Analyzes and manipulates customer support data using pandas to track SLAs, ticket volumes, and agent performance metrics.
This skill equips Claude with specialized expertise in pandas for deep-dive customer support analytics, covering everything from ticket management to SLA tracking. It provides optimized patterns for DataFrame operations, time series analysis, and multi-level aggregations tailored specifically for support operations. Whether you need to build production-ready ETL pipelines from PostgreSQL or generate detailed agent performance reports, this skill ensures data accuracy and performance through proven implementation patterns and automated cleaning routines.
主要功能
0117 GitHub stars
02Comprehensive agent performance and CSAT metric calculation
03Advanced data cleaning and validation for support datasets
04Time series resampling for ticket volume trend reporting
05SLA compliance and response time analysis patterns
06PostgreSQL database integration via SQLAlchemy and ETL logic
使用场景
01Calculating and reporting on weekly SLA compliance rates across support teams
02Analyzing customer satisfaction trends relative to response times and ticket priority
03Building automated data pipelines to transform raw ticket logs into performance dashboards
