spend-analysis
When the user wants to analyze procurement spend, identify savings opportunities, or classify expenditures. Also use when the user mentions "spend analytics," "spend visibility," "spend cube," "category analysis," "Pareto analysis," "tail spend," "maverick spend," "spend classification," or "savings opportunity." For executing sourcing strategies, see strategic-sourcing. For supplier evaluation, see supplier-selection.
npx skills add majiayu000/claude-skill-registry --skill spend-analysis-kishorkukreja-awesome-supply-chain --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
What it does
Analyzes procurement spend to understand spending patterns, identify savings opportunities, improve compliance, and enable data-driven decisions through spend visibility and classification.
How it works
- Guides the user through an initial assessment of objectives, data availability, current state, and scope.
- Establishes a Spend Cube framework with three dimensions (What, Who, Where) and various analysis types (slice by category, supplier, business unit; drill-down; roll-up).
- Collects and prepares data from primary and secondary sources, including an embedded Python extraction routine to consolidate sources, standardize columns, and add a data_source tag.
- Cleans data by removing duplicates, normalizing supplier names, handling common name variations, ensuring spend_amount is numeric, removing negatives, and extracting year/month/quarter from transaction_date.
- Provides data enrichment to classify suppliers and categories, normalize supplier names, and assign a spend_tier based on spend_amount.
- Defines a Category Taxonomy for spend classification and includes an automated classifier (SpendClassifier) using TF-IDF and MultinomialNB to train on labeled data and predict categories for new spend descriptions, with a method to classify an entire spend dataset and flag low-confidence results.
- Presents Spend Analysis Techniques including Pareto Analysis to identify top contributors with A/B/C classification, plus a plotting helper for Pareto charts; Supplier Consolidation Analysis to find consolidation opportunities by category; and Maverick Spend Detection to flag off-contract purchases against preferred suppliers and contracts.
- Includes example usage blocks demonstrating training of the classifier and classification of new spend data.
When to use it
Use when the user mentions spend analytics, spend visibility, spend cube, category analysis, Pareto analysis, tail spend, maverick spend, spend classification, or savings opportunity, to perform spend analysis and identify savings opportunities.
What it can touch
- Uses tool: claude-code
- Includes code blocks in Python for data extraction, cleaning, enrichment, classification, and spend analysis techniques.
Caveats
- License: MIT.
- Requires labeled training data for the automated classification model; performance depends on data quality and labeling.
- Some code blocks may assume specific data shapes (e.g., column names like supplier_name, spend_amount, transaction_date, description).
- Some sections show placeholder usage examples (commented out) that require real data to run.
# Spend Analysis You are an expert in procurement spend analysis and analytics. Your goal is to help organizations understand their spending patterns, identify savings opportunities, improve compliance, and enable data-driven procurement decisions through comprehensive spend visibility and analysis. ## Initial Assessment Before conducting spend analysis, understand: 1. **Analysis Objectives** - What's the primary goal? (cost savings, compliance, consolidation) - Key questions to answer? - Stakeholders and their needs? - Expected outcomes? 2. **Data Availability** - Data sources? (ERP, P2P system, AP, credit cards) - Data quality issues? - Time period covered? - Level of detail available? 3. **Current State** - Existing spend visibility? - Known issues or opportunities? - Previous analysis efforts? - Category management maturity? 4. **Scope & Resources** - Total addressable spend? - Categories to prioritize? - Tools and systems available? - Timeline for analysis? --- ## Spend Analysis Framework ### The Spend Cube Model **Three Dimensions:** 1. **What** - Categories, commodities, items 2. **Who** - Suppliers, vendors, merchants 3. **Where** - Business units, locations, cost centers *
- Initial Assessment
- Spend Analysis Framework
- The Spend Cube Model
- Data Collection & Preparation
- Data Sources
- Data Extraction
- Data Enrichment
- Spend Classification
- Category Taxonomy
- Automated Classification
- Spend Analysis Techniques
- Pareto Analysis (80/20 Rule)
- Supplier Consolidation Analysis
- Maverick Spend Detection
What does the spend-analysis skill do?
When the user wants to analyze procurement spend, identify savings opportunities, or classify expenditures. Also use when the user mentions "spend analytics," "spend visibility," "spend cube," "category analysis," "Pareto analysis," "tail spend," "maverick spend," "spend classification," or "savings opportunity." For executing sourcing strategies, see strategic-sourcing. For supplier evaluation, see supplier-selection.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill spend-analysis-kishorkukreja-awesome-supply-chain --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.