results-analysis

You can generate publication-ready figures and draft Results text by uploading your experimental data logs. The skill analyzes model evaluation outputs and produces multi-figure visuals, tables, and drafted text sections that satisfy publication reporting standards.

Yes, statistical validation is directly supported for model evaluation outputs. The skill performs t-tests, ANOVA, and computes effect sizes on your experimental data to rigorously support the claims in your generated results text.

Analyzing TensorBoard logs and CSV/JSON data for multi-model comparisons is fully supported. The skill reads these experiment outputs, applies statistical testing, and generates cohesive comparison visuals and results text across different datasets.

To ensure reproducibility when drafting a paper's Results section, the skill applies standardized reporting criteria and reproducibility checks to your experimental data. This process guarantees that your generated text, figures, and statistical validations meet strict publication standards.

The tool works directly with raw CSV and JSON experimental logs without requiring manual preprocessing. It automatically loads your experiment outputs, applies statistical testing, and generates multi-figure visuals and results drafting templates.

A key limitation when analyzing experimental data is that the skill requires structured evaluation outputs like CSV, JSON, or TensorBoard logs. It generates results text and visuals based on the provided experimental data, so unstructured raw text inputs may not yield proper statistical validation or figures.