AgentSkillsCN

Analyze Spend Category

分析支出类别

SKILL.md

Skill: Analyze Spend Category

Domain

supply_chain

Description

Analyzes procurement spend by category to identify savings opportunities, supplier consolidation, and contract optimization.

Tags

procurement, spend, sourcing, savings, suppliers, analytics

Use Cases

  • •Spend visibility
  • •Savings identification
  • •Supplier rationalization
  • •Contract optimization

Proprietary Business Rules

Rule 1: Spend Classification

Category taxonomy mapping and classification.

Rule 2: Supplier Concentration Analysis

Supplier fragmentation and consolidation opportunities.

Rule 3: Benchmark Comparison

Price benchmarking against market rates.

Rule 4: Savings Opportunity Sizing

Potential savings quantification.

Input Parameters

  • •analysis_id (string): Analysis identifier
  • •spend_data (list): Transaction-level spend
  • •supplier_data (dict): Supplier information
  • •category_taxonomy (dict): Category hierarchy
  • •benchmark_data (dict): Market benchmarks
  • •contract_data (list): Existing contracts

Output

  • •category_summary (dict): Spend by category
  • •supplier_analysis (dict): Supplier concentration
  • •savings_opportunities (list): Identified savings
  • •consolidation_recommendations (list): Supplier rationalization
  • •benchmark_comparison (dict): Price analysis

Implementation

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

  • •category_taxonomy.csv - Reference data
  • •analysis_dimensions.csv - Reference data
  • •opportunity_levers.csv - Reference data
  • •compliance_requirements.csv - Reference data
  • •risk_indicators.csv - Reference data
  • •parameters.csv - Reference data.

Usage Example

python
from spend_analyzer import analyze_spend

result = analyze_spend(
    analysis_id="SPN-001",
    spend_data=[{"supplier": "SUP-001", "amount": 100000, "category": "IT_hardware"}],
    supplier_data={"SUP-001": {"name": "Tech Corp", "tier": 1}},
    category_taxonomy={"IT_hardware": {"parent": "IT", "level": 2}},
    benchmark_data={"IT_hardware": {"market_rate": 0.95}},
    contract_data=[{"supplier": "SUP-001", "end_date": "2026-12-31"}]
)

print(f"Total Spend Analyzed: ${sum(result['category_summary'].values()):,.0f}")

Test Execution

python
from spend_analyzer import analyze_spend

result = analyze_spend(
    analysis_id=input_data.get('analysis_id'),
    spend_data=input_data.get('spend_data', []),
    supplier_data=input_data.get('supplier_data', {}),
    category_taxonomy=input_data.get('category_taxonomy', {}),
    benchmark_data=input_data.get('benchmark_data', {}),
    contract_data=input_data.get('contract_data', [])
)