AgentSkillsCN

Analyze Pricing Anomaly

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SKILL.md

Skill: Analyze Pricing Anomaly

Domain

retail

Description

Analyzes pricing data to detect anomalies, competitive pricing gaps, and margin optimization opportunities using statistical methods.

Tags

pricing, analytics, retail, anomaly-detection, margin, competitive

Use Cases

  • •Price error detection
  • •Competitive price monitoring
  • •Margin analysis
  • •Promotional effectiveness

Proprietary Business Rules

Rule 1: Statistical Anomaly Detection

Z-score and IQR-based outlier identification.

Rule 2: Competitive Gap Analysis

Price positioning versus competition.

Rule 3: Margin Threshold Alerts

Margin below acceptable thresholds.

Rule 4: Price Elasticity Impact

Estimated volume impact of pricing changes.

Input Parameters

  • •analysis_id (string): Analysis identifier
  • •pricing_data (list): Product pricing records
  • •historical_prices (list): Historical price data
  • •competitive_prices (dict): Competitor pricing
  • •cost_data (dict): Product costs
  • •sales_data (list): Sales volume data

Output

  • •anomalies_detected (list): Pricing anomalies
  • •competitive_analysis (dict): Competitive positioning
  • •margin_alerts (list): Margin concerns
  • •optimization_opportunities (list): Pricing recommendations
  • •summary_statistics (dict): Analysis summary

Implementation

The analysis logic is implemented in pricing_analyzer.py and references data from CSV files:

  • •anomaly_detection_thresholds.csv - Reference data
  • •statistical_parameters.csv - Reference data
  • •anomaly_categories.csv - Reference data
  • •product_category_rules.csv - Reference data
  • •customer_segment_pricing.csv - Reference data
  • •approval_thresholds.csv - Reference data
  • •time_based_patterns.csv - Reference data
  • •parameters.csv - Reference data.

Usage Example

python
from pricing_analyzer import analyze_pricing

result = analyze_pricing(
    analysis_id="PRC-001",
    pricing_data=[{"sku": "SKU-001", "price": 29.99, "list_price": 34.99}],
    historical_prices=[{"sku": "SKU-001", "date": "2025-11-01", "price": 24.99}],
    competitive_prices={"SKU-001": {"competitor_a": 27.99, "competitor_b": 31.99}},
    cost_data={"SKU-001": {"unit_cost": 15.00}},
    sales_data=[{"sku": "SKU-001", "units": 500, "period": "2025-12"}]
)

print(f"Anomalies Found: {len(result['anomalies_detected'])}")

Test Execution

python
from pricing_analyzer import analyze_pricing

result = analyze_pricing(
    analysis_id=input_data.get('analysis_id'),
    pricing_data=input_data.get('pricing_data', []),
    historical_prices=input_data.get('historical_prices', []),
    competitive_prices=input_data.get('competitive_prices', {}),
    cost_data=input_data.get('cost_data', {}),
    sales_data=input_data.get('sales_data', [])
)