ml-paper-to-code

Translate research paper equations into tested model implementations with shape checks.

Updated Feb 23, 2026

Search Tags:#testing #pytorch #ml #research-paper #notation-mapping #loss-implementation #gradcheck

Ready to run

npx skills add https://github.com/rishikanthc/ml-superpowers --skill ml-paper-to-code

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: ml-paper-to-code
Source: https://github.com/rishikanthc/ml-superpowers/tree/main/skills/ml-paper-to-code
Command: npx skills add https://github.com/rishikanthc/ml-superpowers --skill ml-paper-to-code

SYSTEM DOCUMENTATION & REQUIREMENTS

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.

Frequently Asked Questions about ml-paper-to-code

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I translate ML research paper equations into tested PyTorch model code?▼

To translate ML research paper equations into tested PyTorch code, create a notation-to-variable mapping table to match paper symbols to code variables, preventing silent math and shape bugs before production training begins.

What is a notation-to-variable mapping table for implementing novel loss functions?▼

A notation-to-variable mapping table explicitly links research paper symbols to code variables during implementation. It prevents dimension and sign errors when porting novel losses, custom layers, or attention mechanisms from academic papers.

How do I verify custom PyTorch operators from research papers using gradcheck?▼

Verify custom PyTorch operators by selecting Level 3 rigor, which applies full derivation tracing and autograd gradchecks alongside per-equation unit tests and input/output shape checks for maximum reliability.

Can I implement a paper architecture without writing per-equation unit tests?▼

You can implement a paper architecture without per-equation unit tests by selecting Level 1 rigor, which focuses on architecture matching rather than the equation verification and shape checks required by Level 2.

What is the best way to prevent silent shape bugs when porting model architectures from papers?▼

The best way to prevent silent shape bugs when porting model architectures is enforcing explicit notation-to-code mapping and input/output shape checks before production training, ensuring verified implementation code.