ml-paper-to-code

What problem does it solve?

This Skill prevents silent math and shape bugs by enforcing a disciplined translation from research paper notation and equations into verified implementation code before any production training begins.

Core Features & Use Cases

• Notation-to-code mapping: Create explicit tables that map paper symbols to code variables to avoid dimension and sign errors.
• Rigor levels: Choose Level 1 (architecture match), Level 2 (equation verification with unit tests), or Level 3 (full derivation tracing and gradchecks) depending on project needs.
• Target scenarios: Implementing novel losses, custom layers, attention mechanisms, porting architectures, and reproducing paper results with reproducibility safeguards.

Quick Start

Ask the assistant to implement a paper section by first producing a notation-to-variable mapping, selecting a rigor level, and providing per-equation tests and shape checks.