scholar-experiment
bycatlog22· 36 skills
Systematic experimental results analysis workflow for ML/AI research papers. Connects experimental data to publication-ready Results sections with statistical validation, visualizations, and quality checks. Triggers on "analyze experimental results", "generate results section", "statistical analysis of experiments", "compare model performance", "create results visualization".
How to install scholar-experiment?
catlog22/maestro-flow/scholar-experiment
$npx -y skills add catlog22/maestro-flow --skill scholar-experimentInstalls into the current project.
›Prefer a prompt? Paste this to your agent
Run `npx skills use "https://github.com/catlog22/maestro-flow" --skill "catlog22/maestro-flow/scholar-experiment"` 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/catlog22/maestro-flow" that are relevant to the current task. Run `npx skills add "https://github.com/catlog22/maestro-flow"` and select the relevant skills, then follow their instructions.
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