real-estate-expert

Expert guidance for real estate systems, property management, Multiple Listing Service (MLS) integration, customer relationship management, virtual tours, and market analysis.

personamanagmentlayer/pcl|Updated Apr 8, 2026

Works with

Claude CodeCursorClineWindsurfCodex

Installation Guide

Select your AI agent

How to use real-estate-expert on Cursor

AI-first code editor with Composer

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

›Cursor installed and configured on your machine
›Node.js 16+ with npm — verify with node --version
›Active project directory where you want to add real-estate-expert

Run the install command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/personamanagmentlayer/pcl --skill real-estate-expert

Fetches real-estate-expert from personamanagmentlayer/pcl and configures it for Cursor.

Select Cursor when prompted

The CLI shows a list of agents. Use arrow keys and space to select Cursor:

◆ Which agents do you want to install to?

│

│ ── Universal (.agents/skills) ────────────────

│ · Cline · Codex · Goose · Windsurf

│ ●Cursor(selected)

│ · Cursor · Aider · Continue

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/real-estate-expert

Restart Cursor to activate real-estate-expert. Access via /real-estate-expert in your agent's command palette.

⚠

Security Notice

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.

›View source on GitHub ›Skills CLI docs ›About Cursor ›What are agent skills?

Documentation

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Use Cases

User Story & Requirements Generation

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

Competitive Analysis

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

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Core Concepts

Real Estate Systems

Multiple Listing Service (MLS) integration

Property Management Systems (PMS)

Customer Relationship Management (CRM)

Transaction management

Document management

Lease management

Maintenance tracking

PropTech Solutions

Virtual tours and 3D walkthroughs

AI-powered property valuation

Digital signatures and e-closing

Smart home integration

IoT sensors for properties

Blockchain for title management

Augmented reality for staging

Standards and Regulations

RESO (Real Estate Standards Organization)

Fair Housing Act compliance

RESPA (Real Estate Settlement Procedures Act)

Data privacy (GDPR, CCPA)

ADA compliance for websites

NAR Code of Ethics

Property Listing System

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 (w

real-estate-expert

Installation Guide

How to use real-estate-expert on Cursor

Prerequisites

Run the install command

Select Cursor when prompted

Verify installation

Security Notice

Documentation

List & Monetize Your Skill

Use Cases

User Story & Requirements Generation

Competitive Analysis

Roadmap Prioritization

Install Skill

Real Estate Expert

Core Concepts

Real Estate Systems

PropTech Solutions

Standards and Regulations

Property Listing System

`Stakeholder Communication`

`Implementation Guide`

`Best Practices`

`When to Use This`

`Learning Path`

`Related Skills`

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`Reviews`

`Discussion`