TASK-004E: Inspection Results and Run Metrics
AI Execution Profile
- Model class:
Balanced - Reasoning effort:
Medium
Scope
- enrich live inspection result and metric models with more useful but still simple fields
- project richer run metrics through canonical app state during active work
- extend run summary and history persistence with the richer fields introduced by this slice
- keep the result model believable without turning it into a complex machine-vision subsystem
Copy/Paste Prompt
text
Implement only TASK-004E: Inspection Results and Run Metrics.
Read first:
- docs/specs/SLICE-004-operational-maturity.md
- docs/adrs/ADR-001-use-central-app-state-store.md
- docs/adrs/ADR-002-file-backed-run-history-store-before-database-persistence.md
- docs/adrs/ADR-004-use-one-operational-maturity-slice-before-specialized-modules.md
- docs/tasks/slice-004/TASK-004E-inspection-results-and-run-metrics.md
Goal:
- Make runs and run history more informative by adding richer but still simple results and metrics.
Scope:
- Add live run metrics such as completed points, elapsed duration, total defects, and simple grouped defect counts where practical.
- Extend persisted run summary and history models with the richer fields.
- Include selected simulator profile information where it helps explain the run.
- Keep everything tied to the current run and canonical app state.
Do not:
- Build charts or dashboards
- Add advanced computer vision logic
- Create a parallel history model outside the existing persistence boundary
Important:
- Prefer a believable, teachable model over a large data schema
- Keep persistence upgrades backward-aware where practical