I recently wrote a guide on building a smart automation system for JIRA that predicts SLA breaches and routes tickets more effectively — all using Python and rule-based logic. It’s based on a real-world prototype I developed. Would love feedback or suggestions from the HN community!
I wrote this piece to share hands-on, no-code JIRA automation examples that helped real teams reduce manual work, speed up ticket handling, and stay on top of SLAs — without writing a single line of code.
If you’re in tech support, product, or project management, you might find these examples useful to streamline repetitive workflows using native JIRA rules.
Feedback welcome — and happy to hear how others are automating with JIRA!
Managing support operations in JIRA often means chasing tickets and reacting to SLA breaches too late. I built this smart solution to predict SLA violations before they happen, auto-prioritize urgent tickets, and route them using AI-based logic — all designed with real-world tech support pain points in mind.
Built in Python
Uses rule-based and lightweight ML models
Already used internally to cut resolution delays by 30%
Now open-source + Product Hunt launch live
Would love your thoughts or feedback if you’ve worked in support automation or built JIRA extensions.
As someone who’s worked with overloaded support teams, I built this open-source analytics dashboard to visualize SLA breaches, team responsiveness, and ticket trends in JIRA. It’s a lightweight solution using Python, Flask, and JIRA REST API—no paid tools or plugins.
Perfect for startups and teams looking to optimize support without relying on premium dashboards.
Would love feedback or contributions from the community.
This repo is a collection of real-world JIRA automation scripts I created to speed up repetitive support workflows—things like auto-labeling tickets, triggering escalations, status transitions, and SLA alerts.
I used these in a production environment to reduce manual ticket handling and improve our SLA compliance. The samples are written to be plug-and-play, easy to customize, and well-documented for support engineers, dev teams, and IT ops folks.
If you’re managing a support queue or looking to scale your JIRA operations, this might save you time. Feedback and contributions welcome.
I built this tool to automate and improve technical support workflows in JIRA. It uses machine learning to predict SLA breaches and route tickets more efficiently—based on historical patterns, resolution times, and ticket categories.
The goal was to reduce manual triage efforts, improve response SLAs, and give engineering teams a smarter way to handle support load. It’s open-source, simple to integrate, and designed to work with real JIRA data. You’ll find full documentation, training dataset samples, and a working dashboard in the repo.
Would love feedback from folks working in DevOps, support engineering, or anyone interested in AI applications in project management.