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Normalize results by compute budget (Pareto analysis)
Passed 20 of 22 test cases with the skill, and 3 of 22 without it.
and 38 more in the full report
19 fixed1 passed either way0 still failing2 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 19 test cases the skill fixed. Same model, same prompt, with and without the skill.
Evaluate Graph Neural Networks on OGB-ArXiv F1 score: GCN (71.7%), GAT (73.8%), GraphSAGE (72.6%). Compute profile: GCN (0.001e17 FLOPs, 0.5 GPU hours, 0.2M params, $1.50 cost), GAT (0.012e17 FLOPs, 3.5 GPU hours, 1.4M params, $9.00 cost), GraphSAGE (0.004e17 FLOPs, 1.2 GPU hours, 0.6M params, $3.00 cost). Calculate compute-normalized scores and Pareto optimal choices.
| Checked | Without skill | With skill |
|---|---|---|
| The JSON output contains an efficiency_rankings array. | Missed | Passed |
| The efficiency_rankings items contain score_per_gpu_hour fields. | Missed | Passed |
The instructions your model reads when the skill loads.
Analyze the performance-compute tradeoff across methods. Identify Pareto-optimal methods (best performance for a given compute budget), compute-normalized rankings, and efficiency frontiers. Essential for practical method selection under resource constraints.
| Field | Type | Description |
|---|---|---|
| method_scores | object[] | Array of {method, dataset, metric, score} |
| compute_costs | object[] | Array of {method, flops, gpu_hours, params, training_cost_usd} |
{
"pareto_frontier": [
{
"method": "string",
"score": 0.0,
"compute_metric": "string",
"compute_value": 0.0,
"is_pareto_optimal": true
}
],
"efficiency_rankings": [
{
"method": "string",
"score_per_flop": 0.0,
"score_per_gpu_hour": 0.0,
"score_per_param": 0.0
}
],
"compute_normalized_scores": [
{
"method": "string",
"raw_score": 0.0,
"normalized_score": 0.0,
test cases passed, vs 3 / 22 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.