agency-support-responder

Orchestrate multi-channel customer support with performance analytics and knowledge base optimization.

Updated Jul 23, 2026

Search Tags:#analytics #knowledge management #customer support #omnichannel #customer success #sla

Ready to run

npx skills add https://github.com/rajyeole6/AI-RECRUITER --skill agency-support-responder-rajyeole6

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: agency-support-responder
Source: https://github.com/rajyeole6/AI-RECRUITER/tree/main/.agents/skills/support-support-responder
Command: npx skills add https://github.com/rajyeole6/AI-RECRUITER --skill agency-support-responder-rajyeole6

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, numpy, matplotlib.

What problem does it solve?

This skill addresses the challenge of maintaining high-quality, consistent, and timely customer support across multiple channels while managing complex issue resolution and team performance.

Core Features & Use Cases

Omnichannel Support Framework: Standardizes response times and routing logic for email, chat, phone, and social media.
Performance Analytics: Automatically calculates CSAT, resolution times, and identifies support trends to optimize team efficiency.
Knowledge Management: Provides a structured system for creating, optimizing, and troubleshooting knowledge base articles to reduce ticket volume.
Use Case: A support lead can use this agent to analyze the last month of ticket data, identify high-volume issue categories, and generate a prioritized list of knowledge base articles to create, effectively reducing future support load.

Quick Start

Use the agency-support-responder skill to analyze the provided support data and generate a report on current response time trends and improvement recommendations.

Frequently Asked Questions about agency-support-responder

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I automate omnichannel customer support and track SLA compliance?▼

Automate omnichannel customer support by standardizing routing logic and response times across email, chat, and phone. This facilitates end-to-end issue resolution while calculating performance metrics to ensure strict SLA compliance and scalable customer success operations.

What is the best way to analyze support ticket data and identify high-volume issue categories?▼

Analyze support ticket data by processing historical records with pandas and numpy to calculate CSAT and resolution times. This identifies high-volume support trends, enabling you to generate a prioritized list of knowledge base articles to effectively reduce future ticket volume.

Can I use this skill to optimize knowledge base articles and reduce support load?▼

Yes, you can optimize knowledge base articles by leveraging the structured knowledge management system. It troubleshoots existing articles and generates targeted creation lists based on analytics, directly reducing support load and minimizing repetitive ticket volume across channels.

How does customer support performance analytics work for monitoring team efficiency?▼

Customer support performance analytics works by automatically calculating CSAT scores and resolution times from ticket data. It identifies support trends and monitors team efficiency, providing data-driven service improvements for proactive customer outreach and operational scaling.

Do I need pandas and numpy to run customer support data analytics and reporting?▼

Yes, you need pandas and numpy installed to run customer support data analytics. These dependencies process ticket data, calculate performance metrics, and generate analytical reports on response time trends and improvement recommendations using matplotlib visualizations.