Browse skills

Checks evaluator outputs against ground truth data to identify biases, errors, and performance inconsistencies.

Analyzes its own reasoning process to identify biases, errors, and areas for improved performance in complex tasks.

Identifies and suggests fixes for TensorFlow code errors, leveraging snakeo's expertise in debugging deep learning models.

Sundial-org's training-data-curation refines datasets by identifying biases, errors, and gaps for improved AI model performance.

Identifies and suggests fixes for common errors in PyTorch code, streamlining development and improving model performance.

Identifies and suggests fixes for errors in scikit-learn model code, improving accuracy and efficiency.

This skill automatically reports TTS errors, providing valuable data for identifying and resolving audio generation issues quickly.