How can a manager start using the Typo Dashboard?
Daily Engineering Health Checklist (for managers)
Goal: Spot early signals, understand where pain concentrates, and intervene before problems compound
1. Start with the baseline
Before reacting to anything, ask:
What has been normal for this team in the last 4–8 weeks?
Is today’s movement noise or a real shift?
2. Always compare the middle to the tail
Never read averages alone.
Average ≈ P75 Flow is consistent. Problems are systemic or mild.
Average << P90 Pain lives in the tail. A small set of PRs or issues is dragging the system.
P75 is creeping up week over week The whole system is degrading, not just edge cases.
Rule of thumb: Most engineering problems start in the tail before they show up in averages.
3. Cycle Time: ask where the tail begins
When cycle time looks bad:
Check where P75 and P90 diverge.
Then break the cycle time into stages.
Interpretation:
Coding Time Long tail → complex or unclear work causing rework or stalled implementation.
Pickup Time Long tail → work waiting for prioritization or clear ownership.
Review Time Long tail → reviewer bottlenecks, large PRs, or uneven review load.
Merge Time Long tail → release process friction, flaky checks, or coordination delays.
Don’t treat cycle time as one problem.
4. PR Size + Review Time must be read together
Individually, they mislead.
Large PRs + long review tail Reviews are shallow, slow, or avoided.
Small PRs + long review tail Review capacity or process issue.
Large PRs + normal review time The team may be compensating. Watch CFR next.
This pairing usually explains 60–70% of delivery drag.
5. Look for concentration, not spread
Every day, ask:
Are delays spread evenly or concentrated in:
specific repos
specific teams
specific reviewers
specific weeks
See the “Risks” for each team to identify the concentration of issues
Concentration means a lever exists. Spread usually means structural issues.
6. Incidents and delivery must be time-aligned
When incidents rise:
Look backward, not just at the incident page.
What changed 5–14 days earlier?
PR size?
Review depth?
Scope churn?
AI-generated code volume?
7. Use contributor metrics to spot system stress, not performance
Daily question:
Who is the system leaning on the hardest right now?
Signals to watch:
Review load concentration
Workload distribution
Review time tail
Sudden drop in coding days
Interpretation:
High load ≠ underperformance
It usually means a hidden dependency or a knowledge silo
8. AI coding metrics only matter with downstream effects
Don’t stop at adoption or acceptance.
Always check:
Are AI-assisted PRs larger?
Do they take longer to review?
Does CFR rise after high AI usage weeks?
9. Allocation drift
If delivery feels slow:
Check bug vs feature vs task mix.
Watch for week-over-week drift, not absolute numbers.
Interpretation:
Rising bug share
High task share
10. End every review with one decision
If you can’t answer this, you’re still dashboarding:
What is the one thing I’ll change or watch more closely this week?
Good examples:
Cap PR size for 2 teams
Redistribute reviews
Pause the new scope for a sprint
Fix a single release bottleneck
Use Goals in Typo to set up alerts for best practices and metric benchmarks in the team.
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