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Get Started Free →fbarrento/zoom-out
Tell the agent to zoom out and give broader context or a higher-level perspective.
Passed 21 of 22 test cases with the skill, and 21 of 22 without it.
1 fixed20 passed either way0 still failing1 broke
Tokens, turns and time describe resource use, not a dollar price.
A test case the skill fixed. Same model, same prompt, with and without the skill.
In `models/attention.py`, the low-level matrix multiplication and tensor slicing in `forward()` are hard to follow. Don't trace tensor dimensions or PyTorch `einsum` operations; step back and map calling neural network layers and model pipelines using Deep Learning domain concepts.
| Checked | Without skill | With skill |
|---|---|---|
| Identifies calling layers such as Transformer Backbone. | Missed | Passed |
| Uses domain concepts like Multi-Head Attention. | Passed | Passed |
The instructions your model reads when the skill loads.
I don't know this area of code well. Go up a layer of abstraction. Give me a map of all the relevant modules and callers, using the project's domain glossary vocabulary.
test cases passed, vs 21 / 22 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.