Dataset: Issues & Pull Requests
Entity: Pull Requests, Issues
Field ID: last_comment_at
Type: Datetime
Description: The datetime at which the last comment was made.
Source: App
Transformation logic: It is the creation datetime of the last comment
From: |
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Github (PRs, Issues) | Calculated |
Gitlab (PRs, Issues) | Calculated |
Bitbucket (PRs) | Calculated |
Azure Devops (PRs, Issues) | Calculated |
JIRA (Issues) | Calculated |
ClickUp (Issues) | Calculated |
Trello (Issues) | Calculated |
Reporting Use Cases
The Last Comment At field is a vital timestamp for gauging the current engagement level and "freshness" of a work item. It tells you the last time a discussion took place, making it a key indicator for identifying stale, forgotten, or recently active tasks.
Filtering for Stale or Active Items: This is the primary use case for this field, allowing you to effectively manage your backlog and active work.
Backlog Grooming: Create a report of all open items that have not seen any discussion for an extended period by using a filter like
Last Comment At before 90 days ago. This is a powerful way to identify and close out irrelevant or abandoned work.Daily Stand-up View: Build a widget showing all items that have had recent discussions by filtering where
Last Comment At in the previous 24 hours. This helps teams quickly catch up on the latest conversations.Find Un-commented Items: You can also find items that have never been discussed by filtering where
Last Comment At is null.
Calculating Time Since Last Engagement: For open items, you can precisely measure how long it has been since someone last commented, which is a key metric for item health.
A custom dimension in a list report can show the "Days Since Last Comment" with the formula
ROUND((NOW() - last_comment_at) / DAY()). Sorting by this column will immediately bring the most neglected items to the top.
Analyzing Workflow Patterns: You can use this field to understand the time gap between the final discussion and the completion of work.
For example, you can calculate the "Time from Last Comment to Close" with the formula
(closed_at - last_comment_at) / DAY(). A high average for this metric might suggest that items are often resolved without a final confirmation or discussion, or that they are being closed in batches long after the work was effectively done.