I want to build a Jira plugin that, during workflow transitions, shows a warning allowing users to decide whether to continue or cancel instead of outright rejecting the transition.
hey, try using a js trigger along your workflow condition to make a custom warnin moal. its not native but might work as a workaround. good luck and keep us posted!
In my experience, building a Jira plugin for intercepting workflow transitions requires careful manipulation of JavaScript and Jira event listeners. I implemented a popup dialog by listening for transition events and halted the process until the user confirmed their intention. Although this solution was not officially supported by Jira, rigorous testing ensured that workflow data remained consistent. The process required careful orchestration to avoid race conditions between the dialog response and workflow execution.
In my experience developing similar plugins for Jira, a viable solution was to integrate a custom JavaScript module that hooks into the workflow transition events. Rather than relying solely on Jira’s backend, carefully injecting scripts to present a warning dialog provided more flexible control over user actions during transitions. I remember dealing with synchronization issues since dialogues could cause unexpected workflow delays, but thorough testing on various project types helped resolve these issues. Using this technique, I managed to provide a user-friendly warning mechanism without compromising data integrity in the workflow.
hey, i once built a custom plugin using js intercepts to deploy a warnin msg just before the transition. it’s a bit hacky but works well if you sync the front and backend correctly. test in your enviroment to dodge any race conditons, ya know?
In my experience, another practical solution was to incorporate a preliminary REST API call within the workflow transition process that would trigger a modal dialog on the client side. This technique allowed the server to initiate the warning while the transition was suspended until the user provided feedback. Although it required extra attention to asynchronous communication issues and some retry mechanisms to handle slow responses, thorough testing in different environments ensured that it didn’t compromise data consistency or overall system performance.