--- name: Personality Engine description: "Six-system behavior engine that makes any OpenClaw agent feel alive. Editorial voice injects opinions. Selective silence knows when NOT to talk. Variable timing scores urgency with time-of-day awareness. Micro-initiations send ambient pings. Context buffer enables back-references to earlier messages. Response tracker adapts to engagement patterns. Domain-agnostic — works with trading agents, personal assistants, DevOps monitors, or any proactive agent. Part of the OpenClaw Prediction Market Trading Stack with default trading configuration." --- # 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 ``` Trigger fires → engine.py orchestrator ↓ selective_silence (should we stay silent?) ↓ urgency_compute (how urgent is this 0.0-1.0?) ↓ engagement_modifier (adjust for user response patterns) ↓ variable_timing (schedule delivery based on urgency + time of day) ↓ context_buffer (add back-references to earlier messages today) ↓ editorial_voice (inject personality / opinions) ↓ dedup (avoid repeats within rolling window) ↓ send → iMessage (or other transport) ``` Plus two ambient systems: - **micro_initiations**: Unprompted pings when conditions are met (quiet market, good streak, absence detected) - **response_tracker**: Monitors engagement; adjusts urgency + suggests tuning --- ## 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. --- ## 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**: - **morning_is_boring**: If market vol <0.5%, no divergences, no edges → skip - **divergence_is_stale**: If last message on this topic was <3 hours ago AND spread hasn't moved >0.5% → skip - **signals_are_noise**: If all signals have confidence <0.65 AND no position matches → skip - **edge_is_weak**: If all edges <1% → skip - **portfolio_is_flat**: 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: `"Skipped the brief — nothing worth your attention."` **Customization point**: Adjust thresholds in `selective_silence.py`: ```python 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 } ``` --- ## 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 - **x_signals**: (confidence * 0.8) + (position_match ? 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**: ``` adjusted_urgency = base_urgency * engagement_modifier ± jitter if adjusted_urgency >= time_of_day_threshold: schedule_send(now or delayed based on urgency) else: hold for next trigger ``` **Customization point**: Modify `TIME_OF_DAY_THRESHOLDS` and modifier constants in `variable_timing.py`: ```python 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): ```python HOLIDAYS = { (1, 1): "New Year's Day", (1, 20): "MLK Day", (2, 17): "Presidents' Day", (3, 17): "St. Patrick's Day", (5, 26): "Memorial Day", (7, 4): "Independence Day", (9, 1): "Labor Day", (10, 13): "Columbus Day", (11, 11): "Veterans Day", (11, 27): "Thanksgiving", (12, 25): "Christmas", } ``` **Customization point**: Add pools in `micro_initiations.py`: ```python 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"], } } ``` --- ## 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**: - **cross_platform**: If divergence was flagged earlier, compare current spread to earlier spread - **portfolio**: If earlier portfolio message, show change since then - **x_signals**: 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`: ```json { "date": "2026-03-09", "messages": [ { "time": "09:15", "trigger": "cross_platform", "spread": 7.2, "markets": ["kalshi", "polymarket"], "message_id": "msg_abc123" }, { "time": "14:30", "trigger": "portfolio", "pnl": 12.5, "message_id": "msg_def456" } ], "silence_count": 1, "micro_count": 0, "sent_count": 5 } ``` **Auto-reset**: At midnight (UTC), clear context for fresh day. **Back-reference example**: ``` Earlier (9:15 AM): "Mild divergence. Kalshi 52%, Poly 48%." Later (2:30 PM): "That Kalshi/Poly spread I flagged this morning widened to 7% — now 55/48. Worth watching." ``` **Customization point**: Add back-reference logic for new trigger types in `context_buffer.py`: ```python 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 ``` --- ## 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 - **avg_response_time**: 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: ``` ⚠️ ADJUSTMENT SUGGESTION: Trigger: x_signals Sends: 12 | Engagement: 8% | Avg response time: Never Consider: → Lower signal confidence floor (currently 0.65) → Reduce frequency (increase silence thresholds) → Check if message editorial voice is mismatched ``` **Persistence**: `~/.openclaw/state/response_tracker.json`: ```json { "cross_platform": { "sends": 15, "engagements": 9, "ignores": 6, "avg_response_time": 23.5, "last_engagement": "2026-03-09T14:32:00Z" }, "x_signals": { "sends": 12, "engagements": 1, "ignores": 11, "avg_response_time": null, "last_engagement": null } } ``` **Customization point**: Adjust engagement thresholds and modifier multipliers in `response_tracker.py`: ```python 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, } ``` --- ## 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:** ```bash clawhub install kalshalyst kalshi-command-center polymarket-command-center prediction-market-arbiter xpulse portfolio-drift-monitor market-morning-brief personality-engine ``` ## Integration with OpenClaw Agents ### Step 1: Import the engine ```python from personality_engine.engine import PersonalityEngine engine = PersonalityEngine(user_id="user@example.com") ``` ### Step 2: Hook into proactive trigger system In your agent's trigger handler (e.g., `proactive_agent.py`): ```python async def fire_trigger(trigger_type, data): # Your normal trigger logic message_content = generate_message(trigger_type, data) # Pass through personality engine should_send, scheduled_message = await engine.process_trigger( trigger_type=trigger_type, raw_message=message_content, market_data=data, urgency_context={'vol': market_vol, 'pnl': portfolio_pnl, ...} ) if should_send: if scheduled_message.delayed: schedule_send(scheduled_message.content, delay=scheduled_message.delay_seconds) else: send_now(scheduled_message.content) else: log_silence_skip(trigger_type) ``` ### Step 3: Hook engagement tracker When user responds to a message: ```python def handle_user_response(message_id, trigger_type, response_time_seconds): engine.response_tracker.log_engagement(trigger_type, response_time_seconds) ``` ### Step 4: Run micro-initiations Add a separate cron job (every 30 min, low-overhead): ```python async def micro_initiations_check(): micro_message = await engine.check_micro_initiations(context={ 'vol': market_vol, 'trade_count': trades_today, 'user_absence_hours': hours_since_last_engagement, 'portfolio_streak': consecutive_days, }) if micro_message: send_now(micro_message) ``` ### Step 5: Daily context reset At midnight, engine auto-resets context. Manually trigger if needed: ```python engine.context_buffer.reset_daily() ``` --- ## 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. --- ## File Structure ``` personality-engine/ ├── SKILL.md # This file ├── scripts/ │ ├── __init__.py │ ├── engine.py # Main orchestrator │ ├── editorial_voice.py # Opinion injection │ ├── selective_silence.py # Content quality checks │ ├── variable_timing.py # Urgency + time-of-day │ ├── micro_initiations.py # Ambient pings │ ├── context_buffer.py # Daily memory │ └── response_tracker.py # Engagement tracking ├── references/ │ ├── systems-overview.md # Architecture diagram + flow │ └── customization.md # Per-system customization guide └── examples/ └── integration-example.py # Copy-paste integration template ``` --- ## Customization Quick-Start ### Scenario 1: Your agent fires 50 alerts/day and user ignores most 1. **Increase silence thresholds** (`selective_silence.py`): Higher bars for what counts as "worth sending" 2. **Lower variable_timing thresholds** (`variable_timing.py`): Fewer messages slip through outside peak hours 3. **Check editorial_voice**: 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. **Customize micro_initiations pools**: Add domain-specific conditions ```python '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."], } ``` 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. Open `editorial_voice.py` and expand voice pools per trigger 2. Add market-specific opinions: ```python '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.", ], ... ``` 3. Vary by user profile (trader vs long-term investor) by passing `user_profile` to engine init --- ## Performance & State Management **State files** (user's home directory): - `~/.openclaw/state/daily_context.json` (~5KB, resets daily) - `~/.openclaw/state/response_tracker.json` (~2KB, persistent) **Engine overhead**: - editorial_voice: <5ms (dict lookup) - selective_silence: <10ms (threshold comparisons) - variable_timing: <15ms (urgency calculation + time lookup) - micro_initiations: <20ms (condition evaluation) - context_buffer: <5ms (history lookup) - response_tracker: <5ms (metrics lookup) **Total pipeline**: ~60ms per trigger (negligible for async message delivery) --- ## License Part of the OpenClaw portfolio. Use freely in any agent. --- ## 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 --- ## Feedback & Issues Found a bug? Have a feature request? Want to share results? - **GitHub Issues**: [github.com/kingmadellc/openclaw-prediction-stack/issues](https://github.com/kingmadellc/openclaw-prediction-stack/issues) - **X/Twitter**: [@KingMadeLLC](https://x.com/KingMadeLLC) Part of the **OpenClaw Prediction Stack** — the first prediction market skill suite on ClawHub.

Personality Engine

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