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experiment-code

Generate ML experiment code with reproducible PyTorch or scikit-learn workflows.

Updated Apr 23, 2026
Search Tags:#debugging#research#plotting#reproducibility#code-generation#experiment#ml
One-click install
npx skills add https://github.com/Embers-of-the-Fire/agent-research-skills-opencode --skill experiment-code
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: experiment-code
Source: https://github.com/Embers-of-the-Fire/agent-research-skills-opencode/tree/main/.opencode/skills/experiment-code
Command: npx skills add https://github.com/Embers-of-the-Fire/agent-research-skills-opencode --skill experiment-code

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

ML researchers often spend excessive time writing boilerplate code for experiments, debugging, and iterating toward publishable results. This Skill automates and structures the process to accelerate reproducible research.

Core Features & Use Cases

  • Generate and iteratively improve ML experiment code with reproducible run configurations.
  • Build end-to-end training and evaluation pipelines, with logging and artifact storage.
  • Debug errors and reflect on results to guide subsequent iterations for publication-quality experiments.

Quick Start

Request a complete ML experiment codebase that follows a planned experimental design and produces runnable results, plots, and notes.

Frequently Asked Questions about experiment-code

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

FAQPage Schema
How do I automate ML experiment code generation for research papers?▼

Automating ML experiment code generation involves planning an experimental design and producing a self-contained project layout with run artifacts, logging, and plotting outputs. This ensures reproducible research workflows across multiple iterative runs.

What is the best way to structure ML experiments for reproducibility?▼

Structuring ML experiments for reproducibility requires a self-contained project layout with clear run artifacts, logging, and plotting outputs. This approach tracks configuration changes and enables result reflection across multiple runs.

How do I iteratively debug and improve PyTorch training pipelines?▼

Iteratively debugging and improving PyTorch training pipelines requires executing end-to-end workflows, capturing logging data, and reflecting on previous run results to guide subsequent code improvements for publication-quality experiments.

Does this workflow support both PyTorch and scikit-learn pipelines?▼

Yes, the workflow ensures compatibility with both PyTorch and scikit-learn pipelines. It generates end-to-end training and evaluation code tailored to these frameworks, maintaining consistent artifact storage and reproducible run configurations.

How do I generate end-to-end evaluation pipelines with plotting outputs?▼

Generating end-to-end evaluation pipelines with plotting outputs involves building a self-contained project layout that automates training execution, logs run metrics, and stores visual artifacts to guide subsequent experimental iterations.

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