Data Science & Analytics
Metric Design
Phase 1 of the auto-improvement loop — how the app designs and calibrates the ruler (the trustworthy metric) BEFORE any optimization; calibration itself is backend code, read its result with get_ruler. Covers a low-variance primary metric, frozen anchors, a calibrated noise band, guardrails, reward-hack guards, and a mandatory canary that must clear the band or the ruler is rejected.
- Publisher / source
- kirodotdev/KiroCrew (found on GitHub top repositories)
- Upstream record
- kirodotdev/KiroCrew
- Version
- Version 1
- Published
- Publication review
- Approved for publication · recorded passed scan 2026-11-05
- Content fingerprint
9880009db2f00a6b113d56c5ece40c85c4fd1b8a7b213e5d21da497ec18cdb31