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rishikanthc
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ml-code-standards

Enforce reproducibility and coding standards for machine learning codebases.

Updated Feb 23, 2026
Search Tags:#reproducibility#ml#mlflow#run-management#code-standards#experiment-tracking#hydra
One-click install
npx skills add https://github.com/rishikanthc/ml-superpowers --skill ml-code-standards
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ml-code-standards
Source: https://github.com/rishikanthc/ml-superpowers/tree/main/skills/ml-code-standards
Command: npx skills add https://github.com/rishikanthc/ml-superpowers --skill ml-code-standards

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Enforces reproducibility, consistent configuration, and opinionated coding standards so machine learning experiments remain traceable, debuggable, and repeatable from day one rather than becoming ad hoc and unreplicable later.

Core Features & Use Cases

  • Shape documentation: Requires inline tensor shape comments and concise math-oriented variable naming to make debugging and reviews faster.
  • Hydra-first configuration: Mandates Hydra YAML configs and override patterns for consistent experiment parameterization and composition.
  • Reproducible run infrastructure: Defines per-run directories with frozen config snapshots, logs, PIDs, and checkpointing plus guidance for MLflow integration and background training scripts.
  • Use case: During code review or onboarding, apply these standards to immediately surface missing configs, absent shape annotations, or missing run-management scripts and produce a prioritized remediation plan.

Quick Start

Ask the assistant to audit the repository for reproducibility gaps including missing Hydra configs, absent shape comments, no per-run snapshots, and missing run-management scripts and return a prioritized list of fixes.

Frequently Asked Questions about ml-code-standards

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

FAQPage Schema
How do I make machine learning experiments reproducible from the start?▼

To make ML experiments reproducible, enforce consistent configuration and coding standards early. This involves using Hydra for parameter management, tracking runs with MLflow, and maintaining per-run directories with frozen config snapshots and logs.

What are the best coding standards for ML training scripts?▼

The best coding standards for ML scripts mandate inline tensor shape comments and concise math-oriented variable naming. These standards accelerate debugging and code reviews by making tensor transformations and data flow immediately obvious.

How do I use Hydra config overrides for experiment tracking?▼

Hydra config overrides for experiment tracking work by composing YAML files and applying command-line overrides to parameterize training runs. The resulting configuration is frozen into a config.yaml snapshot within a dedicated run directory for traceability.

Does MLflow work with background training scripts for run management?▼

MLflow works seamlessly with background training scripts by logging metrics and artifacts during execution. Standard run management scripts organize these background processes by saving PIDs, logs, and checkpoints in structured per-run directories.

How do I audit my ML codebase for reproducibility gaps?▼

Auditing an ML codebase for reproducibility gaps involves checking for missing Hydra configurations, absent inline shape comments, and lacking per-run snapshot directories. The audit produces a prioritized remediation plan to fix run management and tracking issues.

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