Babysitter spend-analytics-engine
Procurement spend analysis skill with classification, consolidation, and savings identification
install
source · Clone the upstream repo
git clone https://github.com/a5c-ai/babysitter
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/a5c-ai/babysitter "$T" && mkdir -p ~/.claude/skills && cp -r "$T/library/specializations/domains/business/supply-chain/skills/spend-analytics-engine" ~/.claude/skills/a5c-ai-babysitter-spend-analytics-engine && rm -rf "$T"
manifest:
library/specializations/domains/business/supply-chain/skills/spend-analytics-engine/SKILL.mdsource content
Spend Analytics Engine
Overview
The Spend Analytics Engine provides comprehensive procurement spend analysis capabilities. It cleanses and classifies spend data, identifies consolidation opportunities, detects maverick spending, and quantifies savings opportunities to drive procurement value.
Capabilities
- Spend Data Cleansing and Normalization: Data quality improvement
- UNSPSC/Commodity Classification: Standard category assignment
- Supplier Consolidation Analysis: Fragmentation identification
- Price Variance Identification: Unit price analysis across transactions
- Maverick Spend Detection: Off-contract purchasing identification
- Contract Compliance Analysis: Spend vs. contract terms
- Savings Opportunity Quantification: Addressable spend and savings potential
- Spend Trend Visualization: Historical pattern analysis
Input Schema
spend_analysis_request: spend_data: transactions: array - supplier: string description: string amount: float quantity: float date: date business_unit: string cost_center: string period: start_date: date end_date: date reference_data: supplier_master: array category_taxonomy: object contracts: array analysis_scope: analysis_types: array # classification, consolidation, compliance focus_categories: array thresholds: object
Output Schema
spend_analysis_output: spend_summary: total_spend: float supplier_count: integer transaction_count: integer by_category: object by_supplier: object by_business_unit: object classification_results: classified_spend: float unclassified_spend: float category_distribution: object consolidation_opportunities: fragmented_categories: array supplier_rationalization: array estimated_savings: float price_variance_analysis: variance_by_item: array outliers: array benchmark_comparisons: object maverick_spend: off_contract_spend: float percentage: float top_violations: array contract_compliance: compliant_spend: float non_compliant_spend: float compliance_issues: array savings_opportunities: total_addressable_spend: float estimated_savings: float initiatives: array - initiative: string category: string addressable_spend: float savings_potential: float confidence: string visualizations: object
Usage
Comprehensive Spend Analysis
Input: 12 months AP transaction data Process: Cleanse, classify, analyze patterns Output: Complete spend analysis with savings opportunities
Supplier Consolidation Analysis
Input: Classified spend by category Process: Identify fragmentation, model consolidation Output: Consolidation recommendations with savings
Contract Compliance Review
Input: Spend data, contract terms Process: Match spend to contracts, identify leakage Output: Compliance report with violation details
Integration Points
- Spend Analytics Platforms: Coupa, SAP Ariba, Jaggaer
- ERP Systems: AP data extraction
- Classification Services: Automated categorization
- Tools/Libraries: Spend analytics, classification algorithms
Process Dependencies
- Spend Analysis and Savings Identification
- Category Management
- Strategic Sourcing Initiative
Best Practices
- Establish regular data refresh cadence
- Maintain category taxonomy consistency
- Validate classification accuracy periodically
- Focus on actionable savings opportunities
- Track savings realization against projections
- Communicate insights to stakeholders regularly