reply-scoring

Name: reply-scoring
Availability: InStock
Author: titta

Classify inbound email replies into 11 buckets and compute positive reply rate.

29|12|Updated Mar 30, 2026

Search Tags:#analytics #email #gmail #classification #gtm #outbound

Ready to run

npx skills add https://github.com/matteotitta/genesys-skills --skill reply-scoring

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: reply-scoring
Source: https://github.com/matteotitta/genesys-skills/tree/main/skills/primitives/outbound/strategy/reply-scoring
Command: npx skills add https://github.com/matteotitta/genesys-skills --skill reply-scoring

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill solves the ambiguity of campaign performance by automatically categorizing inbound email replies into 11 distinct buckets, allowing you to measure true interest rather than just raw volume.

Core Features & Use Cases

Automated Classification: Uses AI to sort replies into buckets like positive_interested, negative_hostile, or ooo, ensuring consistent measurement.
Metric Calculation: Computes the positive_reply_rate, providing a north-star metric for GTM performance.
Actionable Insights: Surfaces high-priority, positive replies that require immediate human follow-up, ranked by recency.

Quick Start

Invoke the reply-scoring skill to classify the latest replies from my recent outreach campaign and calculate the positive reply rate.

Frequently Asked Questions about reply-scoring

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

FAQPage Schema

How do I automatically classify email replies from outbound campaigns?▼

You can classify email replies by processing Gmail thread data or importing a CSV file, which sorts inbound responses into 11 distinct sentiment buckets to identify high-priority leads requiring follow-up.

What is a positive reply rate and how is it calculated for go-to-market campaigns?▼

Positive reply rate is a post-send GTM metric calculated by dividing the number of inbound email replies classified into positive_interested buckets by the total replies received, providing a north-star measurement for campaign performance.

Can I use Gmail thread data to measure outbound email campaign success?▼

Yes, the skill operates directly on Gmail thread data through Gmail MCP integration to aggregate campaign performance, compute the positive reply rate, and surface ranked action lists for immediate human follow-up.

Do I need a Gmail MCP integration to process inbound email replies?▼

No, while Gmail MCP integration allows direct processing of thread data, you can alternatively use CSV input containing reply content to classify responses and generate a ranked action list of high-priority leads.

What's the best way to identify high-priority leads from inbound email responses?▼

The best way is to classify inbound replies into 11 mutually-exclusive buckets and surface the positive_interested responses, generating a ranked action list ordered by recency to prioritize immediate human follow-up.

Why does my outbound campaign show high reply volume but low lead conversion?▼

High reply volume with low conversion indicates many responses are falling into non-interested categories like ooo or negative_hostile; classifying replies into 11 buckets resolves this ambiguity by measuring true interest.