AgentSkillsCN

Evaluate Site Selection

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SKILL.md

Skill: Evaluate Site Selection

Domain

real_estate

Description

Evaluates potential facility sites analyzing demographics, accessibility, competition, and costs for retail and industrial location decisions.

Tags

real-estate, site-selection, retail, demographics, location, GIS

Use Cases

  • •Retail site selection
  • •Warehouse location analysis
  • •Market potential assessment
  • •Competitive analysis

Proprietary Business Rules

Rule 1: Trade Area Analysis

Customer catchment area delineation and analysis.

Rule 2: Demographic Scoring

Population and income criteria evaluation.

Rule 3: Competitive Assessment

Existing competition density and impact.

Rule 4: Accessibility Rating

Traffic, visibility, and access evaluation.

Input Parameters

  • •site_id (string): Site identifier
  • •site_details (dict): Site characteristics
  • •demographic_data (dict): Area demographics
  • •competitive_data (list): Competitor locations
  • •cost_factors (dict): Site costs
  • •requirements (dict): Site requirements

Output

  • •site_score (float): Overall site rating
  • •demographic_analysis (dict): Population analysis
  • •competitive_analysis (dict): Competition assessment
  • •accessibility_score (dict): Access evaluation
  • •financial_projection (dict): Sales/rent potential
  • •recommendation (string): Site recommendation

Implementation

The evaluation logic is implemented in site_evaluator.py and references data from CSV files:

  • •evaluation_criteria.csv - Reference data
  • •cost_benchmarks.csv - Reference data
  • •infrastructure_scores.csv - Reference data
  • •incentive_types.csv - Reference data
  • •risk_factors.csv - Reference data
  • •facility_requirements.csv - Reference data
  • •parameters.csv - Reference data.

Usage Example

python
from site_evaluator import evaluate_site

result = evaluate_site(
    site_id="SITE-001",
    site_details={"sqft": 5000, "type": "retail", "visibility": "high"},
    demographic_data={"population_3mi": 150000, "median_income": 75000},
    competitive_data=[{"name": "Competitor A", "distance_mi": 1.5}],
    cost_factors={"rent_sqft": 35, "cam_sqft": 8},
    requirements={"min_population": 100000, "max_competition": 3}
)

print(f"Site Score: {result['site_score']}")

Test Execution

python
from site_evaluator import evaluate_site

result = evaluate_site(
    site_id=input_data.get('site_id'),
    site_details=input_data.get('site_details', {}),
    demographic_data=input_data.get('demographic_data', {}),
    competitive_data=input_data.get('competitive_data', []),
    cost_factors=input_data.get('cost_factors', {}),
    requirements=input_data.get('requirements', {})
)