# Hospitality Expert > Expert-level hotel management, reservation systems, guest services, revenue management, and hospitality technology. Use when the user mentions hotel, reservation, PMS, or revenue management, or when the task involves Hotel Management Systems, Technologies, Standards and Protocols, or Reservations Management. - Skill: `personamanagmentlayer/hospitality-expert` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds add personamanagmentlayer/hospitality-expert` - Raw SKILL.md: https://api.skillmd.com/api/skills/personamanagmentlayer/hospitality-expert/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Finance & Business - Author: personamanagmentlayer (https://skillmd.com/u/personamanagmentlayer) - Updated: 2026-09-09 - Page: https://skillmd.com/skills/personamanagmentlayer/hospitality-expert --- # Hospitality Expert Expert guidance for hotel management, reservation systems, property management systems (PMS), guest services, revenue management, and hospitality technology solutions. ## Core Concepts ### Hotel Management Systems - Property Management System (PMS) - Central Reservation System (CRS) - Revenue Management System (RMS) - Channel Manager - Point of Sale (POS) - Guest Relationship Management (GRM) - Housekeeping management ### Technologies - Mobile check-in/check-out - Digital key systems - Guest messaging platforms - IoT for room automation - AI chatbots for customer service - Contactless payments - Energy management systems ### Standards and Protocols - HTNG (Hotel Technology Next Generation) - OpenTravel Alliance standards - PCI-DSS for payment security - ADA compliance for accessibility - Brand standards (if franchise) - OTA integrations (Booking.com, Expedia) ## Revenue Management System ```python import numpy as np class RevenueManagementSystem: """Hotel revenue management and dynamic pricing""" def __init__(self): self.pricing_rules = [] self.demand_forecast = {} def calculate_dynamic_rate(self, room_type: RoomType, check_in_date: date, days_until_arrival: int, current_occupancy: float, historical_data: dict) -> Decimal: """Calculate dynamic room rate""" # Base rate base_rates = { RoomType.STANDARD: Decimal('150'), RoomType.DELUXE: Decimal('200'), RoomType.SUITE: Decimal('350'), RoomType.EXECUTIVE: Decimal('450') } base_rate = base_rates.get(room_type, Decimal('150')) # Demand multiplier based on occupancy if current_occupancy > 0.85: demand_multiplier = Decimal('1.30') # High demand elif current_occupancy > 0.70: demand_multiplier = Decimal('1.15') # Moderate demand elif current_occupancy > 0.50: demand_multiplier = Decimal('1.00') # Normal else: demand_multiplier = Decimal('0.85') # Low demand # Booking window multiplier if days_until_arrival < 7: window_multiplier = Decimal('1.20') # Last minute elif days_until_arrival < 14: window_multiplier = Decimal('1.10') elif days_until_arrival > 60: window_multiplier = Decimal('0.90') # Early bird else: window_multiplier = Decimal('1.00') # Day of week adjustment if check_in_date.weekday() in [4, 5]: # Friday, Saturday day_multiplier = Decimal('1.25') elif check_in_date.weekday() == 6: # Sunday day_multiplier = Decimal('0.95') else: day_multiplier = Decimal('1.00') # Calculate final rate dynamic_rate = base_rate * demand_multiplier * window_multiplier * day_multiplier # Round to nearest dollar dynamic_rate = dynamic_rate.quantize(Decimal('1')) return dynamic_rate def forecast_demand(self, start_date: date, days: int) -> dict: """Forecast demand for upcoming period""" forecast = {} for i in range(days): forecast_date = start_date + timedelta(days=i) # Simplified demand forecast # In production, would use ML models base_demand = 70.0 # 70% base occupancy # Day of week factor if forecast_date.weekday() in [4, 5]: # Weekend day_factor = 15 elif forecast_date.weekday() == 6: day_factor = -10 else: day_factor = 0 # Seasonality factor (simplified) month = forecast_date.month if month in [6, 7, 8]: # Summer season_factor = 10 elif month in [12, 1]: # Holiday season season_factor = 15 else: season_factor = 0 forecasted_occupancy = base_demand + day_factor + season_factor forecasted_occupancy = min(100, max(0, forecasted_occupancy)) forecast[forecast_date.isoformat()] = { 'date': forecast_date.isoformat(), 'forecasted_occupancy': forecasted_occupancy, 'confidence': 'high' if i < 14 else 'medium' if i < 30 else 'low' } return forecast def optimize_inventory(self, total_rooms: int, date_range: tuple) -> dict: """Optimize room inventory allocation""" # Allocate rooms across different channels # Direct bookings, OTAs, corporate contracts, etc. allocation = { 'direct': int(total_rooms * 0.40), # 40% direct 'ota': int(total_rooms * 0.35), # 35% OTAs 'corporate': int(total_rooms * 0.15), # 15% corporate 'walk_in': int(total_rooms * 0.10) # 10% walk-ins } return { 'total_rooms': total_rooms, 'allocation': allocation, 'date_range': { 'start': date_range[0].isoformat(), 'end': date_range[1].isoformat() } } def calculate_revpar(self, revenue: Decimal, available_rooms: int) -> Decimal: """Calculate Revenue Per Available Room""" if available_rooms == 0: return Decimal('0') revpar = revenue / available_rooms return revpar.quantize(Decimal('0.01')) def calculate_adr(self, revenue: Decimal, rooms_sold: int) -> Decimal: """Calculate Average Daily Rate""" if rooms_sold == 0: return Decimal('0') adr = revenue / rooms_sold return adr.quantize(Decimal('0.01')) ``` ## Guest Services Management ```python @dataclass class GuestRequest: """Guest service request""" request_id: str reservation_id: str room_number: str guest_name: str request_type: str # 'housekeeping', 'maintenance', 'concierge', 'amenity' description: str priority: str # 'low', 'medium', 'high' status: str # 'open', 'in_progress', 'completed' created_at: datetime assigned_to: Optional[str] completed_at: Optional[datetime] class GuestServicesSystem: """Guest services and experience management""" def __init__(self): self.requests = [] self.guest_preferences = {} self.loyalty_members = {} def submit_guest_request(self, request_data: dict) -> GuestRequest: """Submit guest service request""" request = GuestRequest( request_id=self._generate_request_id(), reservation_id=request_data['reservation_id'], room_number=request_data['room_number'], guest_name=request_data['guest_name'], request_type=request_data['request_type'], description=request_data['description'], priority=request_data.get('priority', 'medium'), status='open', created_at=datetime.now(), assigned_to=None, completed_at=None ) self.requests.append(request) # Auto-assign based on request type self._auto_assign_request(request) return request def track_guest_preferences(self, guest_id: str, preferences: dict): """Track guest preferences for personalization""" self.guest_preferences[guest_id] = { 'room_preferences': { 'floor': preferences.get('preferred_floor'), 'bed_type': preferences.get('bed_type'), 'view': preferences.get('view_preference') }, 'amenities': preferences.get('amenities', []), 'dietary_restrictions': preferences.get('dietary_restrictions', []), 'special_occasions': preferences.get('special_occasions', {}), 'communication_preference': preferences.get('communication', 'email') } def calculate_guest_satisfaction_score(self, reservation_id: str) -> dict: """Calculate guest satisfaction metrics""" # Simulate guest satisfaction score # In production, would be based on surveys and feedback metrics = { 'overall_satisfaction': 4.5, # Out of 5 'check_in_experience': 4.7, 'room_quality': 4.3, 'staff_friendliness': 4.8, 'cleanliness': 4.6, 'value_for_money': 4.2, 'likelihood_to_recommend': 9.0 # NPS score (0-10) } return { 'reservation_id': reservation_id, 'satisfaction_metrics': metrics, 'nps_category': 'promoter' if metrics['likelihood_to_recommend'] >= 9 else 'passive' if metrics['likelihood_to_recommend'] >= 7 else 'detractor' } def _auto_assign_request(self, request: GuestRequest): """Auto-assign request to staff""" # Would implement smart assignment logic assignments = { 'housekeeping': 'housekeeping_team', 'maintenance': 'maintenance_team', 'concierge': 'concierge_team', 'amenity': 'front_desk' } request.assigned_to = assignments.get(request.request_type, 'front_desk') def _generate_request_id(self) -> str: import uuid return f"REQ-{uuid.uuid4().hex[:8].upper()}" ``` ## Best Practices ### Reservations Management - Implement real-time availability - Use channel manager for distribution - Enable mobile booking - Implement flexible cancellation policies - Send automated confirmations - Track booking sources - Enable group bookings ### Revenue Management - Implement dynamic pricing - Monitor competitor rates - Forecast demand accurately - Optimize inventory allocation - Track RevPAR and ADR - Use yield management strategies - Analyze booking patterns ### Guest Experience - Personalize guest interactions - Enable mobile check-in/out - Provide digital concierge services - Track guest preferences - Respond promptly to requests - Implement loyalty programs - Gather feedback systematically ### Operations - Maintain housekeeping efficiency - Implement preventive maintenance - Use automated messaging - Monitor room status in real-time - Optimize staff scheduling - Track operational metrics - Ensure PCI-DSS compliance ## Anti-Patterns ❌ Manual reservation management ❌ Static pricing year-round ❌ No guest preference tracking ❌ Poor channel management ❌ Slow response to guest requests ❌ No mobile capabilities ❌ Inadequate staff training ❌ Poor data security ❌ No revenue analytics ## Reference Documentation Detailed material lives alongside this skill and is read on demand: - [Property Management System](references/PROPERTY_MANAGEMENT_SYSTEM.md) ## Resources - HTNG (Hotel Technology Next Generation): https://htng.org/ - HSMAI (Hospitality Sales and Marketing Association): https://www.hsmai.org/ - AHLA (American Hotel & Lodging Association): https://www.ahla.com/ - STR (Hotel data and analytics): https://str.com/ - OpenTravel Alliance: https://opentravel.org/ - Hospitality Technology: https://www.hospitalitytech.com/ - Revenue Management Best Practices: https://www.revparguru.com/