StacksGPT
Customer Support

Track Sentiment on Active Support Issues

Analyze the mood of comments on active Linear issues, track changes in Airtable and alert Slack when an issue turns negative.

The Problem

Support leads notice a frustrated customer only after the thread has gone badly. Nobody has time to reread every active issue.

How This Template Solves It

A support team keeps customer issues in Linear. On a schedule the workflow reads recently updated issues, rates the sentiment of the conversation, stores current and previous sentiment in Airtable, and posts to Slack when an issue moves from non-negative to negative.

How It Works

  1. 1Queries Linear's GraphQL API on a schedule for recently updated active issues.
  2. 2An AI information extractor rates the sentiment of each issue's comments.
  3. 3Creates or updates a row in Airtable, moving the old value into a previous sentiment column.
  4. 4An Airtable trigger watches for changes and sends a Slack alert when an issue turns negative.

What You Need

  • A Linear workspace with API access
  • An Airtable base for tracking sentiment
  • An OpenAI API key
  • A Slack workspace

What Stays Under Your Control

  • A person reviews flagged issues and decides how to step in.

Watch Out For

  • Sentiment is a signal, not a verdict. Short technical comments can read as negative.

Step-by-Step Blueprint

This template is the starting point for a full guide with setup steps and prompts:

Adapted from a community workflow template. Before publishing we removed credentials, account and file IDs, email addresses and sample data. The original author's setup notes are kept inside the workflow file.

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