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Optimize experiment design under compute and time budget constraints
Passed 16 of 22 test cases with the skill, and 12 of 22 without it.
and 1 more in the full report
4 fixed12 passed either way6 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 4 test cases the skill fixed. Same model, same prompt, with and without the skill.
In an experiment with 8 potential parameters where compute is limited to 15 total runs, when in the planning sequence should parameter levels be finalized? Provide the sequence positioning relative to computing run limits and building the final matrix.
| Checked | Without skill | With skill |
|---|---|---|
| Places level specification after computing maximum runs and before selecting design type or constructing design matrix | Missed | Passed |
The instructions your model reads when the skill loads.
| Available Runs | Recommended Approach |
|---|---|
| < 10 | One-factor-at-a-time or Plackett-Burman screening |
| 10-30 | Fractional factorial (Resolution III-IV) |
| 30-60 | Fractional factorial (Resolution V) or Taguchi |
| 60-120 | Full factorial on top factors + screening on rest |
| 120+ | Full factorial or RSM with replication |
test cases passed, vs 12 / 22 without
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