Sample Code Mower dashboard
A concrete preview of the kind of private team signal CodeMower.com is meant to show after opt-in metadata uploads.
Illustrative sample data
This page uses example numbers to show the product shape before sign-in. It is not a live cross-team cohort benchmark. Real dashboards use your team's own metadata first; cohort comparison becomes useful only as enough teams opt in.
The minimal OSS example for this same loop lives in examples/demo-calibration: one known-clean control, one known-blocked control, and a sample reviewer value report. This page shows the richer dashboard shape once a team has uploaded more history.
Dashboard areas in the signed-in product
CodeMower.com is organized around operating decisions, not raw upload logs. Each signed-in page answers a specific question and links back to the source metadata that made the recommendation possible.
Reviewer Value
Which AI reviewer/lens should I trust?
Ranks provider/lens pairs by useful signal, false positives, cost, latency, and current recommendation.
Repository Health
Where is my evidence strong or thin?
Shows coverage by repo, separates dogfood/history/calibration, and flags repos that still need reviewer events.
Reports & Evidence
What artifacts are backing the dashboard?
Surfaces uploaded report kinds, recent evidence rows, and provenance so teams can audit what the numbers mean.
Productivity & ROI
What is the modeled value of the loop?
Models parallel reviewer capacity, cost per useful signal, and net value without pretending one human has more hours.
What uploading metadata unlocks
Reviewer value
Which provider/lens pairs catch useful issues without noisy false positives on your own repos.
Cost and latency
How much each lane costs and how long it takes before you promote it into a stronger workflow.
Data provenance
Dogfood, imported history, and calibration evidence stay visibly separated so the dashboard does not overclaim.
Enable-next guidance
Practical recommendations for informational, selective, or merge-gating lanes as evidence accumulates.
Evidence classes stay separate
CodeMower.com keeps operational dogfood, imported history, and reviewer evidence visibly distinct so the dashboard can be useful without overstating what the data proves.
Current dogfood
4
steps
54
events
Use for ingestion health, recency, repo coverage, and whether dogfood workflows are running.
current repo metadata and provider inventory
Imported history
2
steps
18
events
Coverage and timeline context before dogfood was enabled.
sanitized GitHub Actions history
Reviewer evidence
3
steps
66
events
Use for reviewer and lens value once verdicts are tied to known-clean and known-blocked cases.
metadata-only reviewer verdict artifacts
Issue planning lineage
GitHub issue to delivery
codemower-ai/code-mower#269
Code Mower can carry metadata from a GitHub issue into a posted plan, work order, builder/reviewer run, PR, merge, and upload. The dashboard shows what is captured and what is still missing before claiming end-to-end delivery evidence.
codemower-ai/code-mower#269
issue-269-plan.md
issue-269-work-order.md
PR not linked yet
Reviewer checks not linked yet
Merge not linked yet
Metadata uploaded
Next missing evidence: PR.
Sample dashboard preview
This preview uses the same dashboard components as the signed-in team experience, populated with representative sample data inspired by the kind of reviewer effectiveness and productivity signal teams care about.
Cloud activity
Benchmark signal at a glance
Recent uploads, reviewer events, and source provenance for your team. The goal is to make dogfooding health visible before anyone has to read the raw tables.
Uploads
41
Metadata bundles received for your teams.
Events
128
Structured reviewer and workflow events.
Repos
4
Distinct repositories seen in recent metadata.
Spend
$42.18
Recent reported reviewer spend.
Latency
84s
Average recent provider/runtime latency.
Activity trend
Upload and event volume over the last 14 days.
Latest day
06-15
Latest uploads
4
Latest events
6
Source mix
Dogfood, calibration, and imported history stay separated so the signal is explainable.
Dogfood
54
Calibration
74
Historical
18
Anonymous uploads
0
Reviewer signal
Which lanes are earning trust?
These cards summarize useful signal, noisy lanes, spend, latency, and the next lane recommendation from your own uploaded metadata.
Best reviewer so far
Not enough data yet
Upload calibration events to identify which reviewer/lens has useful signal on your codebase.
Noisy lanes
No noisy lane yet
No recent lane has more false positives than useful signal.
Cost / latency
$42.18 / 84s
Recent reported provider spend and average runtime latency across uploaded events.
What to enable next
Promote codex-audit on risky PRs
Base Codex reviews have the best useful-rate in this sample with low false positives and acceptable latency.
Evidence maturity
How strong is this data?
Code Mower should not overclaim. This ladder shows which decisions are supported now, which data is only context, and what is still needed before treating provider/lens comparisons as benchmark-grade.
Step 1
Current operations
You can tell whether current metadata upload plumbing is alive.
41 uploads and 54 dogfood events in scope.
Next: Keep dogfood running from active repositories.
Step 2
Historical context
You can use imported history for activity and workflow context.
18 imported history events in scope.
Next: Keep history visually separated from reviewer accuracy evidence.
Step 3
Reviewer value
You can start comparing provider/lens signal in this scope.
66 reviewer runs, 74 calibration events, and 41 useful signals.
Next: Keep collecting known-clean and known-blocked cases before promoting lanes.
Step 4
Benchmark trust
Provider/model/version provenance is strong enough for early benchmark claims.
92% tool, 81% model, and 72% version coverage.
Next: Use this scope for early provider/lens comparisons, with sample-size caveats.
Productivity model
Parallel AI review capacity
Code Mower can exceed 24 hours/day because this is aggregate portfolio capacity across agents, reviewer lanes, and repos. It is not a claim that one human can work more than one wall-clock day.
Reviewer runs
66
Provider/lens runs represented in recent metadata.
Useful signals
41
Useful issues or decisions captured by reviewer events.
Capacity modeled
2d 5h
Conservative aggregate review and follow-up attention estimate.
Net value
$7,870
Modeled value minus reported provider spend in the recent window.
Assumptions
Calculation
This is a model, not accounting. v1 should let teams tune assumptions and should distinguish reviewer value, builder value, avoided defects, and cycle-time acceleration.
Calibration cases
24
Known-clean and known-blocked PRs used to judge reviewer behavior.
Reviewer runs
128
Structured reviewer and lens runs across the calibration corpus.
30-day spend
$42
Reported API or subscription spend from local metadata.
Avg latency
84s
Average reviewer runtime for recent structured audits.
codex-audit
Strong useful-rate with a low false-positive rate on the sample corpus.
gemini-cli / operability
High false-positive rate means this lane should stay advisory until calibrated further.
Selective triggers
Run Codex on every candidate PR; trigger Claude quality lens on backend/auth/data changes.
What this dashboard should help you decide
Who should review every risky PR?
Use codex-audit as the baseline lane.
It caught the known-blocked control and stayed quiet on the known-clean control.
Who should be selective?
Use Claude for matching backend/auth/data classes.
It added useful independent signal, but with higher cost and latency in this sample.
Who should stay informational?
Keep the experimental lens out of branch protection.
It was cheap, but it blocked a clean control and missed a blocked control.
Reviewer value table
| Lane | Lens | Useful rate | False positives | Cost / run | Latency | Recommendation |
|---|---|---|---|---|---|---|
| codex-audit | base | 82% | 9% | $0.42 | 71s | Merge-gating eligible after one more clean cycle |
| claude-audit | context-driven-quality | 74% | 14% | $0.61 | 96s | Selective trigger for higher-risk PRs |
| gemini-cli | operability | 38% | 31% | $0.08 | 52s | Informational until more calibration data exists |
Why contribute metadata?
The local OSS tool already gives you private reports. Opt-in cloud sharing adds team history, dashboard rows, token management, export/delete controls, and a place to compare reviewer value over time without keeping every terminal artifact in someone's laptop. Cross-team comparisons come later, after enough teams opt in for the data to be honest.
Ready to try the local path? Follow the setup guide. Want the OSS source first? Open GitHub.