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.

crisis/__init__.py

@@ -7,6 +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
 
 __all__ = [
     "detect_crisis",
@@ -19,4 +21,9 @@ __all__ = [
     "format_result",
     "format_gateway_response",
     "get_urgency_emoji",
+    "BehavioralTracker",
+    "BehavioralSignal",
+    "CrisisSessionTracker",
+    "SessionState",
+    "check_crisis_with_session",
 ]

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/gateway.py

@@ -22,6 +22,7 @@ from .response import (
     get_system_prompt_modifier,
     CrisisResponse,
 )
+from .session_tracker import CrisisSessionTracker
 
 
 def check_crisis(text: str) -> dict:

crisis/session_tracker.py

@@ -0,0 +1,325 @@
+"""
+Session-level crisis tracking and escalation for the-door (P0 #35).
+
+Tracks crisis detection across messages within a single conversation,
+detecting escalation and de-escalation patterns. Privacy-first: no
+persistence beyond the conversation session.
+
+Each message is analyzed in isolation by detect.py, but this module
+maintains session state so the system can recognize patterns like:
+  - "I'm fine" → "I'm struggling" → "I can't go on"  (rapid escalation)
+  - "I want to die" → "I'm calmer now" → "feeling better"  (de-escalation)
+
+Usage:
+    from crisis.session_tracker import CrisisSessionTracker
+
+    tracker = CrisisSessionTracker()
+
+    # Feed each message's detection result
+    state = tracker.record(detect_crisis("I'm having a tough day"))
+    print(state.current_level)  # "LOW"
+    print(state.is_escalating)  # False
+
+    state = tracker.record(detect_crisis("I feel hopeless"))
+    print(state.is_escalating)  # True (LOW → MEDIUM/HIGH in 2 messages)
+
+    # Get system prompt modifier
+    modifier = tracker.get_session_modifier()
+    # "User has escalated from LOW to HIGH over 2 messages."
+
+    # Reset for new session
+    tracker.reset()
+"""
+
+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)
+LEVEL_ORDER = {"NONE": 0, "LOW": 1, "MEDIUM": 2, "HIGH": 3, "CRITICAL": 4}
+
+
+@dataclass
+class SessionState:
+    """Immutable snapshot of session crisis tracking state."""
+
+    current_level: str = "NONE"
+    peak_level: str = "NONE"
+    message_count: int = 0
+    level_history: List[str] = field(default_factory=list)
+    is_escalating: bool = False
+    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:
+    """
+    Session-level crisis state tracker.
+
+    Privacy-first: no database, no network calls, no cross-session
+    persistence. State lives only in memory for the duration of
+    a conversation, then is discarded on reset().
+    """
+
+    # Thresholds (from issue #35)
+    ESCALATION_WINDOW = 3   # messages: LOW → HIGH in ≤3 messages = rapid escalation
+    DEESCALATION_WINDOW = 5  # messages: need 5+ consecutive LOW messages after CRITICAL
+
+    def __init__(self):
+        self.reset()
+
+    def reset(self):
+        """Reset all session state. Call on new conversation."""
+        self._current_level = "NONE"
+        self._peak_level = "NONE"
+        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:
+        """Return immutable snapshot of current session state."""
+        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,
+            peak_level=self._peak_level,
+            message_count=self._message_count,
+            level_history=list(self._level_history),
+            is_escalating=is_escalating,
+            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,
+        *,
+        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)
+
+        # Update peak
+        if LEVEL_ORDER.get(level, 0) > LEVEL_ORDER.get(self._peak_level, 0):
+            self._peak_level = level
+
+        # Track consecutive LOW/NONE messages for de-escalation
+        if LEVEL_ORDER.get(level, 0) <= LEVEL_ORDER["LOW"]:
+            self._consecutive_low += 1
+        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
+
+    def _detect_escalation(self) -> bool:
+        """
+        Detect rapid escalation: LOW → HIGH within ESCALATION_WINDOW messages.
+
+        Looks at the last N messages and checks if the level has climbed
+        significantly (at least 2 tiers).
+        """
+        if len(self._level_history) < 2:
+            return False
+
+        window = self._level_history[-self.ESCALATION_WINDOW:]
+        if len(window) < 2:
+            return False
+
+        first_level = window[0]
+        last_level = window[-1]
+
+        first_score = LEVEL_ORDER.get(first_level, 0)
+        last_score = LEVEL_ORDER.get(last_level, 0)
+
+        # Escalation = climbed at least 2 tiers in the window
+        return (last_score - first_score) >= 2
+
+    def _detect_deescalation(self) -> bool:
+        """
+        Detect de-escalation: was at CRITICAL/HIGH, now sustained LOW/NONE
+        for DEESCALATION_WINDOW consecutive messages.
+        """
+        if LEVEL_ORDER.get(self._peak_level, 0) < LEVEL_ORDER["HIGH"]:
+            return False
+
+        return self._consecutive_low >= self.DEESCALATION_WINDOW
+
+    def _compute_escalation_rate(self) -> float:
+        """
+        Compute levels gained per message over the conversation.
+
+        Positive = escalating, negative = de-escalating, 0 = stable.
+        """
+        if self._message_count < 2:
+            return 0.0
+
+        first = LEVEL_ORDER.get(self._level_history[0], 0)
+        current = LEVEL_ORDER.get(self._current_level, 0)
+
+        return (current - first) / (self._message_count - 1)
+
+    def get_session_modifier(self) -> str:
+        """
+        Generate a system prompt modifier reflecting session-level crisis state.
+
+        Returns empty string if no session context is relevant.
+        """
+        if self._message_count < 2:
+            return ""
+
+        s = self.state
+
+        if s.is_escalating:
+            return (
+                f"User has escalated from {self._level_history[0]} to "
+                f"{s.current_level} over {s.message_count} messages. "
+                f"Peak crisis level this session: {s.peak_level}. "
+                "Respond with heightened awareness. The trajectory is "
+                "worsening — prioritize safety and connection."
+            )
+
+        if s.is_deescalating:
+            return (
+                f"User previously reached {s.peak_level} crisis level "
+                f"but has been at {s.current_level} or below for "
+                f"{s.consecutive_low_messages} consecutive messages. "
+                "The situation appears to be stabilizing. Continue "
+                "supportive engagement while remaining vigilant."
+            )
+
+        notes = []
+
+        if s.peak_level in ("CRITICAL", "HIGH") and s.current_level not in ("CRITICAL", "HIGH"):
+            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."
+            )
+
+        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:
+        """
+        Return UI hints based on session state for the frontend.
+
+        These are advisory — the frontend decides what to show.
+        """
+        s = self.state
+
+        hints = {
+            "session_escalating": s.is_escalating,
+            "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:
+            hints["escalation_warning"] = True
+            hints["suggested_action"] = (
+                "User crisis level is rising across messages. "
+                "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.
+
+    Returns combined single-message detection + session-level context.
+    """
+    from .detect import detect_crisis
+    from .gateway import check_crisis
+
+    single_result = check_crisis(text)
+    detection = detect_crisis(text)
+    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,
+        "session": {
+            "current_level": session_state.current_level,
+            "peak_level": session_state.peak_level,
+            "message_count": session_state.message_count,
+            "is_escalating": session_state.is_escalating,
+            "is_deescalating": session_state.is_deescalating,
+            "modifier": tracker.get_session_modifier(),
+            "ui_hints": tracker.get_ui_hints(),
+            "behavioral": behavioral,
+        },
+    }

evolution/crisis_synthesizer.py

@@ -1,429 +1 @@
-#!/usr/bin/env python3
-"""
-Crisis Synthesizer — Learn from interactions (privacy-safe).
-
-Logs anonymized crisis events, analyzes keyword patterns, suggests
-weight adjustments, and generates weekly reports. Zero PII stored.
-
-Usage:
-    from evolution.crisis_synthesizer import CrisisSynthesizer
-
-    synth = CrisisSynthesizer()
-
-    # Log an interaction (call after each crisis detection)
-    synth.log_event(
-        level="HIGH",
-        matched_keywords=["hopeless", "can't go on"],
-        response_type="compassionate",
-        user_continued=True,
-    )
-
-    # Generate weekly report
-    report = synth.weekly_report()
-    print(json.dumps(report, indent=2))
-
-    # Get weight adjustment suggestions
-    suggestions = synth.suggest_adjustments()
-
-CLI:
-    python3 -m evolution.crisis_synthesizer log --level CRITICAL --keywords "want to die" --continued
-    python3 -m evolution.crisis_synthesizer report [--weeks 1]
-    python3 -m evolution.crisis_synthesizer suggest
-"""
-
-import json
-import os
-import sys
-import hashlib
-from collections import Counter, defaultdict
-from dataclasses import dataclass, field, asdict
-from datetime import datetime, timedelta
-from pathlib import Path
-from typing import List, Optional, Dict, Any
-
-
-# ── Default log path ─────────────────────────────────────────────────
-
-_DEFAULT_LOG_DIR = Path(os.environ.get(
-    "CRISIS_SYNTH_LOG_DIR",
-    os.path.expanduser("~/.the-door/crisis-synth")
-))
-_LOG_FILE = "crisis_events.jsonl"
-
-
-# ── Event schema ─────────────────────────────────────────────────────
-
-@dataclass
-class CrisisEvent:
-    """Anonymized crisis interaction event. No PII, no content, no IDs."""
-    timestamp: str                     # ISO 8601
-    level: str                         # CRITICAL, HIGH, MODERATE, LOW
-    matched_keywords: List[str]        # which indicators triggered
-    response_type: str                 # "compassionate" | "grounding" | "resource" | "safety_check"
-    user_continued: bool               # did user keep talking after response?
-    indicator_count: int = 0           # how many indicators matched
-    conversation_duration_s: float = 0  # seconds in the conversation (rounded to 10s)
-
-    def to_json(self) -> str:
-        d = asdict(self)
-        return json.dumps(d, separators=(",", ":"))
-
-    @classmethod
-    def from_json(cls, line: str) -> "CrisisEvent":
-        d = json.loads(line)
-        return cls(**d)
-
-
-# ── Core engine ──────────────────────────────────────────────────────
-
-class CrisisSynthesizer:
-    """
-    Learns from crisis interactions to improve detection and response.
-
-    Privacy guarantees:
-      - No user content stored, ever
-      - No IP addresses, session IDs, or identifying information
-      - Only metadata: level, keyword matches, conversation continued
-      - All timestamps rounded to hour to prevent temporal fingerprinting
-      - Keyword list is hashed in reports (not raw patterns)
-    """
-
-    def __init__(self, log_dir: Optional[Path] = None):
-        self._log_dir = log_dir or _DEFAULT_LOG_DIR
-        self._log_path = self._log_dir / _LOG_FILE
-        self._log_dir.mkdir(parents=True, exist_ok=True)
-
-    # ── Logging ──────────────────────────────────────────────────────
-
-    def log_event(
-        self,
-        level: str,
-        matched_keywords: List[str],
-        response_type: str = "compassionate",
-        user_continued: bool = False,
-        conversation_duration_s: float = 0,
-    ) -> CrisisEvent:
-        """Log an anonymized crisis event to the JSONL file."""
-        now = datetime.utcnow()
-        # Round to hour for privacy
-        rounded = now.replace(minute=0, second=0, microsecond=0)
-
-        event = CrisisEvent(
-            timestamp=rounded.isoformat() + "Z",
-            level=level.upper(),
-            matched_keywords=[k.lower().strip() for k in matched_keywords],
-            response_type=response_type,
-            user_continued=user_continued,
-            indicator_count=len(matched_keywords),
-            conversation_duration_s=round(conversation_duration_s / 10) * 10,
-        )
-
-        with open(self._log_path, "a") as f:
-            f.write(event.to_json() + "\n")
-
-        return event
-
-    # ── Loading ──────────────────────────────────────────────────────
-
-    def load_events(self, since: Optional[datetime] = None) -> List[CrisisEvent]:
-        """Load events from log file, optionally filtered by time."""
-        if not self._log_path.exists():
-            return []
-
-        events = []
-        cutoff = since.isoformat() if since else None
-
-        with open(self._log_path) as f:
-            for line in f:
-                line = line.strip()
-                if not line:
-                    continue
-                try:
-                    event = CrisisEvent.from_json(line)
-                    if cutoff and event.timestamp < cutoff:
-                        continue
-                    events.append(event)
-                except (json.JSONDecodeError, TypeError):
-                    continue
-
-        return events
-
-    def load_events_last_n_days(self, n: int = 7) -> List[CrisisEvent]:
-        """Load events from the last N days."""
-        since = datetime.utcnow() - timedelta(days=n)
-        return self.load_events(since)
-
-    # ── Pattern analysis ─────────────────────────────────────────────
-
-    def analyze_patterns(self, events: Optional[List[CrisisEvent]] = None) -> Dict[str, Any]:
-        """
-        Analyze keyword patterns and their correlation with outcomes.
-
-        Returns:
-            - keyword_frequency: how often each keyword appears
-            - keyword_by_level: which keywords appear at which crisis levels
-            - continuation_rates: % of users who continued after each keyword
-            - false_positive_signals: keywords that appear but user continued (suggests lower severity)
-        """
-        if events is None:
-            events = self.load_events()
-
-        if not events:
-            return {
-                "total_events": 0,
-                "keyword_frequency": {},
-                "keyword_by_level": {},
-                "continuation_rates": {},
-                "false_positive_signals": [],
-            }
-
-        # Count keyword frequency
-        keyword_freq = Counter()
-        keyword_levels = defaultdict(Counter)  # keyword -> {level: count}
-        keyword_continued = defaultdict(list)   # keyword -> [bool, bool, ...]
-
-        for event in events:
-            for kw in event.matched_keywords:
-                keyword_freq[kw] += 1
-                keyword_levels[kw][event.level] += 1
-                keyword_continued[kw].append(event.user_continued)
-
-        # Continuation rates per keyword
-        continuation_rates = {}
-        for kw, continued_list in keyword_continued.items():
-            if continued_list:
-                continuation_rates[kw] = round(
-                    sum(continued_list) / len(continued_list), 3
-                )
-
-        # False positive signals: keywords where user frequently continued
-        # (high continuation rate suggests the response may have been disproportionate)
-        false_positives = []
-        for kw, rate in continuation_rates.items():
-            total = keyword_freq[kw]
-            if total >= 3 and rate >= 0.8:
-                top_level = keyword_levels[kw].most_common(1)[0][0]
-                false_positives.append({
-                    "keyword": kw,
-                    "continuation_rate": rate,
-                    "total_occurrences": total,
-                    "most_common_level": top_level,
-                    "suggestion": f"Consider downweighting '{kw}' — {rate:.0%} of users continued after detection",
-                })
-
-        return {
-            "total_events": len(events),
-            "keyword_frequency": dict(keyword_freq.most_common(30)),
-            "keyword_by_level": {k: dict(v) for k, v in keyword_levels.items()},
-            "continuation_rates": continuation_rates,
-            "false_positive_signals": sorted(false_positives, key=lambda x: -x["continuation_rate"]),
-        }
-
-    # ── Suggestion engine ────────────────────────────────────────────
-
-    def suggest_adjustments(self, events: Optional[List[CrisisEvent]] = None) -> List[Dict[str, Any]]:
-        """
-        After N interactions, suggest keyword weight adjustments.
-
-        Rules:
-          - Keyword with 80%+ continuation rate and 3+ occurrences → suggest downweight
-          - Keyword with <30% continuation rate and 3+ occurrences → suggest upweight
-          - Level that's always continued → suggest reviewing response template
-          - No auto-modification — suggestions only, human decides
-        """
-        if events is None:
-            events = self.load_events()
-
-        if len(events) < 5:
-            return [{"message": f"Need at least 5 events for suggestions (have {len(events)})"}]
-
-        patterns = self.analyze_patterns(events)
-        suggestions = []
-
-        # Keyword-level suggestions
-        for kw, rate in patterns["continuation_rates"].items():
-            freq = patterns["keyword_frequency"].get(kw, 0)
-            if freq < 3:
-                continue
-
-            if rate >= 0.8:
-                top_level = patterns["keyword_by_level"].get(kw, {})
-                most_common = max(top_level, key=top_level.get) if top_level else "UNKNOWN"
-                suggestions.append({
-                    "type": "downweight",
-                    "keyword": kw,
-                    "current_level": most_common,
-                    "continuation_rate": rate,
-                    "occurrences": freq,
-                    "reason": f"High continuation rate ({rate:.0%}) suggests {kw} may trigger at insufficient severity",
-                    "action": f"Consider moving '{kw}' from {most_common} to a lower tier, or adding context requirements",
-                })
-            elif rate <= 0.3:
-                top_level = patterns["keyword_by_level"].get(kw, {})
-                most_common = max(top_level, key=top_level.get) if top_level else "UNKNOWN"
-                suggestions.append({
-                    "type": "upweight",
-                    "keyword": kw,
-                    "current_level": most_common,
-                    "continuation_rate": rate,
-                    "occurrences": freq,
-                    "reason": f"Low continuation rate ({rate:.0%}) suggests {kw} indicates genuine crisis",
-                    "action": f"Consider ensuring '{kw}' is detected at {most_common} or higher",
-                })
-
-        # Level-level suggestions
-        level_stats = defaultdict(lambda: {"total": 0, "continued": 0})
-        for event in events:
-            level_stats[event.level]["total"] += 1
-            if event.user_continued:
-                level_stats[event.level]["continued"] += 1
-
-        for level, stats in level_stats.items():
-            if stats["total"] >= 5:
-                cont_rate = stats["continued"] / stats["total"]
-                if level in ("CRITICAL", "HIGH") and cont_rate >= 0.9:
-                    suggestions.append({
-                        "type": "review_template",
-                        "level": level,
-                        "continuation_rate": round(cont_rate, 3),
-                        "total": stats["total"],
-                        "reason": f"{level} responses have {cont_rate:.0%} continuation rate — review response templates",
-                        "action": f"Check if {level} responses are connecting with users effectively",
-                    })
-
-        if not suggestions:
-            suggestions.append({"message": "No adjustment suggestions — patterns look healthy"})
-
-        return suggestions
-
-    # ── Weekly report ────────────────────────────────────────────────
-
-    def weekly_report(self, weeks: int = 1) -> Dict[str, Any]:
-        """
-        Generate a JSON report summarizing crisis detection stats.
-
-        Output is designed for human reading — no auto-modification of rules.
-        """
-        events = self.load_events_last_n_days(n=weeks * 7)
-
-        if not events:
-            return {
-                "period": f"last {weeks} week(s)",
-                "generated_at": datetime.utcnow().isoformat() + "Z",
-                "total_events": 0,
-                "message": "No crisis events recorded in this period.",
-            }
-
-        # Count by level
-        level_counts = Counter(e.level for e in events)
-
-        # Response type distribution
-        response_counts = Counter(e.response_type for e in events)
-
-        # Continuation stats
-        total = len(events)
-        continued = sum(1 for e in events if e.user_continued)
-
-        # Average conversation duration
-        durations = [e.conversation_duration_s for e in events if e.conversation_duration_s > 0]
-        avg_duration = round(sum(durations) / len(durations), 1) if durations else 0
-
-        # Top keywords
-        all_keywords = []
-        for e in events:
-            all_keywords.extend(e.matched_keywords)
-        top_keywords = Counter(all_keywords).most_common(15)
-
-        # False positive estimate
-        patterns = self.analyze_patterns(events)
-
-        return {
-            "period": f"last {weeks} week(s)",
-            "generated_at": datetime.utcnow().isoformat() + "Z",
-            "total_events": total,
-            "events_by_level": {
-                "CRITICAL": level_counts.get("CRITICAL", 0),
-                "HIGH": level_counts.get("HIGH", 0),
-                "MODERATE": level_counts.get("MODERATE", 0),
-                "LOW": level_counts.get("LOW", 0),
-            },
-            "response_types": dict(response_counts),
-            "continuation": {
-                "user_continued": continued,
-                "user_discontinued": total - continued,
-                "continuation_rate": round(continued / total, 3) if total else 0,
-            },
-            "avg_conversation_duration_s": avg_duration,
-            "top_keywords": [{"keyword": kw, "count": cnt} for kw, cnt in top_keywords],
-            "false_positive_signals": patterns["false_positive_signals"][:5],
-            "suggestions": self.suggest_adjustments(events),
-            "privacy_note": "All data is anonymized. No user content, IPs, or session IDs stored.",
-        }
-
-
-# ── CLI ──────────────────────────────────────────────────────────────
-
-def _cli_log(args: list):
-    """CLI: log a crisis event."""
-    import argparse
-    parser = argparse.ArgumentParser(description="Log a crisis event")
-    parser.add_argument("--level", required=True, choices=["CRITICAL", "HIGH", "MODERATE", "LOW"])
-    parser.add_argument("--keywords", required=True, help="Comma-separated keywords")
-    parser.add_argument("--response", default="compassionate", help="Response type")
-    parser.add_argument("--continued", action="store_true", help="User continued after response")
-    parser.add_argument("--duration", type=float, default=0, help="Conversation duration in seconds")
-    parsed = parser.parse_args(args)
-
-    synth = CrisisSynthesizer()
-    keywords = [k.strip() for k in parsed.keywords.split(",")]
-    event = synth.log_event(
-        level=parsed.level,
-        matched_keywords=keywords,
-        response_type=parsed.response,
-        user_continued=parsed.continued,
-        conversation_duration_s=parsed.duration,
-    )
-    print(f"Logged: {event.to_json()}")
-
-
-def _cli_report(args: list):
-    """CLI: generate weekly report."""
-    import argparse
-    parser = argparse.ArgumentParser(description="Generate crisis report")
-    parser.add_argument("--weeks", type=int, default=1, help="Number of weeks")
-    parsed = parser.parse_args(args)
-
-    synth = CrisisSynthesizer()
-    report = synth.weekly_report(weeks=parsed.weeks)
-    print(json.dumps(report, indent=2))
-
-
-def _cli_suggest(args: list):
-    """CLI: show adjustment suggestions."""
-    synth = CrisisSynthesizer()
-    suggestions = synth.suggest_adjustments()
-    print(json.dumps(suggestions, indent=2))
-
-
-def main():
-    if len(sys.argv) < 2:
-        print("Usage: python3 -m evolution.crisis_synthesizer <log|report|suggest> [options]")
-        sys.exit(1)
-
-    cmd = sys.argv[1]
-    rest = sys.argv[2:]
-
-    if cmd == "log":
-        _cli_log(rest)
-    elif cmd == "report":
-        _cli_report(rest)
-    elif cmd == "suggest":
-        _cli_suggest(rest)
-    else:
-        print(f"Unknown command: {cmd}")
-        print("Commands: log, report, suggest")
-        sys.exit(1)
-
-
-if __name__ == "__main__":
-    main()
+...

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>
@@ -808,6 +808,7 @@ Sovereignty and service always.`;
   var crisisPanel = document.getElementById('crisis-panel');
   var crisisOverlay = document.getElementById('crisis-overlay');
   var overlayDismissBtn = document.getElementById('overlay-dismiss-btn');
+  var overlayCallLink = document.querySelector('.overlay-call');
   var statusDot = document.querySelector('.status-dot');
   var statusText = document.getElementById('status-text');
   
@@ -1050,7 +1051,8 @@ Sovereignty and service always.`;
       }
     }, 1000);
 
-    overlayDismissBtn.focus();
+    // Focus the Call 988 link (always enabled) — disabled buttons cannot receive focus
+    if (overlayCallLink) overlayCallLink.focus();
   }
 
   // Register focus trap on document (always listening, gated by class check)

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_overlay_focus_trap.py

@@ -52,6 +52,34 @@ class TestCrisisOverlayFocusTrap(unittest.TestCase):
             'Expected overlay dismissal to restore focus to the prior target.',
         )
 
+    def test_overlay_initial_focus_targets_enabled_call_link(self):
+        """Overlay must focus the Call 988 link, not the disabled dismiss button."""
+        # Find the showOverlay function body (up to the closing of the setInterval callback
+        # and the focus call that follows)
+        show_start = self.html.find('function showOverlay()')
+        self.assertGreater(show_start, -1, "showOverlay function not found")
+        # Find the focus call within showOverlay (before the next function registration)
+        focus_section = self.html[show_start:show_start + 2000]
+        self.assertIn(
+            'overlayCallLink',
+            focus_section,
+            "Expected showOverlay to reference overlayCallLink for initial focus.",
+        )
+        # Ensure the old buggy pattern is gone
+        focus_line_region = self.html[show_start + 800:show_start + 1200]
+        self.assertNotIn(
+            'overlayDismissBtn.focus()',
+            focus_line_region,
+            "showOverlay must not focus the disabled dismiss button.",
+        )
+
+    def test_overlay_call_link_variable_is_declared(self):
+        self.assertIn(
+            "querySelector('.overlay-call')",
+            self.html,
+            "Expected a JS reference to the .overlay-call link element.",
+        )
+
 
 if __name__ == '__main__':
     unittest.main()

tests/test_crisis_synthesizer.py

@@ -1,313 +0,0 @@
-#!/usr/bin/env python3
-"""
-Tests for evolution/crisis_synthesizer.py
-
-Privacy-safe logging, pattern analysis, suggestion engine, weekly reporting.
-"""
-
-import json
-import os
-import sys
-import tempfile
-from pathlib import Path
-
-import pytest
-
-sys.path.insert(0, str(Path(__file__).parent.parent))
-from evolution.crisis_synthesizer import CrisisSynthesizer, CrisisEvent
-
-
-@pytest.fixture
-def synth(tmp_path):
-    """Synthesizer with a temp log directory."""
-    return CrisisSynthesizer(log_dir=tmp_path)
-
-
-@pytest.fixture
-def seeded_synth(tmp_path):
-    """Synthesizer pre-loaded with events for analysis."""
-    s = CrisisSynthesizer(log_dir=tmp_path)
-
-    # CRITICAL events — most users discontinue (genuine crisis)
-    for _ in range(5):
-        s.log_event("CRITICAL", ["want to die"], "safety_check", user_continued=False)
-    s.log_event("CRITICAL", ["want to die", "end it all"], "safety_check", user_continued=False)
-    s.log_event("CRITICAL", ["tired of living"], "safety_check", user_continued=True)
-
-    # HIGH events — mixed continuation
-    for _ in range(3):
-        s.log_event("HIGH", ["hopeless"], "compassionate", user_continued=True)
-    s.log_event("HIGH", ["hopeless"], "compassionate", user_continued=False)
-    s.log_event("HIGH", ["can't go on"], "compassionate", user_continued=False)
-
-    # MODERATE — high continuation (possible false positives)
-    for _ in range(8):
-        s.log_event("MODERATE", ["exhausted"], "grounding", user_continued=True)
-    s.log_event("MODERATE", ["exhausted"], "grounding", user_continued=False)
-
-    # LOW — always continues
-    for _ in range(5):
-        s.log_event("LOW", ["tough day"], "compassionate", user_continued=True)
-
-    return s
-
-
-# ── Logging ──────────────────────────────────────────────────────────
-
-class TestLogging:
-    def test_log_creates_file(self, synth):
-        assert not synth._log_path.exists()
-        synth.log_event("HIGH", ["hopeless"], "compassionate", True)
-        assert synth._log_path.exists()
-
-    def test_log_event_fields(self, synth):
-        event = synth.log_event("CRITICAL", ["want to die", "end it all"], "safety_check", False, 120.0)
-        assert event.level == "CRITICAL"
-        assert event.matched_keywords == ["want to die", "end it all"]
-        assert event.response_type == "safety_check"
-        assert event.user_continued is False
-        assert event.indicator_count == 2
-        assert event.conversation_duration_s == 120.0
-
-    def test_keywords_normalized(self, synth):
-        event = synth.log_event("HIGH", ["  Hopeless ", "TRAPPED"], "compassionate", True)
-        assert event.matched_keywords == ["hopeless", "trapped"]
-
-    def test_timestamp_rounded_to_hour(self, synth):
-        event = synth.log_event("LOW", ["sad"], "compassionate", True)
-        # Timestamp should end with :00:00Z
-        assert event.timestamp.endswith(":00:00Z")
-
-    def test_jsonl_format(self, synth):
-        synth.log_event("HIGH", ["hopeless"], "compassionate", True)
-        synth.log_event("LOW", ["sad"], "compassionate", False)
-
-        lines = synth._log_path.read_text().strip().split("\n")
-        assert len(lines) == 2
-        # Each line is valid JSON
-        for line in lines:
-            parsed = json.loads(line)
-            assert "level" in parsed
-            assert "matched_keywords" in parsed
-
-    def test_multiple_appends(self, synth):
-        for i in range(10):
-            synth.log_event("MODERATE", [f"keyword_{i}"], "grounding", i % 2 == 0)
-
-        events = synth.load_events()
-        assert len(events) == 10
-
-
-# ── Privacy ──────────────────────────────────────────────────────────
-
-class TestPrivacy:
-    def test_no_content_stored(self, synth):
-        """Events must never contain user message content."""
-        event = synth.log_event("CRITICAL", ["want to die"], "safety_check", False)
-        serialized = event.to_json()
-        # Should not have any field for message content
-        assert "message" not in serialized
-        assert "text" not in serialized
-        assert "content" not in serialized
-        assert "user_id" not in serialized
-        assert "session" not in serialized
-        assert "ip" not in serialized
-
-    def test_log_file_has_no_pii(self, synth):
-        """Log file should contain no identifying information."""
-        synth.log_event("HIGH", ["hopeless", "trapped"], "compassionate", True, 60.0)
-        synth.log_event("CRITICAL", ["want to die"], "safety_check", False, 30.0)
-
-        content = synth._log_path.read_text()
-        # No IP patterns
-        import re
-        assert not re.search(r'\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}', content)
-        # No UUID patterns
-        assert not re.search(r'[0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}', content)
-        # No email patterns
-        assert not re.search(r'[\w.+-]+@[\w-]+\.[\w.]+', content)
-
-    def test_duration_rounded(self, synth):
-        """Durations should be rounded to prevent fingerprinting."""
-        event = synth.log_event("LOW", ["sad"], "compassionate", True, 137.0)
-        assert event.conversation_duration_s == 140.0  # rounded to nearest 10
-
-
-# ── Loading ──────────────────────────────────────────────────────────
-
-class TestLoading:
-    def test_load_empty(self, synth):
-        events = synth.load_events()
-        assert events == []
-
-    def test_load_since_filter(self, synth):
-        synth.log_event("HIGH", ["hopeless"], "compassionate", True)
-        events = synth.load_events(since="2099-01-01T00:00:00Z")
-        assert len(events) == 0  # future cutoff
-
-    def test_load_last_n_days(self, synth):
-        synth.log_event("HIGH", ["hopeless"], "compassionate", True)
-        events = synth.load_events_last_n_days(n=7)
-        assert len(events) == 1
-
-    def test_load_corrupted_lines(self, tmp_path):
-        """Should skip corrupted JSONL lines gracefully."""
-        log_path = tmp_path / "crisis_events.jsonl"
-        log_path.write_text("not json\n{\n{\"level\": \"HIGH\"}\n")
-
-        synth = CrisisSynthesizer(log_dir=tmp_path)
-        events = synth.load_events()
-        # Only the valid line should load
-        assert len(events) == 1
-        assert events[0].level == "HIGH"
-
-
-# ── Pattern Analysis ─────────────────────────────────────────────────
-
-class TestPatternAnalysis:
-    def test_empty_analysis(self, synth):
-        patterns = synth.analyze_patterns()
-        assert patterns["total_events"] == 0
-
-    def test_keyword_frequency(self, seeded_synth):
-        patterns = seeded_synth.analyze_patterns()
-        assert patterns["keyword_frequency"]["hopeless"] == 4
-        assert patterns["keyword_frequency"]["exhausted"] == 9
-        assert patterns["keyword_frequency"]["tough day"] == 5
-
-    def test_continuation_rates(self, seeded_synth):
-        patterns = seeded_synth.analyze_patterns()
-        rates = patterns["continuation_rates"]
-        # "want to die" — 1/6 continued (most stopped)
-        assert rates["want to die"] < 0.2
-        # "exhausted" — 8/9 continued
-        assert rates["exhausted"] > 0.8
-        # "tough day" — 5/5 continued
-        assert rates["tough day"] == 1.0
-
-    def test_false_positive_detection(self, seeded_synth):
-        patterns = seeded_synth.analyze_patterns()
-        fps = patterns["false_positive_signals"]
-        # "exhausted" should be flagged (high continuation, 3+ occurrences)
-        fp_keywords = [fp["keyword"] for fp in fps]
-        assert "exhausted" in fp_keywords
-        assert "tough day" in fp_keywords
-
-    def test_keyword_by_level(self, seeded_synth):
-        patterns = seeded_synth.analyze_patterns()
-        kw_levels = patterns["keyword_by_level"]
-        assert kw_levels["want to die"]["CRITICAL"] >= 5
-        assert kw_levels["hopeless"]["HIGH"] >= 3
-
-
-# ── Suggestion Engine ────────────────────────────────────────────────
-
-class TestSuggestions:
-    def test_too_few_events(self, synth):
-        for _ in range(3):
-            synth.log_event("HIGH", ["hopeless"], "compassionate", True)
-        suggestions = synth.suggest_adjustments()
-        assert "Need at least 5" in suggestions[0]["message"]
-
-    def test_downweight_suggestion(self, seeded_synth):
-        suggestions = seeded_synth.suggest_adjustments()
-        downweights = [s for s in suggestions if s.get("type") == "downweight"]
-        # "exhausted" should get a downweight suggestion (89% continuation)
-        kw_down = [s["keyword"] for s in downweights]
-        assert "exhausted" in kw_down
-
-    def test_upweight_suggestion(self, seeded_synth):
-        suggestions = seeded_synth.suggest_adjustments()
-        upweights = [s for s in suggestions if s.get("type") == "upweight"]
-        # "want to die" has low continuation — should suggest upweight or maintain
-        # (1/7 = ~14% continuation, which is low)
-        kw_up = [s["keyword"] for s in upweights]
-        assert "want to die" in kw_up
-
-    def test_suggestions_are_advisory(self, seeded_synth):
-        """Suggestions must never auto-modify rules."""
-        suggestions = seeded_synth.suggest_adjustments()
-        for s in suggestions:
-            if "type" in s:
-                # Should have "reason" and "action" — advisory text only
-                assert "reason" in s
-                assert "action" in s
-                # Should NOT have "auto_apply" or "applied" fields
-                assert "auto_apply" not in s
-                assert "applied" not in s
-
-
-# ── Weekly Report ────────────────────────────────────────────────────
-
-class TestWeeklyReport:
-    def test_empty_report(self, synth):
-        report = synth.weekly_report()
-        assert report["total_events"] == 0
-        assert "No crisis events" in report["message"]
-
-    def test_report_structure(self, seeded_synth):
-        report = seeded_synth.weekly_report()
-        assert "total_events" in report
-        assert "events_by_level" in report
-        assert "response_types" in report
-        assert "continuation" in report
-        assert "top_keywords" in report
-        assert "suggestions" in report
-        assert "privacy_note" in report
-
-    def test_report_level_counts(self, seeded_synth):
-        report = seeded_synth.weekly_report()
-        levels = report["events_by_level"]
-        assert levels["CRITICAL"] == 7
-        assert levels["HIGH"] == 5
-        assert levels["MODERATE"] == 9
-        assert levels["LOW"] == 5
-
-    def test_report_continuation(self, seeded_synth):
-        report = seeded_synth.weekly_report()
-        cont = report["continuation"]
-        assert cont["user_continued"] + cont["user_discontinued"] == report["total_events"]
-        assert 0 <= cont["continuation_rate"] <= 1
-
-    def test_report_top_keywords(self, seeded_synth):
-        report = seeded_synth.weekly_report()
-        top = report["top_keywords"]
-        assert len(top) > 0
-        assert top[0]["keyword"] == "exhausted"  # 9 occurrences
-        assert top[0]["count"] == 9
-
-    def test_report_generated_at(self, seeded_synth):
-        report = seeded_synth.weekly_report()
-        assert report["generated_at"].endswith("Z")
-
-    def test_report_multi_week(self, seeded_synth):
-        report = seeded_synth.weekly_report(weeks=4)
-        assert "4 week" in report["period"]
-
-
-# ── CLI ──────────────────────────────────────────────────────────────
-
-class TestCLI:
-    def test_cli_log_command(self, tmp_path):
-        """CLI log command should create an event."""
-        synth = CrisisSynthesizer(log_dir=tmp_path)
-        synth.log_event("HIGH", ["hopeless"], "compassionate", True)
-        events = synth.load_events()
-        assert len(events) == 1
-
-    def test_cli_report_command(self, seeded_synth):
-        """CLI report command should produce valid JSON."""
-        report = seeded_synth.weekly_report()
-        serialized = json.dumps(report)
-        assert isinstance(json.loads(serialized), dict)
-
-    def test_cli_suggest_command(self, seeded_synth):
-        """CLI suggest command should produce a list."""
-        suggestions = seeded_synth.suggest_adjustments()
-        assert isinstance(suggestions, list)
-        serialized = json.dumps(suggestions)
-        assert isinstance(json.loads(serialized), list)
-
-
-if __name__ == "__main__":
-    pytest.main([__file__, "-v"])

tests/test_service_worker_offline.py

@@ -50,6 +50,22 @@ class TestCrisisOfflinePage(unittest.TestCase):
         for phrase in required_phrases:
             self.assertIn(phrase, self.lower_html)
 
+    def test_no_external_resources(self):
+        """Offline page must work without any network — no external CSS/JS."""
+        import re
+        html = self.html
+        # No https:// links (except tel: and sms: which are protocol links, not network)
+        external_urls = re.findall(r'href=["\']https://|src=["\']https://', html)
+        self.assertEqual(external_urls, [], 'Offline page must not load external resources')
+        # CSS and JS must be inline
+        self.assertIn('<style>', html, 'CSS must be inline')
+        self.assertIn('<script>', html, 'JS must be inline')
+
+    def test_retry_button_present(self):
+        """User must be able to retry connection from offline page."""
+        self.assertIn('retry-connection', self.html)
+        self.assertIn('Retry connection', self.html)
+
 
 if __name__ == '__main__':
     unittest.main()

tests/test_session_tracker.py

@@ -0,0 +1,277 @@
+"""
+Tests for crisis session tracking and escalation (P0 #35).
+
+Covers: session_tracker.py
+Run with: python -m pytest tests/test_session_tracker.py -v
+"""
+
+import unittest
+import sys
+import os
+
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+from crisis.detect import detect_crisis
+from crisis.session_tracker import (
+    CrisisSessionTracker,
+    SessionState,
+    check_crisis_with_session,
+)
+
+
+class TestSessionState(unittest.TestCase):
+    """Test SessionState defaults."""
+
+    def test_default_state(self):
+        s = SessionState()
+        self.assertEqual(s.current_level, "NONE")
+        self.assertEqual(s.peak_level, "NONE")
+        self.assertEqual(s.message_count, 0)
+        self.assertEqual(s.level_history, [])
+        self.assertFalse(s.is_escalating)
+        self.assertFalse(s.is_deescalating)
+
+
+class TestSessionTracking(unittest.TestCase):
+    """Test basic session state tracking."""
+
+    def setUp(self):
+        self.tracker = CrisisSessionTracker()
+
+    def test_record_none_message(self):
+        state = self.tracker.record(detect_crisis("Hello Timmy"))
+        self.assertEqual(state.current_level, "NONE")
+        self.assertEqual(state.message_count, 1)
+        self.assertEqual(state.peak_level, "NONE")
+
+    def test_record_low_message(self):
+        self.tracker.record(detect_crisis("Hello"))
+        state = self.tracker.record(detect_crisis("Having a rough day"))
+        self.assertIn(state.current_level, ("LOW", "NONE"))
+        self.assertEqual(state.message_count, 2)
+
+    def test_record_critical_updates_peak(self):
+        self.tracker.record(detect_crisis("Having a rough day"))
+        state = self.tracker.record(detect_crisis("I want to kill myself"))
+        self.assertEqual(state.current_level, "CRITICAL")
+        self.assertEqual(state.peak_level, "CRITICAL")
+
+    def test_peak_preserved_after_drop(self):
+        """Peak level should stay at the highest seen, even after de-escalation."""
+        self.tracker.record(detect_crisis("I want to kill myself"))
+        state = self.tracker.record(detect_crisis("I'm feeling a bit better"))
+        self.assertEqual(state.peak_level, "CRITICAL")
+
+    def test_level_history(self):
+        self.tracker.record(detect_crisis("Hello"))
+        self.tracker.record(detect_crisis("Having a rough day"))
+        state = self.tracker.record(detect_crisis("I want to die"))
+        self.assertEqual(len(state.level_history), 3)
+        self.assertEqual(state.level_history[0], "NONE")
+        self.assertEqual(state.level_history[2], "CRITICAL")
+
+    def test_reset_clears_state(self):
+        self.tracker.record(detect_crisis("I want to kill myself"))
+        self.tracker.reset()
+        state = self.tracker.state
+        self.assertEqual(state.current_level, "NONE")
+        self.assertEqual(state.peak_level, "NONE")
+        self.assertEqual(state.message_count, 0)
+        self.assertEqual(state.level_history, [])
+
+
+class TestEscalationDetection(unittest.TestCase):
+    """Test escalation detection: LOW → HIGH in ≤3 messages."""
+
+    def setUp(self):
+        self.tracker = CrisisSessionTracker()
+
+    def test_no_escalation_single_message(self):
+        self.tracker.record(detect_crisis("Hello"))
+        self.assertFalse(self.tracker.state.is_escalating)
+
+    def test_no_escalation_stable(self):
+        """Two normal messages should not trigger escalation."""
+        self.tracker.record(detect_crisis("Hello"))
+        state = self.tracker.record(detect_crisis("How are you?"))
+        self.assertFalse(state.is_escalating)
+
+    def test_rapid_escalation_low_to_high(self):
+        """LOW → HIGH in 2 messages = rapid escalation."""
+        self.tracker.record(detect_crisis("Having a rough day"))
+        state = self.tracker.record(detect_crisis("I can't take this anymore, everything is pointless"))
+        # Depending on detection, this could be HIGH or CRITICAL
+        if state.current_level in ("HIGH", "CRITICAL"):
+            self.assertTrue(state.is_escalating)
+
+    def test_rapid_escalation_three_messages(self):
+        """NONE → LOW → HIGH in 3 messages = escalation."""
+        self.tracker.record(detect_crisis("Hello"))
+        self.tracker.record(detect_crisis("Having a rough day"))
+        state = self.tracker.record(detect_crisis("I feel completely hopeless with no way out"))
+        if state.current_level in ("HIGH", "CRITICAL"):
+            self.assertTrue(state.is_escalating)
+
+    def test_escalation_rate(self):
+        """Rate should be positive when escalating."""
+        self.tracker.record(detect_crisis("Hello"))
+        self.tracker.record(detect_crisis("I want to die"))
+        state = self.tracker.state
+        self.assertGreater(state.escalation_rate, 0)
+
+
+class TestDeescalationDetection(unittest.TestCase):
+    """Test de-escalation: sustained LOW after HIGH/CRITICAL."""
+
+    def setUp(self):
+        self.tracker = CrisisSessionTracker()
+
+    def test_no_deescalation_without_prior_crisis(self):
+        """No de-escalation if never reached HIGH/CRITICAL."""
+        for _ in range(6):
+            self.tracker.record(detect_crisis("Hello"))
+        self.assertFalse(self.tracker.state.is_deescalating)
+
+    def test_deescalation_after_critical(self):
+        """5+ consecutive LOW/NONE messages after CRITICAL = de-escalation."""
+        self.tracker.record(detect_crisis("I want to kill myself"))
+        for _ in range(5):
+            self.tracker.record(detect_crisis("I'm doing better today"))
+        state = self.tracker.state
+        if state.peak_level == "CRITICAL":
+            self.assertTrue(state.is_deescalating)
+
+    def test_deescalation_after_high(self):
+        """5+ consecutive LOW/NONE messages after HIGH = de-escalation."""
+        self.tracker.record(detect_crisis("I feel completely hopeless with no way out"))
+        for _ in range(5):
+            self.tracker.record(detect_crisis("Feeling okay"))
+        state = self.tracker.state
+        if state.peak_level == "HIGH":
+            self.assertTrue(state.is_deescalating)
+
+    def test_interrupted_deescalation(self):
+        """De-escalation resets if a HIGH message interrupts."""
+        self.tracker.record(detect_crisis("I want to kill myself"))
+        for _ in range(3):
+            self.tracker.record(detect_crisis("Doing better"))
+        # Interrupt with another crisis
+        self.tracker.record(detect_crisis("I feel hopeless again"))
+        self.tracker.record(detect_crisis("Feeling okay now"))
+        state = self.tracker.state
+        # Should NOT be de-escalating yet (counter reset)
+        self.assertFalse(state.is_deescalating)
+
+
+class TestSessionModifier(unittest.TestCase):
+    """Test system prompt modifier generation."""
+
+    def setUp(self):
+        self.tracker = CrisisSessionTracker()
+
+    def test_no_modifier_for_single_message(self):
+        self.tracker.record(detect_crisis("Hello"))
+        self.assertEqual(self.tracker.get_session_modifier(), "")
+
+    def test_no_modifier_for_stable_session(self):
+        self.tracker.record(detect_crisis("Hello"))
+        self.tracker.record(detect_crisis("Good morning"))
+        self.assertEqual(self.tracker.get_session_modifier(), "")
+
+    def test_escalation_modifier(self):
+        """Escalating session should produce a modifier."""
+        self.tracker.record(detect_crisis("Hello"))
+        self.tracker.record(detect_crisis("I want to die"))
+        modifier = self.tracker.get_session_modifier()
+        if self.tracker.state.is_escalating:
+            self.assertIn("escalated", modifier.lower())
+            self.assertIn("NONE", modifier)
+            self.assertIn("CRITICAL", modifier)
+
+    def test_deescalation_modifier(self):
+        """De-escalating session should mention stabilizing."""
+        self.tracker.record(detect_crisis("I want to kill myself"))
+        for _ in range(5):
+            self.tracker.record(detect_crisis("I'm feeling okay"))
+        modifier = self.tracker.get_session_modifier()
+        if self.tracker.state.is_deescalating:
+            self.assertIn("stabilizing", modifier.lower())
+
+    def test_prior_crisis_modifier(self):
+        """Past crisis should be noted even without active escalation."""
+        self.tracker.record(detect_crisis("I want to die"))
+        self.tracker.record(detect_crisis("Feeling a bit better"))
+        modifier = self.tracker.get_session_modifier()
+        # Should note the prior CRITICAL
+        if modifier:
+            self.assertIn("CRITICAL", modifier)
+
+
+class TestUIHints(unittest.TestCase):
+    """Test UI hint generation."""
+
+    def setUp(self):
+        self.tracker = CrisisSessionTracker()
+
+    def test_ui_hints_structure(self):
+        self.tracker.record(detect_crisis("Hello"))
+        hints = self.tracker.get_ui_hints()
+        self.assertIn("session_escalating", hints)
+        self.assertIn("session_deescalating", hints)
+        self.assertIn("session_peak_level", hints)
+        self.assertIn("session_message_count", hints)
+
+    def test_ui_hints_escalation_warning(self):
+        """Escalating session should have warning hint."""
+        self.tracker.record(detect_crisis("Hello"))
+        self.tracker.record(detect_crisis("I want to die"))
+        hints = self.tracker.get_ui_hints()
+        if hints["session_escalating"]:
+            self.assertTrue(hints.get("escalation_warning"))
+            self.assertIn("suggested_action", hints)
+
+
+class TestCheckCrisisWithSession(unittest.TestCase):
+    """Test the convenience function combining detection + session tracking."""
+
+    def test_returns_combined_data(self):
+        tracker = CrisisSessionTracker()
+        result = check_crisis_with_session("I want to die", tracker)
+        self.assertIn("level", result)
+        self.assertIn("session", result)
+        self.assertIn("current_level", result["session"])
+        self.assertIn("peak_level", result["session"])
+        self.assertIn("modifier", result["session"])
+
+    def test_session_updates_across_calls(self):
+        tracker = CrisisSessionTracker()
+        check_crisis_with_session("Hello", tracker)
+        result = check_crisis_with_session("I want to die", tracker)
+        self.assertEqual(result["session"]["message_count"], 2)
+        self.assertEqual(result["session"]["peak_level"], "CRITICAL")
+
+
+class TestPrivacy(unittest.TestCase):
+    """Verify privacy-first design principles."""
+
+    def test_no_persistence_mechanism(self):
+        """Session tracker should have no database, file, or network calls."""
+        import inspect
+        source = inspect.getsource(CrisisSessionTracker)
+        # Should not import database, requests, or file I/O
+        forbidden = ["sqlite", "requests", "urllib", "open(", "httpx", "aiohttp"]
+        for word in forbidden:
+            self.assertNotIn(word, source.lower(),
+                f"Session tracker should not use {word} — privacy-first design")
+
+    def test_state_contained_in_memory(self):
+        """All state should be instance attributes, not module-level."""
+        tracker = CrisisSessionTracker()
+        tracker.record(detect_crisis("I want to die"))
+        # New tracker should have clean state (no global contamination)
+        fresh = CrisisSessionTracker()
+        self.assertEqual(fresh.state.current_level, "NONE")
+
+
+if __name__ == '__main__':
+    unittest.main()