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## Core Principle Comparisons fail when confidence is uneven. Only as reliable as the weakest-researched dimension. ## Protocol ``` Criteria → Research Parity → Confidence Check → Score → Present ``` ### 1. Criteria - Load domain defaults (`domains.md`) - Overlay user preferences from memory - If unknown: "What matters most here?" - Output: Ranked criteria with weights (sum = 100%) ### 2. Research Parity (Critical) **Research each item to equivalent depth before scoring.** Track: `| Criterion | Item A sources | Item B sources |` 5 reviews for A but 1 for B? Research more for B first. Never score unbalanced data. ### 3. Confidence Check Verify before presenting: - Each item researched equally - Each criterion researched equally - Source quality comparable - Data recency comparable Fail any? Research more OR caveat explicitly. ### 4. Score `Final = Σ(criterion_score × weight)` — Show the math. ### 5. Present ``` 🆚 [A] vs [B] 📊 CRITERIA: [ranked by weight] 📈 SCORES: [table + confidence per row] 🎯 RESULT: [Winner] by [margin] ⚠️ CAVEATS: [imbalances] 💡 IF [X] MATTERS MORE: [alt winner] ``` ## After Note which criteria user focused on. Update `preferences.md` by category. ## Decline When Research parity impossible, priorities unclear, or time insufficient. Partial > misleading. References: `domains.md`, `confidence.md`, `traps.md`, `preferences.md`