train

Run training experiments from recipe JSON files and store results.

2|Updated Mar 19, 2026
One-click install
npx skills add https://github.com/chenghaoYang/auto-coder-trainer --skill train-chenghaoyang
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: train
Source: https://github.com/chenghaoYang/auto-coder-trainer/tree/main/skills/train
Command: npx skills add https://github.com/chenghaoYang/auto-coder-trainer --skill train-chenghaoyang

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill provides a structured way to execute training experiments from recipes, ensuring reproducibility and trackable results.

Core Features & Use Cases

  • Recipe-driven training: Validate and run ML training configurations defined in recipes.
  • End-to-end pipeline: Load, compile, prepare, train, evaluate, judge, store, and report results.
  • Use Case: Use when you want to compare model trainings across configurations and reproduce experiments.

Quick Start

Run a training experiment by pointing to a recipe JSON file.

Frequently Asked Questions about train

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

FAQPage Schema
How do I run reproducible ML training experiments from a recipe file?

To run reproducible ML training experiments from a recipe file, point the training pipeline to your recipe JSON. The skill validates configurations, seeds random number generators, and executes training backends to ensure trackable results.

What is recipe-driven training for machine learning models?

Recipe-driven training validates and executes machine learning configurations defined in recipe files. This approach provides a structured way to load, compile, prepare, train, evaluate, and store models while ensuring experiment reproducibility.

How do I compare model baselines across different training configurations?

You can compare model baselines across different training configurations by running multiple recipe files through the pipeline. The skill loads, trains, evaluates, and stores results for each configuration, enabling direct comparison of trained models.

Do I need specific training backends to execute machine learning recipes?

Yes, executing machine learning recipes requires compatible training backends. The skill orchestrates these backends during the end-to-end pipeline to compile, prepare, and train models while seeding random number generators for reproducibility.

Can I reproduce machine learning experiments across different datasets?

You can reproduce machine learning experiments across different datasets by applying the same validated recipe configurations. The skill ensures reproducibility by seeding random number generators and executing training backends consistently.

What steps are included in an end-to-end machine learning training pipeline?

An end-to-end machine learning training pipeline includes loading, compiling, preparing, training, evaluating, judging, storing, and reporting results. The skill automates these steps sequentially based on your input recipe JSON file.