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Strategy for large-N sparse pairwise comparison using TrueSkill, active learning, and rank centrality to rank 100+ candidates from limited comparisons.
Passed 17 of 19 test cases with the skill, and 6 of 19 without it.
and 15 more in the full report
15 fixed6 passed either way1 still failing1 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 11 test cases the skill fixed. Same model, same prompt, with and without the skill.
When reporting the synthesized candidate ranking array in the final YAML output, what fields must be present in each candidate object?
| Checked | Without skill | With skill |
|---|---|---|
| Includes rank, candidate, mu, sigma, and ci fields for each item in the ranking list. | Missed | Passed |
The instructions your model reads when the skill loads.
Produce reliable rankings when the candidate set is too large for complete comparison. Uses information-theoretic pair selection and sparse-matrix rating algorithms to converge quickly with minimal comparisons.
| Resource | Allocation |
|---|---|
| Comparisons | N×log(N) to 3N×log(N) |
| Iterations | 5-20 rounds of adaptive selection |
| Convergence target | Top-k stability ≥ 90% for 3 consecutive rounds |
candidates: [] # full candidate list
comparison_history: [] # [{pair, winner, confidence, round}]
ratings: {} # candidate → {mu, sigma}
method: "" # trueskill | bt-incomplete | rank-centrality
iteration: 0
budget_remaining: 0
convergence: {stable: false, score: 0.0, top_k_stable: false}
test cases passed, vs 6 / 19 without
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