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2 contributions to Atlassian Everything
Getting granular with Issue/Work Item history
We have a specific use case that is giving us fits. Here's the gist: - jira query with status transition and assignment history (assuming it's possible) of CRs - analyze with codex to create a report which would contain, at a minimum Backlog Metrics - Total "Active": the total number of CRs not in status "done", "rejected", or "in prod". - Untouched: the number of CRs which have no comments and no changes in status - Wait and Rejection Metrics (90 Day Rolling) - 90 Day Average Wait Time: the average amount of time a CR currently in status "development backlog" or beyond spent in "waiting for customer" status over the last 90 days - 90 Day Rolling Rejections: the total number of CRs moved to status "rejected" over the last 90 days - 90 Day Rejection Reasons: of those CRs rejected over the last 90 days, how many of each "Rejection Reason" occurred - Goals - Mean time to resolution (MTTR): across all CRs which are currently in status "development backlog" or "sprint backlog" or farther along in the workflow, what is the average amount of time they took to go from status "delivery backlog" to "done" In the past I looked a couple of marketplace apps but they didn't really fit our use case with specificity. So my big question is: can Rovo or a Rovo agent pull this data that I can then feed into Codex/Claude Code/Gemini for analysis. I'm thinking if we could just get the raw history of the work item the AI could do the rest.
1 like • Jun 10
Hey, every metric you've listed is native eazyBI — active CRs, untouched issues, 90-day rolling waits, rejection-reason breakdown, MTTR end-to-end. The bit most people miss: you have to enable Import issue change history and Import comments on the data source. Without those flipped on, none of the time-in-status or activity measures work — which is probably why the marketplace apps felt limited. Letting an LLM do the calculations themselves gives you non-deterministic numbers (same prompt, different totals), no audit trail when someone asks how MTTR is defined, and a token bill that scales with your data. Where AI genuinely earns its keep is on top of eazyBI — clustering messy free-text rejection reasons into themes, generating narrative summaries, anomaly detection on a dashboard. The architecture that works: Jira → eazyBI for the numbers → AI for the story. Happy to share measure definitions if useful.
JSM Implementation - Anyone Looking for Work?
Just talked to a company that wasn't the right fit for us right now. They are looking to implement JSM with some AI in the next few weeks. If anyone is available and interested in working on this, send me a dm!
1 like • May 18
Yes, just done full implementation for a client - Virtual chat plus What's up integration
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Michael K.
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@michalek-kj
I'm Atlassian SEM, and I am helping Scrum Masters, PMOs, BAs, or Managers to Master Jira, Confluence, or Service Desk Fast https://projectflow.co.uk/

Active 5d ago
Joined Apr 1, 2026
London UK
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