retell

Analyze Retell voice agent data to extract call metrics and summaries.

1|Updated Dec 23, 2025
One-click install
npx skills add https://github.com/gracebotly/flowetic-app --skill retell
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: retell
Source: https://github.com/gracebotly/flowetic-app/tree/main/workspace/skills/retell
Command: npx skills add https://github.com/gracebotly/flowetic-app --skill retell

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill helps teams quantify and improve the performance of voice agents by surfacing actionable analytics from Retell conversations, including call outcomes and interaction quality.

Core Features & Use Cases

  • Conversation analytics: track duration, sentiment, intents, and outcomes across calls.
  • Agent performance insights: identify training gaps and high-performing patterns.
  • Use Case: QA and product teams use these metrics to optimize retention and resolution rates.

Quick Start

Provide a concise analytics report for the latest Retell conversations, including duration, sentiment, intents, and completion rate.

Frequently Asked Questions about retell

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

FAQPage Schema
How do I extract call metrics like duration and sentiment from Retell voice agent data?

You can extract Retell call metrics by processing voice agent conversation logs to analyze call duration, sentiment, intents, and outcomes. This generates structured analytics summaries for QA dashboards and agent performance tracking.

What are voice analytics and how do they help customer support centers?

Voice analytics process conversational data to track call duration, sentiment, and intents. For customer support centers, they identify training gaps and high-performing patterns to optimize retention and resolution rates.

Can I generate QA dashboards from Retell call summaries and completion rates?

Yes, you can generate QA dashboards from Retell call summaries by extracting structured metrics like completion rates, outcomes, and agent performance. This enables QA teams to monitor and improve interaction quality.

Does intent extraction work for diverse voice conversations and edge cases?

Intent extraction handles diverse conversations and edge cases by processing varying interaction patterns within the voice agent data. It accurately maps conversation intents to structured outcomes regardless of call complexity.

What is the best way to analyze voice agent performance for sales lines?

The best way to analyze voice agent performance for sales lines is to summarize call outcomes and sentiment across sessions. This surfaces actionable analytics revealing high-performing patterns and training gaps.