Engineering health brief
Northstar Labs, a fictional sample organisation · Last 90 days · 1,284 merged pull requests
Review pickup is the constraint: 31 hours waiting, 20 hours reviewing
Northstar Labs cut cycle time 14% without weakening reliability, so the bottleneck has moved. Work now waits longer for attention than it spends in active review, and 18 pull requests sat unclaimed for more than 48 hours.
Read-only GitHub · Your code stays yours · Team-level by default · No mandatory call · No credit card
Engineering health score
These are the five dimensions CodePulse grades, against published thresholds. Each bar is that dimension's score out of 100; the figure beside it is the measurement behind the score. Every dimension stays visible, so a single letter never hides a weak one.
Median cycle time
3.3 days
↓ 14%Improved versus the previous 90 days
Review pickup
31 hours
↑ 22%The largest and fastest-growing wait state
Review coverage
91%
↑ 4 ptsMost merged work received human review
Merge without approval
4.1%
↓ 1.2 ptsMerged PRs with no approving review
Every reading below describes the system - queues, handoffs, review load, ownership - and never individual productivity. Each finding is followed by the experiment it suggests and the measure that would show it worked.
Decision layer
Evidence-backed action plan
Included with GitHub alone
The report turns observed constraints into bounded experiments. It does not invent revenue, headcount, or causality that the connected data cannot establish.
Evidence
Review pickup now consumes 39% of cycle time and 18 PRs waited longer than 48 hours.
Experiment
Route unclaimed reviews after 12 working hours and set a 24-hour first-review service level.
Success measure
Bring median review pickup below 18 hours without reducing review integrity.
Evidence
Seven frequently changed files have a bus factor of one; two sit in the payments path.
Experiment
Assign a secondary owner and require paired review on the highest-risk modules.
Success measure
Reduce critical single-owner files from seven to three this quarter.
Evidence
Bug-fix work rose to 17% while feature investment fell four points.
Experiment
Reserve one capacity lane for the two defect clusters creating the most rework.
Success measure
Return bug-fix allocation below 12% while keeping change failure under 8%.
Your plan will name your constraints, not Northstar's. The first version is ready once your initial sync completes.
The same constraint, in a real organisation
“I'd been telling the CTO we were slow because of tech debt. Turns out almost a third of our PRs were just sitting there waiting on the same two reviewers. We redistributed reviews and the difference was noticeable within a couple weeks.”
Review concentration is the most common constraint we find, and it is invisible in a sprint board. It shows up in the review network further down this report as two people carrying the connections everyone else runs through.
Northstar’s figures are illustrative. This one is not.
Delivery flow
Where the 3.3-day cycle time goes
Included with GitHub alone
Phase timing separates active work from queue time, so a faster team is not confused with a less-blocked system.
Coding
18h
23% of total cycle time
Waiting for review
31h
39% of total cycle time
Active review
20h
25% of total cycle time
Approval to merge
11h
13% of total cycle time
Delivery forecast
For the current 23-PR queue: 50% likelihood of clearing within 4 days; 85% within 8 days, from a Monte Carlo simulation over historical throughput and PR size.
Production outcomes
DORA metrics from deployment events
Requires deployment events
With GitHub deployment data connected, the report distinguishes shipped software from merged pull requests, and grades each metric against the published DORA thresholds.
Deployment frequency
11 / week
Elite performance tier
Measured from deployment data
Lead time to deploy
1.8 days
High performance tier
Measured from deployment data
Change failure rate
7.2%
Medium performance tier
Measured from deployment data
Time to restore
3.4 hours
High performance tier
Measured from deployment data
Tiers follow the published DORA thresholds, including the unflattering ones: 7.2% change failure rate is Medium, not High. Without deployment data, CodePulse marks merge-based proxies with an amber indicator instead of presenting them as production outcomes.
Investment
Where engineering capacity went
Included with GitHub alone
PRs are classified into system-level work types so leadership can discuss portfolio balance without counting individual activity.
End-to-end flow
From work item to merged change
Requires an issue tracker or GitHub Issues
With Jira, Linear, or GitHub Issues connected, the report includes backlog dwell instead of starting the clock only when coding begins. Link coverage is reported beside it, because every issue-derived figure depends on it.
Backlog dwell
1.6 days
Issue created → first commit
Build and review
3.3 days
First commit → merged PR
Total issue lead time
7.8 days
Issue created → merged PR
Issue linkage coverage
76%
Merged PRs connected to work items
Issue keys are read from branch names, PR titles, and GitHub closing references, so link coverage is a data-quality figure worth watching rather than a target.
Review system
Review load and review depth in the same view
Included with GitHub alone
Volume alone cannot tell you whether review is healthy. CodePulse combines team-level workload concentration with the tone and content of the review discussion itself.
Who reviews whom, weighted by volume
Reviews submitted
1,046
Top-two concentration
42%
Waiting over 48h
18 PRs
Actionable comments
74%
Share of review comments classified as actionable
Constructive tone
88%
Constructive or positive rather than critical
Bugs and security issues flagged
14
Comments categorised as bug or security findings
Average comment length
214 chars
Depth signal across 1,278 review comments
Knowledge and change risk
Hotspots that need a second pair of hands
Included with GitHub alone
Changed-file paths, churn, ownership, and review history expose operational concentration without ranking developers.
payments/settlement.ts
46 changes · 1 primary owner · churn up 31%
Add a secondary owner before the next settlement release.
auth/session-manager.ts
38 changes · 2 owners · 6 high-risk PRs
Require two reviewers when authentication behaviour changes.
reporting/export-worker.py
29 changes · 1 primary owner · no review backup
Pair on the next change and document the operational runbook.
AI investment
AI spend, output, acceleration, and reliability
Requires an AI provider connection
When AI providers are connected, the report compares spend with shipped work and quality signals. Before-and-after movement is labelled as association, not proof of causality.
Observed seats
42 / 58
72% roster coverage
AI spend
$4,860
Across connected providers
Merged PRs
312
$15.58 per merged PR
Acceleration
+12%
Medium confidence versus pre-adoption
Throughput improved after adoption, while revert rate remained at 3.8%. The correct conclusion is “promising, keep measuring”, not “AI caused a 12% gain”.
Scope
What you get on day one, and what each integration adds
This sample shows a fully connected organisation. Most of it needs nothing but read-only GitHub. The rest is labelled, and stays absent rather than estimated until you connect the source.
From GitHub alone, in about five minutes
- Engineering health score across all five graded dimensions
- Cycle time split into coding, review pickup, active review, and merge
- Review coverage, merge-without-approval, and review load distribution
- Review comment depth, tone, and bug or security findings
- File hotspots, churn, and single-owner knowledge risk
- Work-type investment mix from PR and branch labels
- Delivery forecast for the current queue
Added by optional integrations
GitHub deployment events
DORA measured from real deploys: deployment frequency, lead time to production, change failure rate, time to restore
Jira, Linear, or GitHub Issues
Backlog dwell, full issue-to-merge lead time, issue link coverage, and issue-derived work types
AI provider (Anthropic, OpenAI, Copilot)
AI spend against merged output, seat roster coverage, and pre- versus post-adoption movement
None of these are required to start, and none are billed separately.
Confidence and trust
Know what the report can, and cannot, conclude
A credible report exposes its data boundary. Missing integrations reduce confidence instead of being filled with invented estimates.
Coverage in this sample
- 1,284 merged pull requests across 8 repositories
- 93% work-type classification coverage
- 1,278 review comments categorised for tone and content
- 76% of merged PRs linked to an issue
- 72% of the AI-tool seat roster observed
Coverage is reported, not assumed. Where it is low, the figures that depend on it are labelled rather than quietly presented as complete.
Connection boundary
- Read-only GitHub permissions; CodePulse cannot push, merge, or modify repositories.
- Your code stays yours: repositories are never cloned and files are never read.
- The only code stored is the short snippet GitHub attaches to an inline review comment, retained so review depth and quality can be assessed.
- Results are team-level by default; individual views require an explicit organisation setting.
You leave with the artifact, not just the dashboard
This brief is a view in the product, and it is also something you can put in front of a board without rebuilding it in slides the night before.
PowerPoint export
The executive summary as a .pptx deck, with the health grade, the phase breakdown, and the action plan already laid out.
PDF and CSV
The report as a PDF for circulation, and the underlying figures as CSV when someone wants to check the arithmetic.
Scheduled digests
A recurring summary to you or your leadership group, so the next board pack starts written rather than blank.
Every plan is one flat price per organisation, whatever your headcount. We never bill per seat, so this report does not get more expensive as your team grows.
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