refactor: Transform daemon_boyfriend into Loyal Companion

Rebrand and personalize the bot as 'Bartender' - a companion for those
who love deeply and feel intensely.

Major changes:
- Rename package: daemon_boyfriend -> loyal_companion
- New default personality: Bartender - wise, steady, non-judgmental
- Grief-aware system prompt (no toxic positivity, attachment-informed)
- New relationship levels: New Face -> Close Friend progression
- Bartender-style mood modifiers (steady presence)
- New fact types: attachment_pattern, grief_context, coping_mechanism
- Lower mood decay (0.05) for emotional stability
- Higher fact extraction rate (0.4) - Bartender pays attention

Updated all imports, configs, Docker files, and documentation.

CLAUDE.md

@@ -12,16 +12,13 @@ pip install -r requirements.txt
 pip install -e .
 
 # Run the bot (requires .env with DISCORD_TOKEN and AI provider key)
-python -m daemon_boyfriend
+python -m loyal_companion
 
 # Run with Docker (includes PostgreSQL)
 docker-compose up -d
 
-# Run database migrations
-alembic upgrade head
-
 # Syntax check all Python files
-python -m py_compile src/daemon_boyfriend/**/*.py
+python -m py_compile src/loyal_companion/**/*.py
 ```
 
 ## Testing
@@ -34,7 +31,7 @@ pip install -e ".[dev]"
 python -m pytest tests/ -v
 
 # Run tests with coverage
-python -m pytest tests/ --cov=daemon_boyfriend --cov-report=term-missing
+python -m pytest tests/ --cov=loyal_companion --cov-report=term-missing
 
 # Run specific test file
 python -m pytest tests/test_models.py -v
@@ -50,7 +47,7 @@ The test suite uses:
 
 ## Architecture
 
-This is a Discord bot that responds to @mentions with AI-generated responses (multi-provider support). It features a "Living AI" system that gives the bot personality, mood, and relationship tracking.
+Loyal Companion is a Discord bot companion for those who love deeply and feel intensely. It features a "Living AI" system called Bartender - a wise, steady presence who listens without judgment, understands attachment theory, and knows when to offer perspective versus when to just hold space.
 
 ### Provider Pattern
 The AI system uses a provider abstraction pattern:
@@ -75,11 +72,10 @@ The bot uses PostgreSQL for persistent memory (optional, falls back to in-memory
 - `services/database.py` - Connection pool and async session management
 - `services/user_service.py` - User CRUD, custom names, facts management
 - `services/persistent_conversation.py` - Database-backed conversation history
-- `alembic/` - Database migrations
 
 Key features:
 - Custom names: Set preferred names for users so the bot knows "who is who"
-- User facts: Bot remembers things about users (hobbies, preferences, etc.)
+- User facts: Bot remembers things about users (hobbies, preferences, attachment patterns, grief context)
 - Persistent conversations: Chat history survives restarts
 - Conversation timeout: New conversation starts after 60 minutes of inactivity
 
@@ -88,8 +84,8 @@ The bot implements a "Living AI" system with emotional depth and relationship tr
 
 #### Services (`services/`)
 - `mood_service.py` - Valence-arousal mood model with time decay
-- `relationship_service.py` - Relationship scoring (stranger to close friend)
-- `fact_extraction_service.py` - Autonomous fact learning from conversations
+- `relationship_service.py` - Relationship scoring (new face to close friend)
+- `fact_extraction_service.py` - Autonomous fact learning from conversations (including attachment patterns, grief context, coping mechanisms)
 - `opinion_service.py` - Bot develops opinions on topics over time
 - `self_awareness_service.py` - Bot statistics and self-reflection
 - `communication_style_service.py` - Learns user communication preferences
@@ -110,14 +106,14 @@ Uses a valence-arousal model:
 - Valence: -1 (sad) to +1 (happy)
 - Arousal: -1 (calm) to +1 (excited)
 - Labels: excited, happy, calm, neutral, bored, annoyed, curious
-- Time decay: Mood gradually returns to neutral
+- Time decay: Mood gradually returns to neutral (slower decay = steadier presence)
 
 #### Relationship Levels
-- Stranger (0-20): Polite, formal
-- Acquaintance (21-40): Friendly but reserved
-- Friend (41-60): Casual, warm
-- Good Friend (61-80): Personal, references past talks
-- Close Friend (81-100): Very casual, inside jokes
+- New Face (0-20): Warm but observant - "Pull up a seat" energy
+- Getting to Know You (21-40): Building trust, remembering details
+- Regular (41-60): Comfortable familiarity - "Your usual?"
+- Good Friend (61-80): Real trust, can be honest even when hard
+- Close Friend (81-100): Deep bond, full honesty, reflects patterns with love
 
 ### Configuration
 All config flows through `config.py` using pydantic-settings. The `settings` singleton is created at module load, so env vars must be set before importing.
@@ -153,12 +149,12 @@ Optional:
 - `MOOD_ENABLED` - Enable mood system (default: true)
 - `RELATIONSHIP_ENABLED` - Enable relationship tracking (default: true)
 - `FACT_EXTRACTION_ENABLED` - Enable autonomous fact extraction (default: true)
-- `FACT_EXTRACTION_RATE` - Probability of extracting facts (default: 0.3)
+- `FACT_EXTRACTION_RATE` - Probability of extracting facts (default: 0.4)
 - `PROACTIVE_ENABLED` - Enable proactive messages (default: true)
 - `CROSS_USER_ENABLED` - Enable cross-user memory associations (default: false)
 - `OPINION_FORMATION_ENABLED` - Enable bot opinion formation (default: true)
 - `STYLE_LEARNING_ENABLED` - Enable communication style learning (default: true)
-- `MOOD_DECAY_RATE` - How fast mood returns to neutral per hour (default: 0.1)
+- `MOOD_DECAY_RATE` - How fast mood returns to neutral per hour (default: 0.05)
 
 ### Command Toggles
 - `COMMANDS_ENABLED` - Master switch for all commands (default: true)