oracle-dev

This Claude Skill provides a specialized workflow for developing, training, and deploying machine learning models. It supports various architectures like CNNs and RNNs, handles the full lifecycle from training to production, and outputs trained models, metrics, and deployment packages. Use it when you need to build a new ML model, require model training, or are preparing for a production deployment.

This skill helps developers diagnose and fix common machine learning training issues like loss divergence, overfitting, and slow convergence. It provides systematic debugging to identify root causes and delivers fixed code with optimization recommendations. Use it when facing training problems like NaN losses, poor validation performance, or when training fails to converge properly.

This skill executes implementation plans by dispatching a fresh subagent for each independent task, with code review between tasks. It enables fast iteration while maintaining quality gates through this review process. Use it when working on mostly independent tasks within the same session to ensure continuous progress with built-in quality checks.

This skill enables semantic and text-based search through previous Claude Code conversations to retrieve facts, patterns, and decisions. Developers should use it when a partner references past discussions or when debugging familiar issues to avoid reinventing solutions. It operates on a "search before reinventing" principle and requires using subagents for efficient context management.