Expert guidance for real estate systems, property management, Multiple Listing Service (MLS) integration, customer relationship management, virtual tours, and market analysis.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionreal-estate-expertExecute the skills CLI command in your project's root directory to begin installation:
Fetches real-estate-expert from personamanagmentlayer/pcl and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate real-estate-expert. Access via /real-estate-expert in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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Expert guidance for real estate systems, property management, Multiple Listing Service (MLS) integration, customer relationship management, virtual tours, and market analysis.
from dataclasses import dataclass
from datetime import datetime
from decimal import Decimal
from typing import List, Optional
from enum import Enum
class PropertyType(Enum):
SINGLE_FAMILY = "single_family"
CONDO = "condo"
TOWNHOUSE = "townhouse"
MULTI_FAMILY = "multi_family"
LAND = "land"
COMMERCIAL = "commercial"
class ListingStatus(Enum):
ACTIVE = "active"
PENDING = "pending"
SOLD = "sold"
WITHDRAWN = "withdrawn"
EXPIRED = "expired"
@dataclass
class Property:
"""Property information"""
property_id: str
mls_number: str
property_type: PropertyType
address: dict
listing_price: Decimal
bedrooms: int
bathrooms: float
square_feet: int
lot_size: float # acres
year_built: int
description: str
features: List[str]
photos: List[str]
status: ListingStatus
listing_date: datetime
listing_agent_id: str
coordinates: tuple # (latitude, longitude)
@dataclass
class ShowingRequest:
"""Property showing request"""
showing_id: str
property_id: str
buyer_agent_id: str
buyer_name: str
requested_date: datetime
duration_minutes: int
status: str # 'pending', 'confirmed', 'cancelled'
notes: str
class PropertyListingSystem:
"""Real estate listing management system"""
def __init__(self):
self.properties = {}
self.showings = []
self.saved_searches = {}
def create_listing(self,
property_data: dict,
agent_id: str) -> Property:
"""Create new property listing"""
property_id = self._generate_property_id()
mls_number = self._generate_mls_number()
property = Property(
property_id=property_id,
mls_number=mls_number,
property_type=PropertyType(property_data['property_type']),
address=property_data['address'],
listing_price=Decimal(str(property_data['price'])),
bedrooms=property_data['bedrooms'],
bathrooms=property_data['bathrooms'],
square_feet=property_data['square_feet'],
lot_size=property_data.get('lot_size', 0),
year_built=property_data['year_built'],
description=property_data['description'],
features=property_data.get('features', []),
photos=property_data.get('photos', []),
status=ListingStatus.ACTIVE,
listing_date=datetime.now(),
listing_agent_id=agent_id,
coordinates=property_data.get('coordinates', (0, 0))
)
self.properties[property_id] = property
# Notify matching saved searches
self._notify_saved_searches(property)
return property
def search_properties(self, criteria: dict) -> List[Property]:
"""Search properties based on criteria"""
results = []
for property in self.properties.values():
if property.status != ListingStatus.ACTIVE:
continue
# Price range
if 'min_price' in criteria:
if property.listing_price < Decimal(str(criteria['min_price'])):
continue
if 'max_price' in criteria:
if property.listing_price > Decimal(str(criteria['max_price'])):
continue
# Bedrooms
if 'min_bedrooms' in criteria:
if property.bedrooms < criteria['min_bedrooms']:
continue
# Bathrooms
if 'min_bathrooms' in criteria:
if property.bathrooms < criteria['min_bathrooms']:
continue
# Square footage
if 'min_sqft' in criteria:
if property.square_feet < criteria['min_sqft']:
continue
# Property type
if 'property_type' in criteria:
if property.property_type.value != criteria['property_type']:
continue
# Location-based search (wMake data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
erichowens/some_claude_skills
sickn33/antigravity-awesome-skills
erichowens/some_claude_skills
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
real-estate-expert is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
real-estate-expert reduced setup friction for our internal harness; good balance of opinion and flexibility.
real-estate-expert fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
We added real-estate-expert from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Solid pick for teams standardizing on skills: real-estate-expert is focused, and the summary matches what you get after install.
I recommend real-estate-expert for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Useful defaults in real-estate-expert — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
I recommend real-estate-expert for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
I recommend real-estate-expert for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Useful defaults in real-estate-expert — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
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