 1: Editorial Voice — Opinion Injection

What: Each trigger type gets a personality — opinions that vary based on market state, portfolio P&L, signal confidence.

Per-trigger voice pools:

cross_platform (Kalshi vs Polymarket divergence)

• - Bullish divergence (>5%): "Big divergence. One of these markets is wrong."
• Mild divergence (2-5%): "Mild divergence. Nothing screaming yet."
• Stale divergence (6+ hours old): "Divergence is stale — markets may have already repriced."

portfolio (user's holdings performance)

• - +15% or better: "Good day. Portfolio's running."
• +5% to +15%: "Solid gains. Steady hand."
• -5% to +5%: "Flat day. Markets are grinding."
• -5% to -15%: "Rough patch. Check your stops."
• -15% or worse: "Heavy day. Buckle up for volatility."

x_signals (social signal scanner)

• - Confidence ≥0.85 + matched position: "Strong signal. This feels real."
• Confidence 0.70-0.85: "New signal on [topic]. Worth watching."
• Confidence <0.70: "Noise signal. Low confidence."

edge (Kalshi edge detection)

• - Edge >3%: "Fat edge. Worth a deep look."
• Edge 1-3%: "Mild edge. Keeping it on radar."
• Edge <1%: "Thin edge. Not worth the friction."

morning (daily brief)

• - Monday: "New week. Here's the lay of the land."
• Friday: "Friday rundown. What matters before the close."
• Other: "Daily digest."

conflicts (overlapping triggers same hour)

• - 2+ conflicts: "Tomorrow's a mess. Multiple overlaps."
• Lighter: "Heads up — couple things hitting together."

Customization point: Add trigger types by extending the VOICE_POOLS dict in editorial_voice.py. Each entry maps (trigger_name, market_state) → list of opinion strings.

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System 2: Selective Silence — Knowing When NOT to Talk

What: Not every trigger fire deserves a message. Silent skips are explicit: "Skipped the brief — nothing worth your attention."

Content quality checks per trigger:

• - morningisboring: If market vol <0.5%, no divergences, no edges → skip
• divergenceisstale: If last message on this topic was <3 hours ago AND spread hasn't moved >0.5% → skip
• signalsarenoise: If all signals have confidence <0.65 AND no position matches → skip
• edgeisweak: If all edges <1% → skip
• portfolioisflat: If daily P&L is -2% to +2% AND no major position changes → skip

Silence cadence:

• - Max 1 silence message per day per user
• Only for expected triggers (morning, portfolio check, etc.)
• Never silence micro-initiations (those are the value)
• When silent, send explicit message: INLINECODE3

Customization point: Adjust thresholds in selective_silence.py:

SILENCE_THRESHOLDS = {
    'vol_floor': 0.5,           # % vol threshold for morning silence
    'divergence_age_limit': 3,  # hours
    'signal_confidence_floor': 0.65,
    'edge_floor': 1.0,          # %
    'portfolio_flat_range': 2.0 # % P&L range
}

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System 3: Variable Timing — Urgency Scoring + Time-of-Day Awareness

What: Schedule message delivery based on urgency (0.0-1.0) and time of day. A mild divergence at 6 AM gets sent immediately (threshold 0.9 before 7 AM). Same divergence at 10 PM gets held (threshold 0.35).

Per-trigger urgency base:

• - cross_platform: (spread / 10%) * 0.6, capped at 1.0

- 5% spread = 0.3 urgency
- 10% spread = 0.6 urgency
- 15%+ spread = 1.0 urgency

• - portfolio: (abs(daily_pnl) / 10%) * 0.7, capped at 1.0

- ±5% P&L = 0.35 urgency
- ±15% P&L = 1.0 urgency

• - xsignals: (confidence * 0.8) + (positionmatch ? 0.2 : 0), capped at 1.0

- Confidence 0.85 + matched = 0.88 urgency
- Confidence 0.70 + no match = 0.56 urgency

• - edge: (edge_size / 5%) * 0.8, capped at 1.0

- 2% edge = 0.32 urgency
- 5% edge = 0.8 urgency

• - meeting: (1.0 - minutes_away / 120) capped at 1.0

- 30 min away = 0.75 urgency
- 5 min away = 0.96 urgency

Time-of-day delivery thresholds:

• - Before 7 AM: threshold 0.90 (almost everything gets sent)
• 7 AM - 9 AM: threshold 0.75 (morning crunch — moderate bar)
• 9 AM - 10 PM: threshold 0.45 (daytime — lower bar, let alerts through)
• 10 PM - 11 PM: threshold 0.35 (wind-down — only high urgency)
• 11 PM - 12 AM: threshold 0.85 (late night — back to high bar)
• 12 AM - 7 AM: threshold 0.90 (sleep time — very high bar)

Modifiers:

• - Weekend: +0.10 urgency (weekends are boring, lower bar for engagement)
• Clustering prevention: If message sent <10 min ago, -0.20 urgency (space out messages)
• Daily fatigue: If 10+ messages today, +0.20 urgency threshold (user tired, fewer but higher-quality alerts)
• Random jitter: ±5% urgency (avoid machine-like precision)

Send logic:
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Customization point: Modify TIME_OF_DAY_THRESHOLDS and modifier constants in variable_timing.py:

TIME_OF_DAY_THRESHOLDS = {
    (0, 7): 0.90,    # midnight - 7 AM
    (7, 9): 0.75,    # 7 - 9 AM
    (9, 22): 0.45,   # 9 AM - 10 PM
    (22, 23): 0.35,  # 10 - 11 PM
    (23, 24): 0.85,  # 11 PM - midnight
}
MODIFIERS = {
    'weekend': 0.10,
    'clustering_prevention': 0.20,
    'daily_fatigue_step': 0.20,
}

---

System 4: Micro-Initiations — Ambient Awareness Pings

What: Unprompted messages when conditions are met. Not triggered by market events — triggered by meta-state.

Pools:

Pool	Trigger	Message
QUIET_MARKET	Vol <0.3% all day, no trades	"Quiet day. Markets are sleeping."
WEEKEND

Saturday/Sunday, no meetings | "Weekend vibes. You're off the hook." |
| MONDAY | Monday 6 AM, fresh week | "Monday morning. Week's open for business." |
| FRIDAY | Friday 4 PM, close approaching | "Friday close. Have a good weekend." |
| HOLIDAY_AWARENESS | US holiday today | "Holiday today. Markets are light." |
| GOOD_STREAK | 5+ consecutive +% days | "On a roll. Good week for you." |
| BAD_STREAK | 5+ consecutive -% days | "Rough stretch. It'll turn around." |
| ABSENCE | No user engagement 24+ hours | "Checking in. Things have been quiet." |

Cadence:

• - Max 2 micro-initiations per week per user
• Skip on busy days (3+ regular alerts already sent that day)
• No repeats within 2 weeks (hash-based dedup: sha256(pool + date) in daily context)

US Holiday calendar (built-in awareness):
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Customization point: Add pools in micro_initiations.py:

MICRO_POOLS = {
    'QUIET_MARKET': {
        'condition': lambda ctx: ctx.vol < 0.3 and ctx.trade_count == 0,
        'messages': ["Quiet day. Markets are sleeping.", "No action today."],
    },
    'YOUR_POOL': {
        'condition': lambda ctx: your_logic_here(),
        'messages': ["Message 1", "Message 2"],
    }
}

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System 5: Context Buffer — Daily Memory + Back-References

What: Messages can reference earlier messages from today. "That Kalshi/PM divergence I flagged at 9 AM widened to 15%." This makes the agent feel like it's thinking about past events, not just firing isolated alerts.

Per-trigger back-reference generation:

• - crossplatform: If divergence was flagged earlier, compare current spread to earlier spread
• portfolio: If earlier portfolio message, show change since then
• xsignals: If same signal fired earlier, acknowledge the repeat with new data
• edge: Compare edge size to earlier edge on same market

Persistence: JSON file at ~/.openclaw/state/daily_context.json:
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Auto-reset: At midnight (UTC), clear context for fresh day.

Back-reference example:
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Customization point: Add back-reference logic for new trigger types in context_buffer.py:

def generate_backreference(trigger_type, current_data, history):
    if trigger_type == 'cross_platform':
        earlier = find_similar_trigger(history, 'cross_platform')
        if earlier:
            spread_change = current_data['spread'] - earlier['spread']
            return f"That spread I flagged {time_ago(earlier)} widened to {spread_change}%."
    return None

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System 6: Response Tracker — Engagement Adaptation

What: Track user's response patterns. If user engages with 70%+ of messages, urgency stays high. If engagement <10%, adjust urgency down or suggest tuning.

Metrics per trigger type:

• - sends: Count of messages sent
• engagements: Count of messages user responded to (within 1-hour window)
• ignores: Count of messages user didn't respond to
• avgresponsetime: Average time from message to user response (minutes)

1-hour engagement window: If user responds to a message within 60 minutes, count it as engagement. After 60 min, assume ignored.

Urgency modifier:

• - Engagement ≥70%: Multiply urgency by 1.3 (user likes these alerts, send more)
• Engagement 40-70%: Urgency × 1.0 (balanced)
• Engagement <10%: Urgency × 0.5 (user ignoring, tone it down)

Adaptation suggestion: After 10+ sends of a trigger type with <20% engagement, log:
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Persistence: ~/.openclaw/state/response_tracker.json:
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Customization point: Adjust engagement thresholds and modifier multipliers in response_tracker.py:

ENGAGEMENT_THRESHOLDS = {
    'high': 0.70,    # ≥70% → 1.3x urgency
    'low': 0.10,     # <10% → 0.5x urgency
}
URGENCY_MULTIPLIERS = {
    'high': 1.3,
    'low': 0.5,
}
SUGGESTION_TRIGGERS = {
    'min_sends': 10,
    'max_engagement_for_suggestion': 0.20,
}

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OpenClaw Ecosystem Integration

The Personality Engine works with any OpenClaw agent — it's domain-agnostic. Designed alongside the Prediction Market Trading Stack but applicable to any proactive agent that sends alerts, digests, or notifications.

Install the complete Prediction Market Trading Stack:
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Integration with OpenClaw Agents

Step 1: Import the engine

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Step 2: Hook into proactive trigger system

In your agent's trigger handler (e.g., proactive_agent.py):

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Step 3: Hook engagement tracker

When user responds to a message:

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Step 4: Run micro-initiations

Add a separate cron job (every 30 min, low-overhead):

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Step 5: Daily context reset

At midnight, engine auto-resets context. Manually trigger if needed:

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Use Cases Beyond Trading

While the default voice pools and thresholds are tuned for prediction market trading, every system is designed for domain adaptation:

Domain	Editorial Voice	Silence Rules	Micro-Initiations
Trading (default)	Market commentary, edge opinions	Skip flat days, stale divergences	Quiet market, good/bad streaks
Personal Assistant

Task prioritization opinions | Skip low-urgency reminders | "Quiet week. Inbox is clean." |
| DevOps/Monitoring | Incident severity opinions | Skip routine health checks | "Uptime streak: 30 days." |
| Sales/CRM | Deal stage opinions | Skip stale leads | "Pipeline looking thin this quarter." |
| Content/Social | Engagement commentary | Skip low-performing posts | "Your last post is outperforming." |

To adapt: swap the VOICE_POOLS dict in editorial_voice.py, update thresholds in selective_silence.py, and add domain-specific MICRO_POOLS in micro_initiations.py. See references/customization.md for full guide.

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File Structure

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Customization Quick-Start

Scenario 1: Your agent fires 50 alerts/day and user ignores most

1. 1. Increase silence thresholds (selective_silence.py): Higher bars for what counts as "worth sending"
2. Lower variabletiming thresholds (variable_timing.py): Fewer messages slip through outside peak hours
3. Check editorialvoice: Opinions might not match user's style
4. Review response_tracker: Which trigger types have <20% engagement? Mute those.

Scenario 2: Your agent never initiates, only reacts

1. 1. Customize micro_initiations pools: Add domain-specific conditions

   'LOW_VOLATILITY_OPPORTUNITY': {
       'condition': lambda ctx: ctx.vol < 0.2 and ctx.last_edge_size > 2.0,
       'messages': ["Calm market, good edge conditions. Might be time to scout."],
   }
   

1. 2. Tune MICRO_CADENCE: Increase max from 2/week to 3-4/week if user loves it

Scenario 3: Your agent's opinions feel generic

1. 1. Open editorial_voice.py and expand voice pools per trigger
2. Add market-specific opinions:

   'VOICE_POOLS': {
       'cross_platform': {
           'big_divergence': [
               "Big divergence. One of these markets is wrong.",
               "Spreads are blown out. Arb opportunity.",
               "Thick divergence — reality check time.",
           ],
           ...
   

1. 3. Vary by user profile (trader vs long-term investor) by passing user_profile to engine init

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Performance & State Management

State files (user's home directory):

• - ~/.openclaw/state/daily_context.json (~5KB, resets daily)
• INLINECODE25 (~2KB, persistent)

Engine overhead:

• - editorialvoice: <5ms (dict lookup)
• selectivesilence: <10ms (threshold comparisons)
• variabletiming: <15ms (urgency calculation + time lookup)
• microinitiations: <20ms (condition evaluation)
• contextbuffer: <5ms (history lookup)
• responsetracker: <5ms (metrics lookup)

Total pipeline: ~60ms per trigger (negligible for async message delivery)

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License

Part of the OpenClaw portfolio. Use freely in any agent.

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Version History

v1.0.0 (2026-03-09)

• - Initial 6-system framework
• Default configuration tuned for prediction market trading agents
• All systems designed for domain adaptation — swap voice pools, thresholds, and micro-initiation conditions for any use case

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Feedback & Issues

Found a bug? Have a feature request? Want to share results?

• - GitHub Issues: github.com/kingmadellc/openclaw-prediction-stack/issues
• X/Twitter: @KingMadeLLC

Part of the OpenClaw Prediction Stack — the first prediction market skill suite on ClawHub.