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Strategy for auditing preference consistency using Consistency Ratio, cycle enumeration, and mElo to detect and resolve intransitivities.
Passed 18 of 22 test cases with the skill, and 12 of 22 without it.
and 7 more in the full report
8 fixed10 passed either way2 still failing2 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 8 test cases the skill fixed. Same model, same prompt, with and without the skill.
We are running an audit based on AHP Consistency Ratio calculation. What value should be recorded in the 'method' field of the standard diagnosis YAML output?
| Checked | Without skill | With skill |
|---|---|---|
| Specifies 'consistency-ratio' as the method in the YAML output block. | Missed | Passed |
The instructions your model reads when the skill loads.
Audit an existing comparison matrix for logical consistency. Detect cycles, quantify transitivity violations, localize problematic judgments, and recommend corrections. Essential as a quality gate before finalizing any ranking.
| Resource | Allocation |
|---|---|
| Cycle detection | Full matrix scan |
| Consistency metric | CR < 0.1 (AHP) or transitivity > 90% |
| Repair comparisons | ≤ 20% of original comparisons |
| Iterations | 1-3 audit-repair cycles |
comparison_matrix: {} # pair → winner
candidates: []
consistency_metrics: {cr: null, transitivity: null, cycles_count: 0}
cycles: [] # [[a>b, b>c, c>a], ...]
problematic_pairs: [] # pairs involved in most cycles
repair_log: [] # [{pair, old_judgment, new_judgment, reason}]
iteration: 0
test cases passed, vs 12 / 22 without
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