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Make AI-powered assertions about screen state
Passed 22 of 22 test cases with the skill, and 9 of 22 without it.
and 11 more in the full report
13 fixed9 passed either way0 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 13 test cases the skill fixed. Same model, same prompt, with and without the skill.
I am calling testdriver.assert in JavaScript and need to configure top-k sampling for the AI model to restrict token choices to the top candidate. What object hierarchy should be passed to assert?
| Checked | Without skill | With skill |
|---|---|---|
| Nests top-k sampling configuration under the ai.top.k property path. | Missed | Passed |
The instructions your model reads when the skill loads.
Make assertions about the screen state with natural language and AI. The AI examines the screen. It makes sure that your assertion is true.
await testdriver.assert(assertion)
await testdriver.assert(assertion, options)
<Expandable title="properties">
<ParamField path="temperature" type="number">
Controls randomness. `0` = deterministic, higher = more creative. Default: model default.
</ParamField>
<ParamField path="top" type="object">
Sampling parameters
<Expandable title="properties">
<ParamField path="p" type="number">
Top-P (nucleus sampling). Range: 0-1.
</ParamField>
<ParamField path="k" type="number">
test cases passed, vs 9 / 22 without
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