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Support Responder

Generates multi-channel customer support responses, resolution workflows, and support analytics frameworks.

2|Updated May 21, 2026
Search Tags:#knowledge base#multi-channel#customer support#escalation#CSAT#helpdesk#ticket resolution
One-click install
npx skills add https://github.com/tcvdog/agency-agents-hermes --skill support-responder-tcvdog
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: Support Responder
Source: https://github.com/tcvdog/agency-agents-hermes/tree/main/support/support-responder
Command: npx skills add https://github.com/tcvdog/agency-agents-hermes --skill support-responder-tcvdog

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, numpy, matplotlib.

What problem does it solve? Handling customer inquiries across email, chat, phone, and social media consistently is difficult, and teams often lack structured processes for issue resolution, escalation, and satisfaction measurement. ## Core Features & Use Cases - Omnichannel Support Framework: Defines response time SLAs, priority routing, and tiered escalation paths for email, live chat, phone, social media, and in-app messaging. - Support Analytics: Provides Python classes for calculating first response time, first-contact resolution rate, CSAT scores, and generating improvement recommendations. - Knowledge Base Management: Includes templates and logic for creating, optimizing, and organizing self-service support articles based on usage analytics. - Use Case: A support lead needs to reduce ticket backlog and improve CSAT. Use this Skill to analyze ticket data, identify top issue categories, and generate a proactive outreach list for at-risk customers. ## Quick Start Ask the agent to draft a response and resolution plan for a frustrated enterprise customer reporting a recurring billing error.

Frequently Asked Questions about Support Responder

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

FAQPage Schema
How do I structure a multi-channel customer support workflow?▼

Define per-channel SLAs for response and resolution times, then set up tiered routing where tier 1 handles general inquiries, tier 2 handles technical issues, and tier 3 manages enterprise or security cases. The Skill provides a YAML configuration covering email, chat, phone, social, and in-app channels.

How to measure customer support performance with Python?▼

Use the included SupportAnalytics class with pandas to calculate average first response time, resolution time, first-contact resolution rate, and CSAT scores from ticket data. It also groups metrics by channel, priority, and agent for performance comparison.

What is a good first contact resolution rate for support teams?▼

The Skill targets 80% or higher first-contact resolution while maintaining quality standards. If your rate falls below 80%, it recommends expanding agent training and improving knowledge base accessibility to close the gap.

Can this support framework handle escalation to engineering teams?▼

Yes, the tiered model defines explicit escalation criteria, routing technical complexity from tier 1 to tier 2, and engineering-required, security, or data recovery issues to tier 3 specialists. Escalation thresholds are configurable per channel.

Why do customers keep submitting tickets after reading knowledge base articles?▼

High related ticket volume usually means the article does not fully solve the problem. The KnowledgeBaseManager flags articles with more than 20 related tickets and recommends reviewing and expanding the content based on feedback themes.

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