review-accuracy-calibration
Installation
SKILL.md
Review Accuracy and Calibration
Overview
The accuracy problem in code review has two faces: over-flagging (false positives that waste reviewer and author time) and under-flagging (missing real defects). AI-assisted review tools generate false positives that waste 2-5 hours per developer per week, and 25% of AI suggestions contain errors. The fix is not reviewing less — it is calibrating more precisely.
This skill provides the meta-layer that makes every other review skill more effective: a confidence model, heuristics to suppress false positives, a severity calibration table, and an escalation decision guide. Load when filtering comments, assigning severity, or deciding whether to block a PR.
Quick Reference — Confidence Levels
| Level | Label | Post? | Severity floor |
|---|---|---|---|
| C4 — Certain | You have evidence: test failure, spec violation, data loss | Yes | HIGH or CRITICAL |
| C3 — High | Strong reasoning: well-known anti-pattern, measurable impact | Yes | MEDIUM or higher |
| C2 — Medium | Plausible concern but depends on context you lack | Conditional | LOW or NIT |
| C1 — Low | Speculative; could be intentional or context-dependent | No (investigate first) | — |