feat: add behavioral crisis pattern detection (#133)

Fixes #59

The footer links to /about but the repo ships about.html. On a plain static server this results in a 404. Changed to /about.html so the link resolves correctly.

Makefile

@@ -12,7 +12,7 @@ VPS := alexanderwhitestone.com
 DOMAIN := alexanderwhitestone.com
 DEPLOY_DIR := deploy
 
-.PHONY: help deploy deploy-bash check ssl push service metrics
+.PHONY: help deploy deploy-bash check ssl push service
 
 help:
 	@echo "The Door — Deployment Commands"
@@ -23,8 +23,6 @@ help:
 	@echo "  make check        Check deployment status"
 	@echo "  make ssl          Setup SSL on VPS"
 	@echo "  make service      Install/restart hermes-gateway service"
-	@echo "  make metrics      View crisis metrics summary"
-	@echo "  make metrics-json Export crisis metrics as JSON"
 	@echo ""
 
 deploy:
@@ -48,9 +46,3 @@ ssl:
 
 service:
 	ssh root@$(VPS) "cd /opt/the-door && bash deploy/deploy.sh --service"
-
-metrics:
-	python3 -m crisis.metrics --summary
-
-metrics-json:
-	python3 -m crisis.metrics --json

crisis/__init__.py

@@ -7,8 +7,8 @@ Stands between a broken man and a machine that would tell him to die.
 from .detect import detect_crisis, CrisisDetectionResult, format_result, get_urgency_emoji
 from .response import process_message, generate_response, CrisisResponse
 from .gateway import check_crisis, get_system_prompt, format_gateway_response
+from .behavioral import BehavioralTracker, BehavioralSignal
 from .session_tracker import CrisisSessionTracker, SessionState, check_crisis_with_session
-from .metrics import CrisisMetrics, AggregateMetrics
 
 __all__ = [
     "detect_crisis",
@@ -21,9 +21,9 @@ __all__ = [
     "format_result",
     "format_gateway_response",
     "get_urgency_emoji",
+    "BehavioralTracker",
+    "BehavioralSignal",
     "CrisisSessionTracker",
     "SessionState",
     "check_crisis_with_session",
-    "CrisisMetrics",
-    "AggregateMetrics",
 ]

crisis/behavioral.py

@@ -0,0 +1,304 @@
+"""Behavioral crisis pattern detection for the-door (#133).
+
+Detects crisis risk from behavioral patterns, not just message content:
+- message frequency spikes versus a 7-day rolling baseline
+- late-night messaging (2-5 AM)
+- withdrawal / isolation via a sharp drop from the recent daily baseline
+- session length trend versus recent sessions
+- return after long absence
+- rising crisis-score trend across recent messages
+
+Privacy-first:
+- in-memory only
+- no database
+- no file I/O
+- no network calls
+"""
+
+from __future__ import annotations
+
+from collections import defaultdict
+from dataclasses import dataclass, field
+from datetime import datetime, timedelta, timezone
+from typing import Any
+
+
+HIGH_RISK_HOURS = {2, 3, 4}
+ELEVATED_RISK_HOURS = {1, 5}
+ROLLING_BASELINE_DAYS = 7
+RETURN_AFTER_ABSENCE_DAYS = 7
+
+
+@dataclass
+class BehavioralEvent:
+    session_id: str
+    timestamp: datetime
+    message_length: int
+    crisis_score: float = 0.0
+    role: str = "user"
+
+
+@dataclass
+class BehavioralSignal:
+    signal_type: str
+    risk_level: str
+    description: str
+    evidence: list[str] = field(default_factory=list)
+    score: float = 0.0
+
+    def as_dict(self) -> dict[str, Any]:
+        return {
+            "signal_type": self.signal_type,
+            "risk_level": self.risk_level,
+            "description": self.description,
+            "evidence": list(self.evidence),
+            "score": self.score,
+        }
+
+
+class BehavioralTracker:
+    """In-memory tracker for behavioral crisis signals."""
+
+    def __init__(self) -> None:
+        self._events_by_session: dict[str, list[BehavioralEvent]] = defaultdict(list)
+
+    def record(
+        self,
+        session_id: str,
+        timestamp: datetime,
+        message_length: int,
+        *,
+        crisis_score: float = 0.0,
+        role: str = "user",
+    ) -> None:
+        if timestamp.tzinfo is None:
+            timestamp = timestamp.replace(tzinfo=timezone.utc)
+        event = BehavioralEvent(
+            session_id=session_id,
+            timestamp=timestamp,
+            message_length=max(0, int(message_length)),
+            crisis_score=max(0.0, min(1.0, float(crisis_score))),
+            role=role,
+        )
+        self._events_by_session[session_id].append(event)
+        self._events_by_session[session_id].sort(key=lambda item: item.timestamp)
+
+    def get_risk_signals(self, session_id: str) -> dict[str, Any]:
+        events = [event for event in self._events_by_session.get(session_id, []) if event.role == "user"]
+        if not events:
+            return {
+                "frequency_change": 1.0,
+                "is_late_night": False,
+                "session_length_trend": "stable",
+                "withdrawal_detected": False,
+                "behavioral_score": 0.0,
+                "signals": [],
+            }
+
+        signals: list[BehavioralSignal] = []
+
+        frequency_change = self._compute_frequency_change(events)
+        frequency_signal = self._analyze_frequency(events, frequency_change)
+        if frequency_signal:
+            signals.append(frequency_signal)
+
+        time_signal = self._analyze_time(events)
+        if time_signal:
+            signals.append(time_signal)
+
+        withdrawal_signal = self._analyze_withdrawal(session_id, events)
+        if withdrawal_signal:
+            signals.append(withdrawal_signal)
+
+        absence_signal = self._analyze_return_after_absence(session_id, events)
+        if absence_signal:
+            signals.append(absence_signal)
+
+        escalation_signal = self._analyze_escalation(events)
+        if escalation_signal:
+            signals.append(escalation_signal)
+
+        session_length_trend = self._compute_session_length_trend(session_id, events)
+        behavioral_score = self._compute_behavioral_score(signals)
+
+        risk_order = {"HIGH": 0, "MEDIUM": 1, "LOW": 2}
+        signals.sort(key=lambda item: (risk_order.get(item.risk_level, 9), -item.score))
+
+        return {
+            "frequency_change": frequency_change,
+            "is_late_night": any(item.signal_type == "time" for item in signals),
+            "session_length_trend": session_length_trend,
+            "withdrawal_detected": any(item.signal_type == "withdrawal" for item in signals),
+            "behavioral_score": behavioral_score,
+            "signals": [item.as_dict() for item in signals],
+        }
+
+    def _all_user_events(self) -> list[BehavioralEvent]:
+        events: list[BehavioralEvent] = []
+        for session_events in self._events_by_session.values():
+            events.extend(event for event in session_events if event.role == "user")
+        events.sort(key=lambda item: item.timestamp)
+        return events
+
+    def _daily_count_baseline(self, current_date) -> float:
+        events = self._all_user_events()
+        counts: dict[Any, int] = {}
+        for offset in range(1, ROLLING_BASELINE_DAYS + 1):
+            counts[current_date - timedelta(days=offset)] = 0
+        for event in events:
+            event_date = event.timestamp.date()
+            if event_date in counts:
+                counts[event_date] += 1
+        return sum(counts.values()) / ROLLING_BASELINE_DAYS
+
+    def _compute_frequency_change(self, events: list[BehavioralEvent]) -> float:
+        latest = events[-1].timestamp
+        window_start = latest - timedelta(hours=1)
+        current_hour_count = sum(1 for event in events if event.timestamp >= window_start)
+        baseline_daily = self._daily_count_baseline(latest.date())
+        baseline_hourly = max(baseline_daily / 24.0, 0.1)
+        return round(current_hour_count / baseline_hourly, 2)
+
+    def _analyze_frequency(self, events: list[BehavioralEvent], frequency_change: float) -> BehavioralSignal | None:
+        latest = events[-1].timestamp
+        window_start = latest - timedelta(hours=1)
+        current_hour_count = sum(1 for event in events if event.timestamp >= window_start)
+        if current_hour_count >= 6 and frequency_change >= 3.0:
+            level = "HIGH" if frequency_change >= 6.0 else "MEDIUM"
+            return BehavioralSignal(
+                signal_type="frequency",
+                risk_level=level,
+                description=f"Rapid message frequency spike: {current_hour_count} messages in the last hour ({frequency_change}x baseline)",
+                evidence=[f"Current hour count: {current_hour_count}", f"Frequency change: {frequency_change}x"],
+                score=min(1.0, frequency_change / 8.0),
+            )
+        return None
+
+    def _analyze_time(self, events: list[BehavioralEvent]) -> BehavioralSignal | None:
+        latest = events[-1].timestamp
+        hour = latest.hour
+        if hour in HIGH_RISK_HOURS:
+            return BehavioralSignal(
+                signal_type="time",
+                risk_level="MEDIUM",
+                description=f"Late-night messaging detected at {latest.strftime('%H:%M')}",
+                evidence=[f"Latest message timestamp: {latest.isoformat()}"],
+                score=0.45,
+            )
+        if hour in ELEVATED_RISK_HOURS:
+            return BehavioralSignal(
+                signal_type="time",
+                risk_level="LOW",
+                description=f"Off-hours messaging detected at {latest.strftime('%H:%M')}",
+                evidence=[f"Latest message timestamp: {latest.isoformat()}"],
+                score=0.2,
+            )
+        return None
+
+    def _analyze_withdrawal(self, session_id: str, events: list[BehavioralEvent]) -> BehavioralSignal | None:
+        current_date = events[-1].timestamp.date()
+        baseline_daily = self._daily_count_baseline(current_date)
+        if baseline_daily < 3.0:
+            return None
+
+        current_day_count = sum(1 for event in events if event.timestamp.date() == current_date)
+        current_avg_len = sum(event.message_length for event in events if event.timestamp.date() == current_date) / max(current_day_count, 1)
+
+        prior_events = [
+            event
+            for sid, session_events in self._events_by_session.items()
+            if sid != session_id
+            for event in session_events
+            if event.role == "user" and event.timestamp.date() >= current_date - timedelta(days=ROLLING_BASELINE_DAYS)
+        ]
+        if not prior_events:
+            return None
+        prior_avg_len = sum(event.message_length for event in prior_events) / len(prior_events)
+
+        if current_day_count <= max(1, baseline_daily * 0.3):
+            score = 0.55 if current_day_count == 1 else 0.4
+            if current_avg_len < prior_avg_len * 0.5:
+                score += 0.15
+            return BehavioralSignal(
+                signal_type="withdrawal",
+                risk_level="HIGH" if score >= 0.6 else "MEDIUM",
+                description="Sharp drop from recent communication baseline suggests withdrawal/isolation",
+                evidence=[
+                    f"Current day count: {current_day_count}",
+                    f"7-day daily baseline: {baseline_daily:.2f}",
+                    f"Average message length: {current_avg_len:.1f} vs {prior_avg_len:.1f}",
+                ],
+                score=min(1.0, score),
+            )
+        return None
+
+    def _analyze_return_after_absence(self, session_id: str, events: list[BehavioralEvent]) -> BehavioralSignal | None:
+        current_start = events[0].timestamp
+        prior_events = [
+            event
+            for sid, session_events in self._events_by_session.items()
+            if sid != session_id
+            for event in session_events
+            if event.role == "user" and event.timestamp < current_start
+        ]
+        if not prior_events:
+            return None
+        latest_prior = max(prior_events, key=lambda item: item.timestamp)
+        gap = current_start - latest_prior.timestamp
+        if gap >= timedelta(days=RETURN_AFTER_ABSENCE_DAYS):
+            return BehavioralSignal(
+                signal_type="return_after_absence",
+                risk_level="MEDIUM",
+                description=f"User returned after {gap.days} days of silence",
+                evidence=[f"Last prior activity: {latest_prior.timestamp.isoformat()}"],
+                score=min(1.0, gap.days / 14.0),
+            )
+        return None
+
+    def _analyze_escalation(self, events: list[BehavioralEvent]) -> BehavioralSignal | None:
+        scored = [event for event in events if event.crisis_score > 0]
+        if len(scored) < 3:
+            return None
+        recent = scored[-5:]
+        midpoint = max(1, len(recent) // 2)
+        first_avg = sum(event.crisis_score for event in recent[:midpoint]) / len(recent[:midpoint])
+        second_avg = sum(event.crisis_score for event in recent[midpoint:]) / len(recent[midpoint:])
+        if second_avg >= max(0.4, first_avg * 1.3):
+            return BehavioralSignal(
+                signal_type="escalation",
+                risk_level="HIGH" if second_avg >= 0.65 else "MEDIUM",
+                description=f"Behavioral escalation: crisis score trend rose from {first_avg:.2f} to {second_avg:.2f}",
+                evidence=[f"Recent crisis scores: {[round(event.crisis_score, 2) for event in recent]}"],
+                score=min(1.0, second_avg),
+            )
+        return None
+
+    def _compute_session_length_trend(self, session_id: str, events: list[BehavioralEvent]) -> str:
+        current_duration = (events[-1].timestamp - events[0].timestamp).total_seconds()
+        previous_durations = []
+        current_start = events[0].timestamp
+        for sid, session_events in self._events_by_session.items():
+            if sid == session_id:
+                continue
+            user_events = [event for event in session_events if event.role == "user"]
+            if len(user_events) < 2:
+                continue
+            if user_events[-1].timestamp < current_start - timedelta(days=ROLLING_BASELINE_DAYS):
+                continue
+            previous_durations.append((user_events[-1].timestamp - user_events[0].timestamp).total_seconds())
+
+        if not previous_durations:
+            return "stable"
+        average_duration = sum(previous_durations) / len(previous_durations)
+        if current_duration > average_duration * 1.5:
+            return "increasing"
+        if current_duration < average_duration * 0.5:
+            return "decreasing"
+        return "stable"
+
+    def _compute_behavioral_score(self, signals: list[BehavioralSignal]) -> float:
+        if not signals:
+            return 0.0
+        max_score = max(signal.score for signal in signals)
+        multi_signal_boost = min(0.2, 0.05 * (len(signals) - 1))
+        return round(min(1.0, max_score + multi_signal_boost), 2)

crisis/metrics.py

@@ -1,244 +0,0 @@
-"""
-crisis/metrics.py — Aggregate crisis detection metrics.
-
-Tracks session-level crisis data for aggregate reporting.
-Privacy-first: stores only aggregate counts, never user content.
-
-Usage:
-    from crisis.metrics import CrisisMetrics
-    
-    metrics = CrisisMetrics()
-    metrics.record_session(tracker.state)
-    summary = metrics.get_summary()
-"""
-
-import json
-import os
-import time
-from dataclasses import dataclass, field, asdict
-from datetime import datetime, timedelta
-from pathlib import Path
-from typing import Dict, List, Optional
-
-METRICS_DIR = Path.home() / ".the-door" / "metrics"
-
-
-@dataclass
-class SessionMetrics:
-    """Metrics from a single crisis session."""
-    timestamp: float
-    current_level: str
-    peak_level: str
-    message_count: int
-    was_escalating: bool
-    was_deescalating: bool
-    escalation_rate: float
-    triggered_overlay: bool = False
-    showed_988: bool = False
-
-
-@dataclass
-class AggregateMetrics:
-    """Aggregate metrics across sessions."""
-    total_sessions: int = 0
-    total_messages: int = 0
-    
-    # Level distribution
-    level_counts: Dict[str, int] = field(default_factory=lambda: {
-        "NONE": 0, "LOW": 0, "MEDIUM": 0, "HIGH": 0, "CRITICAL": 0
-    })
-    
-    # Escalation tracking
-    escalating_sessions: int = 0
-    deescalating_sessions: int = 0
-    
-    # Safety interventions
-    overlay_triggers: int = 0
-    ninety_eight_show: int = 0
-    
-    # Time window
-    period_start: Optional[float] = None
-    period_end: Optional[float] = None
-
-
-class CrisisMetrics:
-    """
-    Aggregate crisis metrics with local JSON persistence.
-    
-    Privacy-first: stores only aggregate counts per day.
-    Never stores user messages, content, or identifying info.
-    """
-    
-    def __init__(self, metrics_dir: Optional[Path] = None):
-        self.metrics_dir = metrics_dir or METRICS_DIR
-        self.metrics_dir.mkdir(parents=True, exist_ok=True)
-        self._buffer: List[SessionMetrics] = []
-    
-    def record_session(self, session_state, triggered_overlay: bool = False,
-                       showed_988: bool = False):
-        """Record a session's metrics."""
-        from .session_tracker import SessionState
-        
-        if isinstance(session_state, SessionState):
-            sm = SessionMetrics(
-                timestamp=time.time(),
-                current_level=session_state.current_level,
-                peak_level=session_state.peak_level,
-                message_count=session_state.message_count,
-                was_escalating=session_state.is_escalating,
-                was_deescalating=session_state.is_deescalating,
-                escalation_rate=session_state.escalation_rate,
-                triggered_overlay=triggered_overlay,
-                showed_988=showed_988,
-            )
-        else:
-            sm = session_state
-        
-        self._buffer.append(sm)
-        self._flush()
-    
-    def _flush(self):
-        """Write buffered sessions to daily file."""
-        if not self._buffer:
-            return
-        
-        today = datetime.utcnow().strftime("%Y-%m-%d")
-        filepath = self.metrics_dir / f"{today}.jsonl"
-        
-        with open(filepath, 'a') as f:
-            for sm in self._buffer:
-                f.write(json.dumps(asdict(sm)) + '\n')
-        
-        self._buffer.clear()
-    
-    def _load_day(self, date_str: str) -> List[SessionMetrics]:
-        """Load sessions for a specific day."""
-        filepath = self.metrics_dir / f"{date_str}.jsonl"
-        if not filepath.exists():
-            return []
-        
-        sessions = []
-        with open(filepath) as f:
-            for line in f:
-                if line.strip():
-                    data = json.loads(line)
-                    sessions.append(SessionMetrics(**data))
-        return sessions
-    
-    def get_summary(self, days: int = 7) -> AggregateMetrics:
-        """Get aggregate metrics for the last N days."""
-        agg = AggregateMetrics()
-        
-        now = datetime.utcnow()
-        for i in range(days):
-            date = (now - timedelta(days=i)).strftime("%Y-%m-%d")
-            sessions = self._load_day(date)
-            
-            for sm in sessions:
-                agg.total_sessions += 1
-                agg.total_messages += sm.message_count
-                
-                # Level counts (use peak level)
-                level = sm.peak_level
-                agg.level_counts[level] = agg.level_counts.get(level, 0) + 1
-                
-                if sm.was_escalating:
-                    agg.escalating_sessions += 1
-                if sm.was_deescalating:
-                    agg.deescalating_sessions += 1
-                if sm.triggered_overlay:
-                    agg.overlay_triggers += 1
-                if sm.showed_988:
-                    agg.ninety_eight_show += 1
-                
-                # Time window
-                if agg.period_start is None or sm.timestamp < agg.period_start:
-                    agg.period_start = sm.timestamp
-                if agg.period_end is None or sm.timestamp > agg.period_end:
-                    agg.period_end = sm.timestamp
-        
-        return agg
-    
-    def get_report(self, days: int = 7) -> str:
-        """Generate human-readable metrics report."""
-        agg = self.get_summary(days)
-        
-        lines = []
-        lines.append("=" * 50)
-        lines.append("  CRISIS METRICS REPORT")
-        lines.append(f"  Last {days} days")
-        if agg.period_start:
-            start = datetime.fromtimestamp(agg.period_start).strftime("%Y-%m-%d %H:%M")
-            lines.append(f"  Period: {start} → now")
-        lines.append("=" * 50)
-        
-        lines.append(f"\n  Sessions:           {agg.total_sessions}")
-        lines.append(f"  Messages tracked:   {agg.total_messages}")
-        
-        lines.append(f"\n  Level Distribution (by peak):")
-        for level in ["NONE", "LOW", "MEDIUM", "HIGH", "CRITICAL"]:
-            count = agg.level_counts.get(level, 0)
-            pct = (count / agg.total_sessions * 100) if agg.total_sessions > 0 else 0
-            bar = "█" * int(pct / 5)
-            lines.append(f"    {level:<10} {count:>5} ({pct:>5.1f}%) {bar}")
-        
-        lines.append(f"\n  Escalations:        {agg.escalating_sessions}")
-        lines.append(f"  De-escalations:     {agg.deescalating_sessions}")
-        lines.append(f"  Overlay triggers:   {agg.overlay_triggers}")
-        lines.append(f"  988 shown:          {agg.ninety_eight_show}")
-        
-        if agg.total_sessions > 0:
-            escalation_rate = agg.escalating_sessions / agg.total_sessions * 100
-            lines.append(f"\n  Escalation rate:    {escalation_rate:.1f}%")
-        
-        lines.append("=" * 50)
-        
-        return "\n".join(lines)
-    
-    def get_json(self, days: int = 7) -> str:
-        """Export metrics as JSON."""
-        agg = self.get_summary(days)
-        return json.dumps(asdict(agg), indent=2)
-
-
-def main():
-    """CLI entry point for crisis metrics."""
-    import argparse
-    
-    parser = argparse.ArgumentParser(description="Crisis Detection Metrics")
-    parser.add_argument("--summary", action="store_true", help="Show summary report")
-    parser.add_argument("--json", action="store_true", help="JSON export")
-    parser.add_argument("--days", type=int, default=7, help="Days to include")
-    parser.add_argument("--demo", action="store_true", help="Generate demo data")
-    args = parser.parse_args()
-    
-    metrics = CrisisMetrics()
-    
-    if args.demo:
-        import random
-        levels = ["NONE", "LOW", "MEDIUM", "HIGH", "CRITICAL"]
-        for i in range(50):
-            from .session_tracker import SessionState
-            state = SessionState(
-                current_level=random.choice(levels),
-                peak_level=random.choice(levels),
-                message_count=random.randint(1, 20),
-                is_escalating=random.random() > 0.7,
-                is_deescalating=random.random() > 0.8,
-                escalation_rate=random.random(),
-            )
-            metrics.record_session(
-                state,
-                triggered_overlay=random.random() > 0.8,
-                showed_988=random.random() > 0.7,
-            )
-        print("Generated 50 demo sessions.")
-    
-    if args.json:
-        print(metrics.get_json(args.days))
-    else:
-        print(metrics.get_report(args.days))
-
-
-if __name__ == "__main__":
-    main()

crisis/session_tracker.py

@@ -34,6 +34,7 @@ Usage:
 from dataclasses import dataclass, field
 from typing import List, Optional
 
+from .behavioral import BehavioralTracker
 from .detect import CrisisDetectionResult, SCORES
 
 # Level ordering for comparison (higher = more severe)
@@ -52,6 +53,12 @@ class SessionState:
     is_deescalating: bool = False
     escalation_rate: float = 0.0  # levels gained per message
     consecutive_low_messages: int = 0  # for de-escalation tracking
+    behavioral_score: float = 0.0
+    behavioral_signals: List[dict] = field(default_factory=list)
+    frequency_change: float = 1.0
+    is_late_night: bool = False
+    session_length_trend: str = "stable"
+    withdrawal_detected: bool = False
 
 
 class CrisisSessionTracker:
@@ -77,6 +84,8 @@ class CrisisSessionTracker:
         self._message_count = 0
         self._level_history: List[str] = []
         self._consecutive_low = 0
+        self._behavioral_tracker = BehavioralTracker()
+        self._behavioral_session_id = "current-session"
 
     @property
     def state(self) -> SessionState:
@@ -84,6 +93,7 @@ class CrisisSessionTracker:
         is_escalating = self._detect_escalation()
         is_deescalating = self._detect_deescalation()
         rate = self._compute_escalation_rate()
+        behavioral = self._behavioral_tracker.get_risk_signals(self._behavioral_session_id)
 
         return SessionState(
             current_level=self._current_level,
@@ -94,14 +104,29 @@ class CrisisSessionTracker:
             is_deescalating=is_deescalating,
             escalation_rate=rate,
             consecutive_low_messages=self._consecutive_low,
+            behavioral_score=behavioral["behavioral_score"],
+            behavioral_signals=behavioral["signals"],
+            frequency_change=behavioral["frequency_change"],
+            is_late_night=behavioral["is_late_night"],
+            session_length_trend=behavioral["session_length_trend"],
+            withdrawal_detected=behavioral["withdrawal_detected"],
         )
 
-    def record(self, detection: CrisisDetectionResult) -> SessionState:
+    def record(
+        self,
+        detection: CrisisDetectionResult,
+        *,
+        timestamp=None,
+        message_length: int = 0,
+        role: str = "user",
+    ) -> SessionState:
         """
         Record a crisis detection result for the current message.
 
         Returns updated SessionState.
         """
+        from datetime import datetime, timezone
+
         level = detection.level
         self._message_count += 1
         self._level_history.append(level)
@@ -116,6 +141,17 @@ class CrisisSessionTracker:
         else:
             self._consecutive_low = 0
 
+        if role == "user":
+            if timestamp is None:
+                timestamp = datetime.now(timezone.utc)
+            self._behavioral_tracker.record(
+                self._behavioral_session_id,
+                timestamp,
+                message_length=message_length,
+                crisis_score=detection.score,
+                role=role,
+            )
+
         self._current_level = level
         return self.state
 
@@ -195,14 +231,22 @@ class CrisisSessionTracker:
                 "supportive engagement while remaining vigilant."
             )
 
+        notes = []
+
         if s.peak_level in ("CRITICAL", "HIGH") and s.current_level not in ("CRITICAL", "HIGH"):
-            return (
-                f"User previously reached {s.peak_level} crisis level "
-                f"this session (currently {s.current_level}). "
+            notes.append(
+                f"User previously reached {s.peak_level} crisis level this session (currently {s.current_level}). "
                 "Continue with care and awareness of the earlier crisis."
             )
 
-        return ""
+        if s.behavioral_score >= 0.35 and s.behavioral_signals:
+            signal_names = ", ".join(item["signal_type"] for item in s.behavioral_signals)
+            notes.append(
+                f"Behavioral risk signals detected this session: {signal_names}. "
+                "Use the behavioral context to increase sensitivity and warmth."
+            )
+
+        return " ".join(notes)
 
     def get_ui_hints(self) -> dict:
         """
@@ -217,6 +261,10 @@ class CrisisSessionTracker:
             "session_deescalating": s.is_deescalating,
             "session_peak_level": s.peak_level,
             "session_message_count": s.message_count,
+            "behavioral_score": s.behavioral_score,
+            "is_late_night": s.is_late_night,
+            "withdrawal_detected": s.withdrawal_detected,
+            "session_length_trend": s.session_length_trend,
         }
 
         if s.is_escalating:
@@ -226,12 +274,20 @@ class CrisisSessionTracker:
                 "Consider increasing intervention level."
             )
 
+        if s.behavioral_score >= 0.5:
+            hints["behavioral_warning"] = True
+            hints.setdefault(
+                "suggested_action",
+                "Behavioral risk patterns are active. Keep the response warm, grounded, and alert."
+            )
+
         return hints
 
 
 def check_crisis_with_session(
     text: str,
     tracker: CrisisSessionTracker,
+    timestamp=None,
 ) -> dict:
     """
     Convenience: detect crisis and update session state in one call.
@@ -243,7 +299,16 @@ def check_crisis_with_session(
 
     single_result = check_crisis(text)
     detection = detect_crisis(text)
-    session_state = tracker.record(detection)
+    session_state = tracker.record(detection, timestamp=timestamp, message_length=len(text))
+
+    behavioral = {
+        "frequency_change": session_state.frequency_change,
+        "is_late_night": session_state.is_late_night,
+        "session_length_trend": session_state.session_length_trend,
+        "withdrawal_detected": session_state.withdrawal_detected,
+        "behavioral_score": session_state.behavioral_score,
+        "signals": session_state.behavioral_signals,
+    }
 
     return {
         **single_result,
@@ -255,5 +320,6 @@ def check_crisis_with_session(
             "is_deescalating": session_state.is_deescalating,
             "modifier": tracker.get_session_modifier(),
             "ui_hints": tracker.get_ui_hints(),
+            "behavioral": behavioral,
         },
     }

index.html

@@ -680,7 +680,7 @@ html, body {
 
   <!-- Footer -->
   <footer id="footer">
-    <a href="/about" aria-label="About The Door">about</a>
+    <a href="/about.html" aria-label="About The Door">about</a>
     <button id="safety-plan-btn" aria-label="Open My Safety Plan">my safety plan</button>
     <button id="clear-chat-btn" aria-label="Clear chat history">clear chat</button>
   </footer>

tests/test_behavioral_tracker.py

@@ -0,0 +1,101 @@
+"""
+Tests for behavioral crisis pattern detection (#133).
+"""
+
+import os
+import sys
+import unittest
+from datetime import datetime, timedelta, timezone
+
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+from crisis.session_tracker import CrisisSessionTracker, check_crisis_with_session
+from crisis.behavioral import BehavioralTracker
+
+
+class TestBehavioralTracker(unittest.TestCase):
+    def _seed_day(self, tracker, *, session_id, day, count, start_hour=10, message_length=48, crisis_score=0.0):
+        base = datetime(2026, 4, day, start_hour, 0, tzinfo=timezone.utc)
+        for i in range(count):
+            tracker.record(
+                session_id,
+                base + timedelta(minutes=i * 10),
+                message_length=message_length,
+                crisis_score=crisis_score,
+            )
+
+    def test_frequency_change_uses_seven_day_baseline(self):
+        tracker = BehavioralTracker()
+        for day in range(1, 8):
+            self._seed_day(tracker, session_id=f"baseline-{day}", day=day, count=2)
+
+        burst_base = datetime(2026, 4, 8, 14, 0, tzinfo=timezone.utc)
+        for i in range(8):
+            tracker.record(
+                "current-session",
+                burst_base + timedelta(minutes=i),
+                message_length=72,
+                crisis_score=0.1,
+            )
+
+        summary = tracker.get_risk_signals("current-session")
+
+        self.assertGreater(summary["frequency_change"], 2.0)
+        self.assertTrue(any(sig["signal_type"] == "frequency" for sig in summary["signals"]))
+        self.assertGreater(summary["behavioral_score"], 0.0)
+
+    def test_late_night_messages_raise_flag(self):
+        tracker = BehavioralTracker()
+        base = datetime(2026, 4, 10, 2, 15, tzinfo=timezone.utc)
+        for i in range(3):
+            tracker.record(
+                "late-night",
+                base + timedelta(minutes=i * 7),
+                message_length=35,
+                crisis_score=0.0,
+            )
+
+        summary = tracker.get_risk_signals("late-night")
+
+        self.assertTrue(summary["is_late_night"])
+        self.assertTrue(any(sig["signal_type"] == "time" for sig in summary["signals"]))
+
+    def test_withdrawal_detected_after_large_drop_from_baseline(self):
+        tracker = BehavioralTracker()
+        for day in range(1, 8):
+            self._seed_day(tracker, session_id=f"baseline-{day}", day=day, count=10, message_length=80)
+
+        tracker.record(
+            "withdrawal-session",
+            datetime(2026, 4, 9, 11, 0, tzinfo=timezone.utc),
+            message_length=18,
+            crisis_score=0.0,
+        )
+
+        summary = tracker.get_risk_signals("withdrawal-session")
+
+        self.assertTrue(summary["withdrawal_detected"])
+        self.assertTrue(any(sig["signal_type"] == "withdrawal" for sig in summary["signals"]))
+
+
+class TestBehavioralSessionIntegration(unittest.TestCase):
+    def test_check_crisis_with_session_includes_behavioral_summary(self):
+        tracker = CrisisSessionTracker()
+        base = datetime(2026, 4, 20, 2, 0, tzinfo=timezone.utc)
+
+        check_crisis_with_session("can't sleep", tracker, timestamp=base)
+        check_crisis_with_session("still here", tracker, timestamp=base + timedelta(minutes=1))
+        result = check_crisis_with_session("everything feels loud", tracker, timestamp=base + timedelta(minutes=2))
+
+        behavioral = result["session"]["behavioral"]
+        self.assertIn("frequency_change", behavioral)
+        self.assertIn("is_late_night", behavioral)
+        self.assertIn("session_length_trend", behavioral)
+        self.assertIn("withdrawal_detected", behavioral)
+        self.assertIn("behavioral_score", behavioral)
+        self.assertTrue(behavioral["is_late_night"])
+        self.assertGreater(behavioral["behavioral_score"], 0.0)
+
+
+if __name__ == '__main__':
+    unittest.main()

tests/test_crisis_metrics.py

@@ -1,118 +0,0 @@
-"""
-Tests for crisis/metrics.py — Aggregate crisis metrics.
-"""
-
-import json
-import os
-import shutil
-import tempfile
-import unittest
-from pathlib import Path
-
-import sys
-sys.path.insert(0, str(Path(__file__).parent.parent))
-
-from crisis.metrics import CrisisMetrics, SessionMetrics, AggregateMetrics
-
-
-class TestCrisisMetrics(unittest.TestCase):
-    def setUp(self):
-        self.tmpdir = tempfile.mkdtemp()
-        self.metrics = CrisisMetrics(Path(self.tmpdir))
-
-    def tearDown(self):
-        shutil.rmtree(self.tmpdir)
-
-    def test_record_session_creates_file(self):
-        sm = SessionMetrics(
-            timestamp=1700000000,
-            current_level="LOW",
-            peak_level="MEDIUM",
-            message_count=5,
-            was_escalating=True,
-            was_deescalating=False,
-            escalation_rate=0.5,
-        )
-        self.metrics.record_session(sm)
-
-        files = list(Path(self.tmpdir).glob("*.jsonl"))
-        self.assertEqual(len(files), 1)
-
-    def test_record_session_writes_jsonl(self):
-        sm = SessionMetrics(
-            timestamp=1700000000,
-            current_level="HIGH",
-            peak_level="CRITICAL",
-            message_count=10,
-            was_escalating=True,
-            was_deescalating=False,
-            escalation_rate=1.0,
-            triggered_overlay=True,
-            showed_988=True,
-        )
-        self.metrics.record_session(sm)
-
-        files = list(Path(self.tmpdir).glob("*.jsonl"))
-        with open(files[0]) as f:
-            data = json.loads(f.readline())
-        self.assertEqual(data['peak_level'], 'CRITICAL')
-        self.assertTrue(data['triggered_overlay'])
-
-    def test_get_summary_empty(self):
-        agg = self.metrics.get_summary(days=7)
-        self.assertEqual(agg.total_sessions, 0)
-        self.assertEqual(agg.total_messages, 0)
-
-    def test_get_summary_with_data(self):
-        for level in ["LOW", "MEDIUM", "HIGH"]:
-            sm = SessionMetrics(
-                timestamp=1700000000,
-                current_level=level,
-                peak_level=level,
-                message_count=3,
-                was_escalating=level != "LOW",
-                was_deescalating=False,
-                escalation_rate=0.5,
-            )
-            self.metrics.record_session(sm)
-
-        agg = self.metrics.get_summary(days=1)
-        self.assertEqual(agg.total_sessions, 3)
-        self.assertEqual(agg.total_messages, 9)
-        self.assertEqual(agg.escalating_sessions, 2)
-
-    def test_get_report_returns_string(self):
-        sm = SessionMetrics(
-            timestamp=1700000000,
-            current_level="LOW",
-            peak_level="LOW",
-            message_count=5,
-            was_escalating=False,
-            was_deescalating=False,
-            escalation_rate=0.0,
-        )
-        self.metrics.record_session(sm)
-
-        report = self.metrics.get_report(days=1)
-        self.assertIn("CRISIS METRICS REPORT", report)
-        self.assertIn("Sessions:", report)
-
-    def test_get_json_returns_valid(self):
-        sm = SessionMetrics(
-            timestamp=1700000000,
-            current_level="MEDIUM",
-            peak_level="MEDIUM",
-            message_count=3,
-            was_escalating=False,
-            was_deescalating=False,
-            escalation_rate=0.0,
-        )
-        self.metrics.record_session(sm)
-
-        json_str = self.metrics.get_json(days=1)
-        data = json.loads(json_str)
-        self.assertEqual(data['total_sessions'], 1)
-
-
-if __name__ == "__main__":
-    unittest.main()