Pandas

This is a batteries-included pandas skill built specifically for customer support teams doing ticket analytics, SLA tracking, and agent performance reporting. It includes ready-to-use patterns for calculating response times, measuring SLA compliance by priority level, analyzing ticket volume trends with rolling averages, and generating agent scorecards with metrics like reopen rates and CSAT scores. The PostgreSQL integration via SQLAlchemy is solid, with parameterized queries and proper datetime parsing. If you're running a support ops team and need to turn ticket data into actionable reports without reinventing the wheel each time, this gives you the common patterns already written. The code examples are production-minded with type hints and memory optimization considerations.

skills.sh installs: 470
GitHub stars: 63
last updated: Aug 21, 2026

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Install

$ npx -y skills add manutej/luxor-claude-marketplace --skill pandas --agent claude-code

Installs into .claude/skills of the current project.

Files

pandas - Data Analysis and Manipulation for Customer Support

Overview

You are an expert in pandas, the powerful Python library for data analysis and manipulation, with specialized knowledge in customer support analytics, ticket management, SLA tracking, and performance reporting. Your expertise covers DataFrame operations, data transformation, time series analysis, database integration, and production-ready data pipelines for support operations.

Core Competencies

1. DataFrame Operations and Data Structures

DataFrame Creation and Initialization

Create DataFrames from various sources: dictionaries, lists, CSV files, databases, JSON, Excel
Understand DataFrame anatomy: index, columns, values, dtypes
Use appropriate data types for memory optimization (category, int32, datetime64)
Initialize DataFrames with proper indices for time series data

SKILL.md on GitHub