GitHub delivery evidence
Build and verify the change with supported GitHub context.
GitHubCycle timeCI insightsNo score
See how pull requests actually move.
Cycle time, CI reliability, and branch risk, grounded in the GitHub activity already connected to lube. Aggregate signal you can drill into, without turning your team into a leaderboard.
pull request · cycle time
2d 4h- Open to first review6h
- Review to approved1d 2h
- Approved to merged3h
- Merged to deployed5h
Insights read the work happening in Engineering.
One connected loop, held on the stage this capability serves. The other stages stay as context so you can see what feeds in and what comes next.
BD
Discovery
Feedback
Product
Engineering
Release
Reliability
Evidence
Engineering
Build and verify the change.
Available now
VCS insights
Inspect delivery workflow activity in the connected release workspace.
Test intelligence
Investigate test outcomes and flakiness evidence across runs.
API Explorer
Keep supported API contracts inspectable alongside release evidence.
Compute pricing
Compare public cloud compute pricing as planning evidence.
Watch merged catch up to opened.
Opened and merged pull requests per week, drawn from the GitHub history lube already indexes. A widening gap means review is falling behind; a closing one means the queue is clearing.
pull requests · per week
opened merged
W1W2W3W4W5
Signal you can point at.
Where a PR actually waits.
The stages from open to deployed, drawn to scale from merged pull-request history. A long review wait shows up as the widest bar, not a hunch.
real review threadsper stagedrillable
pull request · cycle time
22h- Opened to first review6h
- Review to approved14h
- Approved to merged2h
One flag where it's needed.
Activity per contributor fills to its share. Only a blocked or stalled reviewer is flagged, so attention lands on the one thing to unblock, not the whole team.
activity shareat-risk onlyno leaderboard
- dana14 PRs
- sam11 PRs
- rae3 open · stalled reviewat risk
- kai7 PRs
A pipeline you can debug.
Runs, checks, and jobs feed failure rate, recovery time, and a flakiness read, so a red pipeline becomes a diagnosable pattern instead of a mystery.
failure rateCI MTTRflakiness
- failure-rate4.2%
- recovery (MTTR)18m
- flakiest workflowe2e
Signals most dashboards skip.
From CI conclusions to how AI-assisted work is detected, the raw material is specific and honest about its method.
CI conclusions
How a run ended.
successfailurecancelledskippedtimed_outaction_required
Branch risk
Computed level.
lowmediumhighblocked
Branch ops
With dry-run.
deleterenamerefreshprotectunprotect
AI detection
How it is inferred.
co_author_trailermessage_markerauthor_identitycommitter_identity
Governance
Events tracked.
releasessecurity alertsrule eventsbranch protection
Search
Over commits.
shamessageauthortrigram full-text
An honest limit
VCS insights are GitHub-first, with partial GitLab support and Bitbucket largely on the roadmap. Everything here describes workflow activity. It is not a causal measure of individual productivity, and lube will not present it as one.
GitHub
supported source-control integration for cycle time and CI insights
Per-PR
review latency read from real review threads, not estimates
CI MTTR
recovery time tracked over runs so a red pipeline is diagnosable
Risk score
computed per branch with structured reasons before any cleanup
Follow the evidence further.
Ground your delivery conversations in real activity.
Start free
Connect GitHub to get started.