real-estate-expert

personamanagmentlayer/pcl · updated Apr 8, 2026

MDX-style export adds YAML metadata + attribution linking explainx.ai and this canonical listing URL.

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

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

skill.md

Real Estate Expert

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

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    WITHDRAWN = "withdrawn"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
how to use real-estate-expert

How to use real-estate-expert on Cursor

AI-first code editor with Composer

1

Prerequisites

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

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add real-estate-expert
2

Execute installation 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

The skills CLI fetches real-estate-expert from GitHub repository personamanagmentlayer/pcl and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/real-estate-expert

Reload or restart Cursor to activate real-estate-expert. Access the skill through slash commands (e.g., /real-estate-expert) or your agent's skill management interface.

Security & Verification 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 development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

List & Monetize Your Skill

Submit your Claude Code skill and start earning

GET_STARTED →

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

Make data-driven prioritization decisions faster

Stakeholder Communication

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

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ 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.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.640 reviews
  • Nikhil Taylor· Dec 20, 2024

    real-estate-expert is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Dev Kapoor· Dec 4, 2024

    real-estate-expert reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • James Robinson· Nov 11, 2024

    real-estate-expert fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Omar Abbas· Oct 2, 2024

    We added real-estate-expert from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Chinedu Chen· Sep 21, 2024

    Solid pick for teams standardizing on skills: real-estate-expert is focused, and the summary matches what you get after install.

  • Oshnikdeep· Sep 13, 2024

    I recommend real-estate-expert for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Omar Smith· Sep 5, 2024

    Useful defaults in real-estate-expert — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Isabella Flores· Sep 1, 2024

    I recommend real-estate-expert for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • James Martinez· Aug 24, 2024

    I recommend real-estate-expert for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Hana Robinson· Aug 20, 2024

    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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