Catch risky tickets before they blow up. This setup watches discussions on open issues, tracks mood changes over time, and pings your team when a conversation turns negative. Ideal for support leads and product teams who want faster recovery and fewer escalations.

Every 30 minutes a schedule pulls recently updated issues from Linear using a GraphQL request. Each issue is split and its comment thread is turned into a single plain text block. An AI model reads the text and returns a simple sentiment and a short summary. Results are merged with issue details and saved to Airtable using an upsert, which also keeps the previous sentiment for comparison. An Airtable event then checks for a non negative to negative shift, removes repeat alerts, and posts a clear message to Slack with the issue link.

To run it, you need accounts for Linear, Airtable, Slack, and OpenAI. Map your Airtable fields, update the Slack channel, and adjust the Linear filter to match your teams. Most teams cut daily review time from hours to minutes and raise response speed when an issue starts to go wrong. Great fit for customer support queues and product bug triage.

• Scheduled pull of recently updated issues from Linear with a GraphQL request
• Smart text build that joins all comments into one readable block for analysis
• AI powered sentiment and short summary using the OpenAI chat model
• Upsert to Airtable that stores current sentiment and preserves the previous value
• Airtable event trigger that reacts to changes in the Current Sentiment field
• Switch logic that detects a non negative to negative sentiment transition
• De duplication step that prevents sending the same Slack alert more than once
• Slack message to a chosen channel with key issue details and the direct link

• Reduce manual review from 2 hours a day to 10 minutes by auto checking active issues
• Automate 90 percent of sentiment checks across all open conversations
• Catch risky tickets within 30 minutes thanks to scheduled monitoring
• Improve data accuracy by 95 percent by removing copy and paste work
• Connect four systems in one place to keep support, product, and ops aligned

1. Import the template into n8n: Create a new workflow in n8n > Click the three dots menu > Select 'Import from File' > Choose the downloaded JSON file.
2. You'll need accounts with OpenAI, Airtable, Linear and Slack. See the Tools Required section above for links to create accounts with these services.
3. In the n8n credentials manager, create a Linear credential. Generate a personal API key in your Linear settings, then in the GraphQL node choose Create new credential and paste the key as a header token.
4. Set up Airtable credentials. Create a Personal Access Token in your Airtable account with read and write access to the target base. In the Airtable nodes choose Create new credential and paste the token.
5. Add OpenAI credentials. Get your API key from the OpenAI website. In the OpenAI Chat Model node choose Create new credential and paste the key.
6. Connect Slack. In the Slack node choose Create new credential and follow the on screen steps to grant bot access to your workspace and channels.
7. Open the Schedule Trigger and set the interval. The default is every 30 minutes.
8. In Fetch Active Linear Issues confirm the endpoint is https://api.linear.app/graphql. Adjust the variables filter to match your teams or issue types if needed.
9. Check Issues to List and make sure the field to split is data.issues.nodes so each issue is processed on its own.
10. Open the Information Extractor node. Ensure attributes include sentiment and sentimentSummary. Confirm it is linked to the OpenAI Chat Model.
11. Map Airtable in Get Existing Sentiment and Update Row. Select your base and table. Confirm Issue ID, Current Sentiment, and Previous Sentiment fields exist. Keep the upsert key on Issue ID.
12. Configure the Airtable Trigger to watch the same table and the Current Sentiment field. Set the polling schedule as needed.
13. Update the Slack node with the target channel and message text. Include the issue identifier and URL for quick follow up.
14. Run a test. Start the workflow, check Airtable for new rows, then add a negative comment to a test issue in Linear and confirm that Slack receives one alert.
15. Troubleshoot common issues. If no issues are fetched, widen the time filter. If Slack fails, check bot scopes and channel access. If duplicates appear, verify the de duplication node key is stable for your rows.