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Sprint retros based on what actually happened, not what people remember

Sprint completion data from Jira, PR review metrics from GitHub, and CI pipeline stats analyzed for patterns like review bottlenecks, estimation drift, and flaky tests. The retrospective lands in Confluence with velocity trends, carry-over analysis, and specific action items.

Integrates with:

What changes

Before
With ZeroTwo
Retro data source
Memory and whoever speaks up
Jira velocity, GitHub PR metrics, and CI pipeline data
Pattern identification
Same vague complaints sprint after sprint
Specific bottlenecks identified with numbers and trends
Action item quality
'Communicate better' and 'plan more carefully'
'Redistribute PR load from Reviewer X' and 'Fix flaky checkout test'
Prep time
30-60 minutes pulling data manually
Retro doc ready in Confluence before the meeting starts

Retro data source

Before

Memory and whoever speaks up

With ZeroTwo

Jira velocity, GitHub PR metrics, and CI pipeline data

Pattern identification

Before

Same vague complaints sprint after sprint

With ZeroTwo

Specific bottlenecks identified with numbers and trends

Action item quality

Before

'Communicate better' and 'plan more carefully'

With ZeroTwo

'Redistribute PR load from Reviewer X' and 'Fix flaky checkout test'

Prep time

Before

30-60 minutes pulling data manually

With ZeroTwo

Retro doc ready in Confluence before the meeting starts

Retros are driven by whoever talks loudest

Retros are driven by whoever talks loudest and whatever happened most recently. Nobody pulls actual velocity data. The same process problems persist quarter after quarter because the retro is based on feelings, not evidence.

Sprint 14's carry-over tickets were misattributed to scope creep when data showed the real causes were review bottlenecks and estimation inaccuracy. Without data, the team keeps solving the wrong problems.

How ZeroTwo builds the sprint retrospective

1

Pulls sprint completion, carry-over, and cycle time data

Jira

Sprint 14 closed with 34 of 42 story points completed (81% velocity). 8 tickets carried over. 3 tickets were in 'In Review' for 4+ days. 2 tickets re-opened after QA. Average ticket cycle time: 4.2 days (up from 3.1 last sprint).

2

Pulls PR review turnaround, reviewer load, and CI stats

GitHub

47 PRs merged during the sprint. Average review turnaround: 26 hours (up from 18 hours last sprint). 3 PRs waited 4+ days for first review (all assigned to the same reviewer). CI failure rate: 12% (8 of 67 pipeline runs). Most common failure: flaky integration test in checkout module.

3

Identifies patterns across velocity, reviews, and CI data

Google

3 patterns: review bottleneck on one team member (assigned 14 of 47 PRs), estimation accuracy declining (actual exceeded estimates by 34% vs 12% last sprint), and the flaky checkout test caused 6 CI re-runs costing ~3 hours.

4

Writes the retrospective to Confluence with trends and action items

Confluence

Sprint 14 retrospective written with velocity trends (3-sprint graph), carry-over analysis, review bottleneck data, and 3 specific action items with suggested owners.

End of each sprint (biweekly) · Retro doc written to Confluence

Get started in under 10 minutes

1

Connect your tools

One-click OAuth for each integration. No API keys, no engineering.

2

Describe what you need

“At the end of each sprint, pull completion and carry-over data from Jira, PR review turnaround and CI failure rates from GitHub, and write a retrospective to Confluence with velocity trends, identified patterns, and specific action items with owners.”

3

It runs on schedule

Runs at the end of each sprint (biweekly). Retro doc appears in Confluence before the meeting.

Frequently asked questions

No. It replaces the data-gathering and pattern-finding that usually happens (or doesn't happen) before the meeting. The team still discusses, but they start from evidence instead of memory.

Yes. ZeroTwo stores data from each sprint run and surfaces multi-sprint trends. If the same bottleneck appears three sprints in a row, it flags it as a recurring issue.

ZeroTwo adapts to however your team uses Jira. If you use T-shirt sizes, time estimates, or custom fields, the analysis adjusts accordingly.

ZeroTwo identifies systemic bottlenecks, not individual blame. If one reviewer is overloaded, it flags the load distribution as a process problem, not a performance problem. The framing is always about the system.

Yes. ZeroTwo supports Jira, Linear, and Asana for sprint tracking. The analysis adapts to each tool's data model. GitHub and GitLab are both supported for PR and CI data.

Related workflows

Stop doing the work your tools should do for you.

Set it up once. ZeroTwo runs it every time.