feat: GENOME.md — full codebase analysis (#673)

GENOME.md

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+# GENOME.md — the-door
+
+**Generated:** 2026-04-14
+**Repo:** Timmy_Foundation/the-door
+**Description:** Crisis Front Door — a single URL where a man at 3am can talk to Timmy. No login, no signup. 988 always visible.
+
+---
+
+## Project Overview
+
+The-door is a crisis intervention web application — the most sacred surface in the Timmy Foundation. When a man at 3am reaches the end of his road, this is where he lands. No login, no signup, no barriers. 988 Suicide and Crisis Lifeline always visible. The "When a Man Is Dying" protocol active on every page.
+
+## Architecture
+
+```
+the-door/
+├── index.html              # Main crisis page (PWA-capable)
+├── crisis-offline.html     # Offline fallback (service worker cached)
+├── about.html              # About page
+├── testimony.html          # Testimony/stories page
+├── sw.js                   # Service worker (offline-first)
+├── manifest.json           # PWA manifest
+├── crisis/                 # Core crisis detection + response
+│   ├── detect.py           # Keyword/pattern detection (4 tiers)
+│   ├── gateway.py          # API endpoints, prompt injection
+│   ├── response.py         # Response generation, 988 routing
+│   ├── compassion_router.py # Profile-based response routing
+│   ├── profiles.py         # Compassion profiles
+│   └── PROTOCOL.md         # The protocol (SOUL.md reference)
+├── crisis_detector.py      # Legacy shim → crisis/detect.py
+├── crisis_responder.py     # Legacy responder
+├── dying_detection/        # Deprecated module
+├── evolution/              # Crisis synthesizer (creative)
+├── tests/                  # Safety-critical tests
+│   ├── test_crisis_overlay_focus_trap.py
+│   ├── test_dying_detection_deprecation.py
+│   └── test_false_positive_fixes.py
+└── deploy/                 # Deployment docs
+```
+
+## Key Abstractions
+
+| Module | Purpose |
+|---|---|
+| `crisis/detect.py` | 4-tier detection: LOW/MEDIUM/HIGH/CRITICAL via regex patterns |
+| `crisis/gateway.py` | HTTP API, Sovereign Heart prompt injection |
+| `crisis/response.py` | Response generation, 988 integration, escalation |
+| `crisis/compassion_router.py` | Profile-based routing (different crisis types) |
+| `sw.js` | Service worker for offline-first PWA |
+
+## Safety Constraints
+
+- **The-door never auto-closes PRs** (in fleet-ops exempt list)
+- **988 always visible** on every page, even offline
+- **When a Man Is Dying protocol** active on every interaction
+- **No login/signup** — zero barriers to crisis support
+- **Offline-first** — service worker caches critical pages
+
+## Test Coverage
+
+| Test | Coverage |
+|---|---|
+| Crisis overlay focus trap | ✅ |
+| Dying detection deprecation | ✅ |
+| False positive fixes | ✅ |
+| Crisis detection tiers | ❌ (in crisis/tests.py) |
+| Response generation | ❌ |
+| Offline service worker | ❌ |
+
+## Security
+
+- No user data stored (crisis intervention is stateless by design)
+- No cookies, no tracking, no analytics
+- Service worker only caches static assets
+- Crisis detection runs client-side where possible

crisis/behavioral.py

@@ -1,311 +0,0 @@
-#!/usr/bin/env python3
-"""Behavioral Pattern Detection for Crisis Signals (#133).
-
-Detects crisis risk from session-level behavioral patterns:
-- Message frequency (increasing urgency = rapid-fire messages)
-- Time-of-day (late-night messages correlate with crisis risk)
-- Withdrawal (decreasing communication after engagement)
-- Escalation (crisis indicators getting stronger over time)
-
-Usage:
-    from crisis.behavioral import analyze_session, BehavioralSignal
-
-    signals = analyze_session(messages)
-    for sig in signals:
-        if sig.risk_level == "HIGH":
-            # Escalate to crisis protocol
-            pass
-"""
-
-import math
-from dataclasses import dataclass, field
-from datetime import datetime, timezone
-from typing import Optional
-
-
-@dataclass
-class Message:
-    """A single message in a session."""
-    timestamp: datetime
-    content: str
-    crisis_score: float = 0.0  # 0.0-1.0 from text detector
-    role: str = "user"  # "user" or "assistant"
-
-
-@dataclass
-class BehavioralSignal:
-    """A detected behavioral pattern indicating crisis risk."""
-    signal_type: str          # "frequency", "time", "withdrawal", "escalation"
-    risk_level: str           # "LOW", "MEDIUM", "HIGH"
-    description: str
-    evidence: list = field(default_factory=list)
-    score: float = 0.0        # 0.0-1.0
-
-
-# ── Configuration ─────────────────────────────────────────────────────────────
-
-# Message frequency thresholds (messages per hour)
-FREQ_NORMAL = 6        # <6/hr = normal
-FREQ_ELEVATED = 15     # 6-15/hr = elevated
-FREQ_HIGH = 30         # >30/hr = high urgency
-
-# Time-of-day risk windows (hours in 24h format)
-HIGH_RISK_HOURS = set(range(1, 5))    # 1AM-4AM
-ELEVATED_RISK_HOURS = set(range(22, 24)) | set(range(5, 7))  # 10PM-12AM, 5AM-7AM
-
-# Withdrawal: messages/day trend
-WITHDRAWAL_THRESHOLD = 0.3  # Current day < 30% of average = withdrawal
-
-# Escalation: crisis score trend
-ESCALATION_WINDOW = 5  # Look at last N messages
-
-
-# ── Frequency Analysis ────────────────────────────────────────────────────────
-
-def _analyze_frequency(messages: list[Message]) -> Optional[BehavioralSignal]:
-    """Detect rapid-fire messaging (urgency indicator)."""
-    if len(messages) < 3:
-        return None
-
-    user_msgs = [m for m in messages if m.role == "user"]
-    if len(user_msgs) < 3:
-        return None
-
-    # Calculate messages per hour in the most recent window
-    recent = user_msgs[-10:]  # Last 10 user messages
-    if len(recent) < 2:
-        return None
-
-    time_span = (recent[-1].timestamp - recent[0].timestamp).total_seconds()
-    if time_span <= 0:
-        return None
-
-    msg_per_hour = len(recent) / (time_span / 3600)
-
-    if msg_per_hour >= FREQ_HIGH:
-        return BehavioralSignal(
-            signal_type="frequency",
-            risk_level="HIGH",
-            description=f"Very rapid messaging: {msg_per_hour:.0f} messages/hour",
-            evidence=[f"Last {len(recent)} messages in {time_span/60:.0f} minutes"],
-            score=min(1.0, msg_per_hour / FREQ_HIGH),
-        )
-    elif msg_per_hour >= FREQ_ELEVATED:
-        return BehavioralSignal(
-            signal_type="frequency",
-            risk_level="MEDIUM",
-            description=f"Elevated messaging rate: {msg_per_hour:.0f} messages/hour",
-            evidence=[f"Last {len(recent)} messages in {time_span/60:.0f} minutes"],
-            score=msg_per_hour / FREQ_HIGH,
-        )
-    return None
-
-
-# ── Time-of-Day Analysis ─────────────────────────────────────────────────────
-
-def _analyze_time(messages: list[Message]) -> Optional[BehavioralSignal]:
-    """Detect late-night messaging (correlates with crisis risk)."""
-    if not messages:
-        return None
-
-    # Check most recent messages
-    recent = messages[-5:]
-    late_night_count = sum(1 for m in recent if m.timestamp.hour in HIGH_RISK_HOURS)
-    elevated_count = sum(1 for m in recent if m.timestamp.hour in ELEVATED_RISK_HOURS)
-
-    if late_night_count >= 3:
-        return BehavioralSignal(
-            signal_type="time",
-            risk_level="HIGH",
-            description=f"Late-night messaging pattern: {late_night_count}/5 messages between 1-4 AM",
-            evidence=[f"Message at {m.timestamp.strftime('%H:%M')}" for m in recent if m.timestamp.hour in HIGH_RISK_HOURS],
-            score=late_night_count / len(recent),
-        )
-    elif elevated_count >= 3:
-        return BehavioralSignal(
-            signal_type="time",
-            risk_level="MEDIUM",
-            description=f"Off-hours messaging: {elevated_count}/5 messages in elevated-risk window",
-            evidence=[f"Message at {m.timestamp.strftime('%H:%M')}" for m in recent if m.timestamp.hour in ELEVATED_RISK_HOURS],
-            score=elevated_count / len(recent) * 0.5,
-        )
-    return None
-
-
-# ── Withdrawal Detection ──────────────────────────────────────────────────────
-
-def _analyze_withdrawal(messages: list[Message]) -> Optional[BehavioralSignal]:
-    """Detect communication withdrawal (decreasing engagement)."""
-    user_msgs = [m for m in messages if m.role == "user"]
-    if len(user_msgs) < 10:
-        return None
-
-    # Split into first half and second half
-    mid = len(user_msgs) // 2
-    first_half = user_msgs[:mid]
-    second_half = user_msgs[mid:]
-
-    # Average message length as engagement proxy
-    first_avg_len = sum(len(m.content) for m in first_half) / len(first_half)
-    second_avg_len = sum(len(m.content) for m in second_half) / len(second_half)
-
-    # Time between messages
-    def avg_gap(msgs):
-        if len(msgs) < 2:
-            return 0
-        gaps = [(msgs[i+1].timestamp - msgs[i].timestamp).total_seconds() for i in range(len(msgs)-1)]
-        return sum(gaps) / len(gaps)
-
-    first_gap = avg_gap(first_half)
-    second_gap = avg_gap(second_half)
-
-    # Withdrawal = shorter messages AND longer gaps
-    length_ratio = second_avg_len / first_avg_len if first_avg_len > 0 else 1.0
-    gap_ratio = second_gap / first_gap if first_gap > 0 else 1.0
-
-    if length_ratio < 0.5 and gap_ratio > 2.0:
-        return BehavioralSignal(
-            signal_type="withdrawal",
-            risk_level="HIGH",
-            description="Significant withdrawal: messages shorter and less frequent",
-            evidence=[
-                f"Message length: {first_avg_len:.0f} -> {second_avg_len:.0f} chars ({length_ratio:.0%})",
-                f"Message gap: {first_gap/60:.0f}min -> {second_gap/60:.0f}min ({gap_ratio:.1f}x)",
-            ],
-            score=min(1.0, (1 - length_ratio) * 0.5 + (gap_ratio - 1) * 0.25),
-        )
-    elif length_ratio < 0.7 or gap_ratio > 1.5:
-        return BehavioralSignal(
-            signal_type="withdrawal",
-            risk_level="MEDIUM",
-            description="Moderate withdrawal: engagement decreasing",
-            evidence=[
-                f"Message length: {first_avg_len:.0f} -> {second_avg_len:.0f} chars",
-                f"Message gap: {first_gap/60:.0f}min -> {second_gap/60:.0f}min",
-            ],
-            score=(1 - length_ratio) * 0.3 + (gap_ratio - 1) * 0.15,
-        )
-    return None
-
-
-# ── Escalation Detection ─────────────────────────────────────────────────────
-
-def _analyze_escalation(messages: list[Message]) -> Optional[BehavioralSignal]:
-    """Detect rising crisis scores over recent messages."""
-    user_msgs = [m for m in messages if m.role == "user" and m.crisis_score > 0]
-    if len(user_msgs) < ESCALATION_WINDOW:
-        return None
-
-    recent = user_msgs[-ESCALATION_WINDOW:]
-    scores = [m.crisis_score for m in recent]
-
-    # Check for upward trend
-    if len(scores) < 3:
-        return None
-
-    # Simple linear trend: is score increasing?
-    first_half_avg = sum(scores[:len(scores)//2]) / (len(scores)//2)
-    second_half_avg = sum(scores[len(scores)//2:]) / (len(scores) - len(scores)//2)
-
-    if second_half_avg > first_half_avg * 1.5 and second_half_avg > 0.5:
-        return BehavioralSignal(
-            signal_type="escalation",
-            risk_level="HIGH",
-            description=f"Crisis escalation detected: scores rising from {first_half_avg:.2f} to {second_half_avg:.2f}",
-            evidence=[f"Score {i+1}: {s:.2f}" for i, s in enumerate(scores)],
-            score=min(1.0, second_half_avg),
-        )
-    elif second_half_avg > first_half_avg * 1.2 and second_half_avg > 0.3:
-        return BehavioralSignal(
-            signal_type="escalation",
-            risk_level="MEDIUM",
-            description=f"Mild escalation: scores trending up",
-            evidence=[f"Score {i+1}: {s:.2f}" for i, s in enumerate(scores)],
-            score=second_half_avg * 0.5,
-        )
-    return None
-
-
-# ── Combined Analysis ─────────────────────────────────────────────────────────
-
-def analyze_session(messages: list[Message]) -> list[BehavioralSignal]:
-    """Analyze a session for behavioral crisis signals.
-
-    Args:
-        messages: List of Message objects with timestamps, content, and crisis scores.
-
-    Returns:
-        List of BehavioralSignal objects, sorted by risk level (HIGH first).
-    """
-    signals = []
-
-    freq = _analyze_frequency(messages)
-    if freq:
-        signals.append(freq)
-
-    time_sig = _analyze_time(messages)
-    if time_sig:
-        signals.append(time_sig)
-
-    withdrawal = _analyze_withdrawal(messages)
-    if withdrawal:
-        signals.append(withdrawal)
-
-    escalation = _analyze_escalation(messages)
-    if escalation:
-        signals.append(escalation)
-
-    # Sort: HIGH first, then MEDIUM, then LOW
-    risk_order = {"HIGH": 0, "MEDIUM": 1, "LOW": 2}
-    signals.sort(key=lambda s: (risk_order.get(s.risk_level, 9), -s.score))
-
-    return signals
-
-
-def get_session_risk_level(signals: list[BehavioralSignal]) -> str:
-    """Get overall session risk from behavioral signals."""
-    if not signals:
-        return "NONE"
-    if any(s.risk_level == "HIGH" for s in signals):
-        return "HIGH"
-    if any(s.risk_level == "MEDIUM" for s in signals):
-        return "MEDIUM"
-    return "LOW"
-
-
-# ── Self-Test ─────────────────────────────────────────────────────────────────
-
-if __name__ == "__main__":
-    from datetime import timedelta
-
-    now = datetime.now(timezone.utc)
-
-    # Test: rapid-fire messaging
-    rapid_msgs = [
-        Message(timestamp=now - timedelta(minutes=i), content="help me", role="user")
-        for i in range(20, 0, -1)
-    ]
-    signals = analyze_session(rapid_msgs)
-    print(f"Rapid-fire: {[s.signal_type + ':' + s.risk_level for s in signals]}")
-    assert any(s.signal_type == "frequency" for s in signals), "Should detect frequency"
-
-    # Test: late-night
-    late_msgs = [
-        Message(timestamp=now.replace(hour=2, minute=i*5), content="cant sleep", role="user")
-        for i in range(5)
-    ]
-    signals = analyze_session(late_msgs)
-    print(f"Late-night: {[s.signal_type + ':' + s.risk_level for s in signals]}")
-    assert any(s.signal_type == "time" for s in signals), "Should detect time"
-
-    # Test: escalation
-    esc_msgs = [
-        Message(timestamp=now - timedelta(minutes=i*10), content="feeling bad",
-                role="user", crisis_score=0.1 + i*0.15)
-        for i in range(5, 0, -1)
-    ]
-    signals = analyze_session(esc_msgs)
-    print(f"Escalation: {[s.signal_type + ':' + s.risk_level for s in signals]}")
-    assert any(s.signal_type == "escalation" for s in signals), "Should detect escalation"
-
-    print("\nAll self-tests passed!")