What problem does it solve? Interpreting raw experiment outputs scattered across JSON and CSV files is tedious and error-prone, especially when comparing runs across models, hyperparameters, and seeds. This Skill automates the analysis workflow so researchers get structured findings instead of manual spreadsheet work. ## Core Features & Use Cases - Result Aggregation: Locates and parses JSON/CSV result files from figures/, results/, or project output directories into structured data. - Comparison Tables: Organizes results by independent variables (model type, hyperparameters, data config) and dependent metrics, always computing delta versus baseline. - Statistical Analysis: Reports mean and standard deviation across seeds, identifies parameter sweep trends, and flags outliers or suspicious results. - Use Case: After running a hyperparameter sweep with three random seeds, ask the Skill to analyze the results directory and receive a comparison table, key findings with interpretations, and suggested follow-up experiments. ## Quick Start Ask the assistant to analyze the experiment results in the results directory and compare them against the baseline.
