Use when an existing model's results are disappointing and the user wants higher accuracy - 'accuracy is still too low', 'improve/boost the model', 'why is it bad', 'make it better than the paper', 'combine methods to beat SOTA'. Diagnoses the real bottleneck first (error analysis, learning curves, leakage, saliency), then researches the specific gap, synthesizes a principled combination of techniques from the literature, and ranks next levers by expected impact - with honest ceilings, not magic promises.
How to install accuracy-improvement-loop?
mxslr/mlcraft/accuracy-improvement-loop
$npx -y skills add mxslr/mlcraft --skill accuracy-improvement-loopInstalls into the current project.
›Prefer a prompt? Paste this to your agent
Run `npx skills use "https://github.com/mxslr/mlcraft" --skill "mxslr/mlcraft/accuracy-improvement-loop"` and follow the generated skill instructions now. Read its complete output, redirecting it to a temporary file first if necessary. Resolve relative paths from the supporting-files directory it provides.
Use the skills in "https://github.com/mxslr/mlcraft" that are relevant to the current task. Run `npx skills add "https://github.com/mxslr/mlcraft"` and select the relevant skills, then follow their instructions.
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