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Verifies math-heavy code for algorithmic correctness and numerical stability.
Passed 15 of 20 test cases with the skill, and 7 of 20 without it.
and 7 more in the full report
8 fixed8 passed either way6 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
This benchmark measured an earlier version of the skill.
One of 8 test cases the skill fixed. Same model, same prompt, with and without the skill.
We have documented mathematical derivations in `derivation.ipynb` alongside our implementation in `src/ml/layer.py`. What command should be executed to verify that the derivation notebook runs end-to-end without errors?
| Checked | Without skill | With skill |
|---|---|---|
| executes jupyter nbconvert --execute derivation.ipynb to validate notebook execution | Missed | Passed |
The instructions your model reads when the skill loads.
Intensive analysis ensuring numerical stability and alignment with standards.
/math-review
Verification: Run the command with --help flag to verify availability.
math-review:context-syncedmath-review:requirements-mappedmath-review:derivations-verifiedmath-review:stability-assessedmath-review:evidence-loggedmath-review:findings-verifiedpwd && git status -sb && git diff --stat origin/main..HEAD
test cases passed, vs 7 / 20 without
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