compare

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