Primary Use Case: CR5 Index Calculation

Core Mapping Logic

• Use the Bundled Mapping: Always use the canonical country_to_region_mapping.json file for the most accurate and up-to-date World Bank classifications. It handles common naming variations found in datasets.
• Region Focus: The seven major geographical regions are the primary focus. Income group classifications (LIC, LMC, UMC, HIC) are available in the mapping but are secondary.
• Handling Unmapped Entities: If a country/entity name from the source data is not found in the mapping, log it for review. Do not guess its classification.

Key Considerations

• Data Integrity: Verify that the sum of individual country GDPs within a region approximates the official regional total from the World Bank source. Discrepancies may occur due to data vintage or methodology.
• Flexible N: The "N" in CRN (e.g., CR5) is a parameter. The skill should adapt if a region has fewer than N members (e.g., North America has only 3 countries, so CR5 becomes CR3 for that region).
• Output Formatting: Present monetary values in a readable format (e.g., with thousand separators). Express ratios as percentages.

When to Use Scripts vs. Reasoning

• Use scripts/classify_and_calculate.py (Low Freedom): For the deterministic, error-prone tasks of loading data, applying the mapping, performing grouping, sorting, and arithmetic calculations. This ensures consistency and accuracy.
• Use Your Judgment (High Freedom): For interpreting user intent, identifying the correct source sheets in a workbook, designing the structure and naming of the output spreadsheet, and communicating findings or data discrepancies to the user.

Quick Start Command

For a standard CR5 analysis from Google Sheets, the expected user command pattern is: "Read the country GDP data from [Source Spreadsheet], calculate the CR5 index for each World Bank region, and save the results to a new spreadsheet named [Output Name] with a table titled [Table Name]."