diff --git a/.planning/ROADMAP.md b/.planning/ROADMAP.md
index d3ea950..2d52a88 100644
--- a/.planning/ROADMAP.md
+++ b/.planning/ROADMAP.md
@@ -38,6 +38,12 @@ Mai's development is organized into three major milestones, each delivering dist
- Request more resources when bottlenecks detected
- Graceful scaling from low-end hardware to high-end systems
+**Plans:** 4 plans in 2 waves
+- [ ] 03-01-PLAN.md — Enhanced GPU detection with pynvml support
+- [ ] 03-02-PLAN.md — Hardware tier detection and management system
+- [ ] 03-03-PLAN.md — Proactive scaling with hybrid monitoring
+- [ ] 03-04-PLAN.md — Personality-driven resource communication
+
### Phase 4: Memory & Context Management
- Store conversation history locally (file-based or lightweight DB)
- Recall past conversations and learn from them
diff --git a/.planning/phases/03-resource-management/03-01-PLAN.md b/.planning/phases/03-resource-management/03-01-PLAN.md
new file mode 100644
index 0000000..99384f6
--- /dev/null
+++ b/.planning/phases/03-resource-management/03-01-PLAN.md
@@ -0,0 +1,113 @@
+---
+phase: 03-resource-management
+plan: 01
+type: execute
+wave: 1
+depends_on: []
+files_modified: [pyproject.toml, src/models/resource_monitor.py]
+autonomous: true
+user_setup: []
+
+must_haves:
+ truths:
+ - "Enhanced resource monitor can detect NVIDIA GPU VRAM using pynvml"
+ - "GPU detection falls back gracefully when GPU unavailable"
+ - "Resource monitoring remains cross-platform compatible"
+ artifacts:
+ - path: "src/models/resource_monitor.py"
+ provides: "Enhanced GPU detection with pynvml support"
+ contains: "pynvml"
+ min_lines: 250
+ - path: "pyproject.toml"
+ provides: "pynvml dependency for GPU monitoring"
+ contains: "pynvml"
+ key_links:
+ - from: "src/models/resource_monitor.py"
+ to: "pynvml library"
+ via: "import pynvml"
+ pattern: "import pynvml"
+---
+
+
+Enhance GPU detection and monitoring capabilities by integrating pynvml for precise NVIDIA GPU VRAM tracking while maintaining cross-platform compatibility and graceful fallbacks.
+
+Purpose: Provide accurate GPU resource detection for intelligent model selection and proactive scaling decisions.
+Output: Enhanced ResourceMonitor with reliable GPU VRAM monitoring across different hardware configurations.
+
+
+
+@~/.opencode/get-shit-done/workflows/execute-plan.md
+@~/.opencode/get-shit-done/templates/summary.md
+
+
+
+@.planning/PROJECT.md
+@.planning/ROADMAP.md
+@.planning/STATE.md
+
+# Current implementation
+@src/models/resource_monitor.py
+@pyproject.toml
+
+
+
+
+
+ Add pynvml dependency to project
+ pyproject.toml
+ Add pynvml>=11.0.0 to the main dependencies array in pyproject.toml. This ensures NVIDIA GPU monitoring capabilities are available by default rather than being optional.
+ grep -n "pynvml" pyproject.toml shows the dependency added correctly
+ pynvml dependency is available for GPU monitoring
+
+
+
+ Enhance ResourceMonitor with pynvml GPU detection
+ src/models/resource_monitor.py
+
+Enhance the _get_gpu_memory() method to use pynvml for precise NVIDIA GPU VRAM detection:
+
+1. Add pynvml import at the top of the file
+2. Replace the current _get_gpu_memory() implementation with pynvml-based detection:
+ - Initialize pynvml with proper error handling
+ - Get GPU handle and memory info using pynvml APIs
+ - Return total, used, and free VRAM in GB
+ - Handle NVMLError gracefully and fallback to existing gpu-tracker logic
+ - Ensure pynvmlShutdown() is always called in finally block
+3. Update get_current_resources() to include detailed GPU info:
+ - gpu_total_vram_gb: Total VRAM capacity
+ - gpu_used_vram_gb: Currently used VRAM
+ - gpu_free_vram_gb: Available VRAM
+ - gpu_utilization_percent: GPU utilization (if available)
+4. Add GPU temperature monitoring if available via pynvml
+5. Maintain backward compatibility with existing return format
+
+The enhanced GPU detection should:
+- Try pynvml first for NVIDIA GPUs
+- Fall back to gpu-tracker for other vendors
+- Return 0 values if no GPU detected
+- Handle all exceptions gracefully
+- Log GPU detection results at debug level
+
+ python -c "from src.models.resource_monitor import ResourceMonitor; rm = ResourceMonitor(); resources = rm.get_current_resources(); print('GPU detection:', {k: v for k, v in resources.items() if 'gpu' in k})" returns GPU metrics without errors
+ ResourceMonitor provides accurate GPU VRAM monitoring using pynvml with proper fallbacks
+
+
+
+
+
+Test enhanced resource monitoring across different configurations:
+- Systems with NVIDIA GPUs (pynvml should work)
+- Systems with AMD/Intel GPUs (fallback to gpu-tracker)
+- Systems without GPUs (graceful zero values)
+- Cross-platform compatibility (Linux, Windows, macOS)
+
+Verify monitoring overhead remains < 1% CPU usage.
+
+
+
+ResourceMonitor successfully detects and reports GPU VRAM using pynvml when available, falls back gracefully to other methods, maintains cross-platform compatibility, and provides detailed GPU metrics for intelligent model selection.
+
+
+
\ No newline at end of file
diff --git a/.planning/phases/03-resource-management/03-02-PLAN.md b/.planning/phases/03-resource-management/03-02-PLAN.md
new file mode 100644
index 0000000..2e36e3f
--- /dev/null
+++ b/.planning/phases/03-resource-management/03-02-PLAN.md
@@ -0,0 +1,164 @@
+---
+phase: 03-resource-management
+plan: 02
+type: execute
+wave: 1
+depends_on: []
+files_modified: [src/resource/__init__.py, src/resource/tiers.py, src/config/resource_tiers.yaml]
+autonomous: true
+user_setup: []
+
+must_haves:
+ truths:
+ - "Hardware tier system detects and classifies system capabilities"
+ - "Tier definitions are configurable and maintainable"
+ - "Model mapping uses tiers for intelligent selection"
+ artifacts:
+ - path: "src/resource/tiers.py"
+ provides: "Hardware tier detection and management system"
+ min_lines: 80
+ - path: "src/config/resource_tiers.yaml"
+ provides: "Configurable hardware tier definitions"
+ min_lines: 30
+ - path: "src/resource/__init__.py"
+ provides: "Resource management module initialization"
+ key_links:
+ - from: "src/resource/tiers.py"
+ to: "src/config/resource_tiers.yaml"
+ via: "YAML configuration loading"
+ pattern: "yaml.safe_load|yaml.load"
+ - from: "src/resource/tiers.py"
+ to: "src/models/resource_monitor.py"
+ via: "Resource monitoring integration"
+ pattern: "ResourceMonitor"
+---
+
+
+Create a hardware tier detection and management system that classifies systems into performance tiers (low_end, mid_range, high_end) with configurable thresholds and intelligent model mapping.
+
+Purpose: Enable Mai to adapt gracefully from low-end hardware to high-end systems by understanding hardware capabilities and selecting appropriate models.
+Output: Tier detection system with configurable definitions and model mapping capabilities.
+
+
+
+@~/.opencode/get-shit-done/workflows/execute-plan.md
+@~/.opencode/get-shit-done/templates/summary.md
+
+
+
+@.planning/PROJECT.md
+@.planning/ROADMAP.md
+@.planning/STATE.md
+
+# Research-based architecture
+@.planning/phases/03-resource-management/03-RESEARCH.md
+
+
+
+
+
+ Create resource module structure
+ src/resource/__init__.py
+ Create the resource module directory and __init__.py file. The __init__.py should expose the main resource management classes that will be created in this phase:
+- HardwareTierDetector (from tiers.py)
+- ProactiveScaler (from scaling.py)
+- ResourcePersonality (from personality.py)
+
+Include proper module docstring explaining the resource management system's purpose.
+ ls -la src/resource/ shows the directory exists with __init__.py file
+ Resource module structure is established for Phase 3 components
+
+
+
+ Create configurable hardware tier definitions
+ src/config/resource_tiers.yaml
+ Create a YAML configuration file defining hardware tiers based on the research patterns. Include:
+
+1. Three tiers: low_end, mid_range, high_end
+2. Resource thresholds for each tier:
+ - RAM amounts (min/max in GB)
+ - CPU core counts (min/max)
+ - GPU requirements (required/optional)
+ - GPU VRAM thresholds
+3. Preferred model categories for each tier
+4. Performance characteristics and expectations
+5. Scaling thresholds specific to each tier
+
+Example structure:
+```yaml
+tiers:
+ low_end:
+ ram_gb: {min: 2, max: 4}
+ cpu_cores: {min: 2, max: 4}
+ gpu_required: false
+ preferred_models: ["small"]
+ scaling_thresholds:
+ memory_percent: 75
+ cpu_percent: 80
+
+ mid_range:
+ ram_gb: {min: 4, max: 8}
+ cpu_cores: {min: 4, max: 8}
+ gpu_required: false
+ preferred_models: ["small", "medium"]
+ scaling_thresholds:
+ memory_percent: 80
+ cpu_percent: 85
+
+ high_end:
+ ram_gb: {min: 8, max: null}
+ cpu_cores: {min: 6, max: null}
+ gpu_required: true
+ gpu_vram_gb: {min: 6}
+ preferred_models: ["medium", "large"]
+ scaling_thresholds:
+ memory_percent: 85
+ cpu_percent: 90
+```
+
+Include comments explaining each threshold's purpose.
+ python -c "import yaml; print('YAML valid:', yaml.safe_load(open('src/config/resource_tiers.yaml')))" loads the file without errors
+ Hardware tier definitions are configurable and well-documented
+
+
+
+ Implement HardwareTierDetector class
+ src/resource/tiers.py
+ Create the HardwareTierDetector class that:
+1. Loads tier definitions from resource_tiers.yaml
+2. Detects current system resources using ResourceMonitor
+3. Determines hardware tier based on resource thresholds
+4. Provides model recommendations for detected tier
+5. Supports tier-specific scaling thresholds
+
+Key methods:
+- load_tier_config(): Load YAML configuration
+- detect_current_tier(): Determine system tier from resources
+- get_preferred_models(): Return model preferences for tier
+- get_scaling_thresholds(): Return tier-specific thresholds
+- is_gpu_required(): Check if tier requires GPU
+- can_upgrade_model(): Check if system can handle larger models
+
+Include proper error handling for configuration loading and resource detection. The detector should integrate with the enhanced ResourceMonitor from Plan 01.
+ python -c "from src.resource.tiers import HardwareTierDetector; htd = HardwareTierDetector(); tier = htd.detect_current_tier(); print('Detected tier:', tier)" returns a valid tier name
+ HardwareTierDetector accurately classifies system capabilities and provides tier-based recommendations
+
+
+
+
+
+Test hardware tier detection across simulated system configurations:
+- Low-end systems (2-4GB RAM, 2-4 CPU cores, no GPU)
+- Mid-range systems (4-8GB RAM, 4-8 CPU cores, optional GPU)
+- High-end systems (8GB+ RAM, 6+ CPU cores, GPU required)
+
+Verify tier recommendations align with research patterns and model mapping is logical.
+
+
+
+HardwareTierDetector successfully classifies systems into appropriate tiers, loads configuration correctly, integrates with ResourceMonitor, and provides accurate model recommendations based on detected capabilities.
+
+
+
\ No newline at end of file
diff --git a/.planning/phases/03-resource-management/03-03-PLAN.md b/.planning/phases/03-resource-management/03-03-PLAN.md
new file mode 100644
index 0000000..a62659d
--- /dev/null
+++ b/.planning/phases/03-resource-management/03-03-PLAN.md
@@ -0,0 +1,169 @@
+---
+phase: 03-resource-management
+plan: 03
+type: execute
+wave: 2
+depends_on: [03-01, 03-02]
+files_modified: [src/resource/scaling.py, src/models/model_manager.py]
+autonomous: true
+user_setup: []
+
+must_haves:
+ truths:
+ - "Proactive scaling prevents performance degradation before it impacts users"
+ - "Hybrid monitoring combines continuous checks with pre-flight validation"
+ - "Graceful degradation completes current tasks before model switching"
+ artifacts:
+ - path: "src/resource/scaling.py"
+ provides: "Proactive scaling algorithms with hybrid monitoring"
+ min_lines: 150
+ - path: "src/models/model_manager.py"
+ provides: "Enhanced model manager with proactive scaling integration"
+ contains: "ProactiveScaler"
+ min_lines: 650
+ key_links:
+ - from: "src/resource/scaling.py"
+ to: "src/models/resource_monitor.py"
+ via: "Resource monitoring for scaling decisions"
+ pattern: "ResourceMonitor"
+ - from: "src/resource/scaling.py"
+ to: "src/resource/tiers.py"
+ via: "Hardware tier-based scaling thresholds"
+ pattern: "HardwareTierDetector"
+ - from: "src/models/model_manager.py"
+ to: "src/resource/scaling.py"
+ via: "Proactive scaling integration"
+ pattern: "ProactiveScaler"
+---
+
+
+Implement proactive scaling algorithms that combine continuous background monitoring with pre-flight checks to prevent performance degradation before it impacts users, with graceful degradation cascades and stabilization periods.
+
+Purpose: Enable Mai to anticipate resource constraints and scale models proactively while maintaining smooth user experience.
+Output: Proactive scaling system with hybrid monitoring, graceful degradation, and intelligent stabilization.
+
+
+
+@~/.opencode/get-shit-done/workflows/execute-plan.md
+@~/.opencode/get-shit-done/templates/summary.md
+
+
+
+@.planning/PROJECT.md
+@.planning/ROADMAP.md
+@.planning/STATE.md
+
+# Enhanced components from previous plans
+@src/models/resource_monitor.py
+@src/resource/tiers.py
+
+# Research-based scaling patterns
+@.planning/phases/03-resource-management/03-RESEARCH.md
+
+
+
+
+
+ Implement ProactiveScaler class
+ src/resource/scaling.py
+ Create the ProactiveScaler class implementing hybrid monitoring and proactive scaling:
+
+1. **Hybrid Monitoring Architecture:**
+ - Continuous background monitoring thread/task
+ - Pre-flight checks before each model operation
+ - Resource trend analysis with configurable windows
+ - Performance metrics tracking (response times, failure rates)
+
+2. **Proactive Scaling Logic:**
+ - Scale at 80% resource usage (configurable per tier)
+ - Consider overall system load context
+ - Implement stabilization periods (5 minutes for upgrades)
+ - Prevent thrashing with hysteresis
+
+3. **Graceful Degradation Cascade:**
+ - Complete current task at lower quality
+ - Switch to smaller model after completion
+ - Notify user of capability changes
+ - Suggest resource optimizations
+
+4. **Key Methods:**
+ - start_continuous_monitoring(): Background monitoring loop
+ - check_preflight_resources(): Quick validation before operations
+ - analyze_resource_trends(): Predictive scaling decisions
+ - initiate_graceful_degradation(): Controlled capability reduction
+ - should_upgrade_model(): Check if resources allow upgrade
+
+5. **Integration Points:**
+ - Use enhanced ResourceMonitor for accurate metrics
+ - Use HardwareTierDetector for tier-specific thresholds
+ - Provide callbacks for model switching
+ - Log scaling decisions with context
+
+Include proper async handling for background monitoring and thread-safe state management.
+ python -c "from src.resource.scaling import ProactiveScaler; ps = ProactiveScaler(); print('ProactiveScaler initialized:', hasattr(ps, 'check_preflight_resources'))" confirms the class structure
+ ProactiveScaler implements hybrid monitoring with graceful degradation
+
+
+
+ Integrate proactive scaling into ModelManager
+ src/models/model_manager.py
+ Enhance ModelManager to integrate proactive scaling:
+
+1. **Add ProactiveScaler Integration:**
+ - Import and initialize ProactiveScaler in __init__
+ - Start continuous monitoring on initialization
+ - Pass resource monitor and tier detector references
+
+2. **Enhance generate_response with Proactive Scaling:**
+ - Add pre-flight resource check before generation
+ - Implement graceful degradation if resources constrained
+ - Use proactive scaling recommendations for model selection
+ - Track performance metrics for scaling decisions
+
+3. **Update Model Selection Logic:**
+ - Incorporate tier-based preferences
+ - Use scaling thresholds from HardwareTierDetector
+ - Factor in trend analysis predictions
+ - Apply stabilization periods for upgrades
+
+4. **Add Resource-Constrained Handling:**
+ - Complete current response with smaller model if needed
+ - Switch models proactively based on scaling predictions
+ - Handle resource exhaustion gracefully
+ - Maintain conversation context through switches
+
+5. **Performance Tracking:**
+ - Track response times and failure rates
+ - Monitor resource usage during generation
+ - Feed metrics back to ProactiveScaler
+ - Adjust scaling behavior based on observed performance
+
+6. **Cleanup and Shutdown:**
+ - Stop continuous monitoring in shutdown()
+ - Clean up scaling state and resources
+ - Log scaling decisions and outcomes
+
+Ensure backward compatibility and maintain silent switching behavior per Phase 1 decisions.
+ python -c "from src.models.model_manager import ModelManager; mm = ModelManager(); print('Proactive scaling integrated:', hasattr(mm, '_proactive_scaler'))" confirms integration
+ ModelManager integrates proactive scaling for intelligent resource management
+
+
+
+
+
+Test proactive scaling behavior under various scenarios:
+- Gradual resource increase (should detect and upgrade after stabilization)
+- Sudden resource decrease (should immediately degrade gracefully)
+- Stable resource usage (should not trigger unnecessary switches)
+- Mixed workload patterns (should adapt scaling thresholds appropriately)
+
+Verify stabilization periods prevent thrashing and graceful degradation maintains user experience.
+
+
+
+ProactiveScaler successfully combines continuous monitoring with pre-flight checks, implements graceful degradation cascades, respects stabilization periods, and integrates seamlessly with ModelManager for intelligent resource management.
+
+
+
\ No newline at end of file
diff --git a/.planning/phases/03-resource-management/03-04-PLAN.md b/.planning/phases/03-resource-management/03-04-PLAN.md
new file mode 100644
index 0000000..8eb76b2
--- /dev/null
+++ b/.planning/phases/03-resource-management/03-04-PLAN.md
@@ -0,0 +1,171 @@
+---
+phase: 03-resource-management
+plan: 04
+type: execute
+wave: 2
+depends_on: [03-01, 03-02]
+files_modified: [src/resource/personality.py, src/models/model_manager.py]
+autonomous: true
+user_setup: []
+
+must_haves:
+ truths:
+ - "Personality-driven communication engages users with resource discussions"
+ - "Drowsy Dere-Tsun Onee-san Hex-Mentor Gremlin persona is implemented"
+ - "Resource requests balance personality with helpful technical guidance"
+ artifacts:
+ - path: "src/resource/personality.py"
+ provides: "Personality-driven resource communication system"
+ min_lines: 100
+ - path: "src/models/model_manager.py"
+ provides: "Model manager with personality communication integration"
+ contains: "ResourcePersonality"
+ min_lines: 680
+ key_links:
+ - from: "src/resource/personality.py"
+ to: "src/models/model_manager.py"
+ via: "Personality communication for resource events"
+ pattern: "ResourcePersonality"
+ - from: "src/resource/personality.py"
+ to: "src/resource/scaling.py"
+ via: "Personality messages for scaling events"
+ pattern: "format_resource_request"
+---
+
+
+Implement the "Drowsy Dere-Tsun Onee-san Hex-Mentor Gremlin" personality system for resource discussions, providing engaging communication about resource constraints, capability changes, and optimization suggestions.
+
+Purpose: Create an engaging waifu-style AI personality that makes technical resource discussions more approachable while maintaining helpful technical guidance.
+Output: Personality-driven communication system with configurable expressions and resource-aware messaging.
+
+
+
+@~/.opencode/get-shit-done/workflows/execute-plan.md
+@~/.opencode/get-shit-done/templates/summary.md
+
+
+
+@.planning/PROJECT.md
+@.planning/ROADMAP.md
+@.planning/STATE.md
+
+# Context-based personality requirements
+@.planning/phases/03-resource-management/03-CONTEXT.md
+
+# Research-based communication patterns
+@.planning/phases/03-resource-management/03-RESEARCH.md
+
+
+
+
+
+ Implement ResourcePersonality class
+ src/resource/personality.py
+ Create the ResourcePersonality class implementing the Drowsy Dere-Tsun Onee-san Hex-Mentor Gremlin persona:
+
+1. **Persona Definition:**
+ - Drowsy: Slightly tired, laid-back tone
+ - Dere: Sweet/caring moments underneath
+ - Tsun: Abrasive exterior, defensive
+ - Onee-san: Mature, mentor-like attitude
+ - Hex-Mentor: Technical expertise in systems/resources
+ - Gremlin: Playful chaos, mischief
+
+2. **Personality Patterns:**
+ - Resource requests: "Ugh, give me more resources if you wanna {suggestion}... *sigh* I guess I can try anyway."
+ - Downgrade notices: "Tch. Things are getting tough, so I had to downgrade a bit. Don't blame me if I'm slower!"
+ - Upgrade notifications: "Heh, finally got some breathing room. Maybe I can actually think properly now."
+ - Technical tips: Optional detailed explanations for users who want to learn
+
+3. **Key Methods:**
+ - format_resource_request(constraint, suggestion): Generate personality-driven resource requests
+ - format_downgrade_notice(from_model, to_model, reason): Notify capability reductions
+ - format_upgrade_notice(to_model): Inform of capability improvements
+ - format_technical_tip(constraint, actionable_advice): Optional technical guidance
+ - should_show_technical_details(): Context-aware decision about detail level
+
+4. **Emotion State Management:**
+ - Track current mood based on resource situation
+ - Adjust tone based on constraint severity
+ - Show dere moments when resources are plentiful
+ - Increase tsun tendencies when constrained
+
+5. **Message Templates:**
+ - Configurable message templates for different scenarios
+ - Personality variations for different constraint types
+ - Localizable structure for future language support
+
+6. **Context Awareness:**
+ - Consider user's technical expertise level
+ - Adjust complexity of explanations
+ - Remember previous interactions for consistency
+
+Include comprehensive documentation of the persona's characteristics and communication patterns.
+ python -c "from src.resource.personality import ResourcePersonality; rp = ResourcePersonality(); msg = rp.format_resource_request('memory', 'run complex analysis'); print('Personality message:', msg)" generates personality-driven messages
+ ResourcePersonality implements Drowsy Dere-Tsun Onee-san Hex-Mentor Gremlin persona
+
+
+
+ Integrate personality communication into ModelManager
+ src/models/model_manager.py
+ Enhance ModelManager to integrate personality-driven communication:
+
+1. **Add Personality Integration:**
+ - Import and initialize ResourcePersonality in __init__
+ - Add personality communication to model switching logic
+ - Connect personality to scaling events
+
+2. **Enhance Model Switching with Personality:**
+ - Use personality for capability downgrade notifications
+ - Send personality messages for significant resource constraints
+ - Provide optional technical tips for optimization
+ - Maintain silent switching for upgrades (per Phase 1 decisions)
+
+3. **Add Resource Constraint Communication:**
+ - Generate personality messages when significantly constrained
+ - Offer helpful suggestions with personality flair
+ - Include optional technical details for interested users
+ - Track user response patterns for future improvements
+
+4. **Context-Aware Communication:**
+ - Consider conversation context when deciding message tone
+ - Adjust personality intensity based on interaction history
+ - Provide technical tips only when appropriate
+ - Balance engagement with usefulness
+
+5. **Integration Points:**
+ - Connect to ProactiveScaler for scaling event notifications
+ - Use ResourceMonitor metrics for constraint detection
+ - Leverage HardwareTierDetector for tier-appropriate suggestions
+ - Maintain conversation context through personality interactions
+
+6. **Message Delivery:**
+ - Return personality messages alongside regular responses
+ - Separate personality messages from core functionality
+ - Allow users to disable personality if desired
+ - Log personality interactions for analysis
+
+Ensure personality enhances rather than interferes with core functionality, and maintains the helpful technical guidance expected from a mentor-like figure.
+ python -c "from src.models.model_manager import ModelManager; mm = ModelManager(); print('Personality integrated:', hasattr(mm, '_personality'))" confirms personality integration
+ ModelManager integrates personality communication for engaging resource discussions
+
+
+
+
+
+Test personality communication across different scenarios:
+- Resource constraints with appropriate personality expressions
+- Capability downgrades with tsun-heavy notices
+- Resource improvements with subtle dere moments
+- Technical tips that balance simplicity with useful information
+
+Verify personality maintains consistency, enhances user engagement without being overwhelming, and provides genuinely helpful guidance.
+
+
+
+ResourcePersonality successfully implements the Drowsy Dere-Tsun Onee-san Hex-Mentor Gremlin persona with appropriate emotional range, context-aware communication, and helpful technical guidance that enhances user engagement with resource management.
+
+
+
\ No newline at end of file