Explain AWS Cost and Usage Report file formats and column structures.
npx skills add https://github.com/T-Klug/aws-cur-report-generator --skill cur-data
Please help me install this Agent Skill. Skill: cur-data Source: https://github.com/T-Klug/aws-cur-report-generator/tree/main/.claude/skills/cur-data Command: npx skills add https://github.com/T-Klug/aws-cur-report-generator --skill cur-data
This Skill provides essential knowledge about the structure, column formats, and common analysis patterns for AWS Cost and Usage Reports (CUR), enabling users to effectively interpret and utilize their AWS billing data.
unblended_cost, blended_cost, and net_unblended_cost.line_item_type values.Explain the different AWS CUR file formats supported by the tool.
High-intent search queries and answers about installing and using this skill.
The AWS Cost and Usage Report (CUR) data structure contains billing data in CSV, CSV.GZ, or Parquet formats, featuring canonical column names that map to older and newer variants for tracking costs and usage metrics.
To identify discounts and Savings Plans in AWS CUR billing data, you analyze specific line item types and key cost metrics like net_unblended_cost to evaluate Savings Plan effectiveness and applied discount patterns.
Aggregating AWS cost data by service, account, date, and month requires grouping specific CUR columns, utilizing canonical cost metrics like unblended_cost or blended_cost to summarize total billing expenditures accurately.
AWS CUR supports Parquet, CSV, and CSV.GZ file formats for cost analysis reporting, enabling efficient querying and columnar data processing for large-scale billing datasets.
Detecting spending anomalies in AWS CUR data using z-scores involves calculating standard deviations from your historical cost metrics to identify unusual variations and outliers in your AWS billing patterns.
CUR column names differ between older and newer AWS billing reports due to format updates, requiring column name normalization to map canonical names correctly when analyzing historical cost data structures.
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