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ChuaHanChong
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research

Analyze web search results and academic papers to propose ranked ML optimization techniques.

Updated Mar 5, 2026
Search Tags:#research#optimization#machine learning#literature review#hyperparameter tuning#model improvement
One-click install
npx skills add https://github.com/ChuaHanChong/ml-optimizer --skill research-chuahanchong
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: research
Source: https://github.com/ChuaHanChong/ml-optimizer/tree/main/skills/research
Command: npx skills add https://github.com/ChuaHanChong/ml-optimizer --skill research-chuahanchong

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill helps you find and evaluate new techniques to improve your Machine Learning models by researching existing literature and knowledge bases.

Core Features & Use Cases

  • Automated Literature Review: Scans web search results and user-provided papers for relevant ML optimization methods.
  • Actionable Proposals: Generates detailed, ranked proposals with implementation steps, expected impact, and complexity.
  • Use Case: You're training a diffusion model for image restoration and it's not performing as well as expected. This Skill can research new loss functions, architectural improvements, or training strategies to boost performance.

Quick Start

Use the research skill to find new techniques for improving a transformer model for text classification.

Frequently Asked Questions about research

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

FAQPage Schema
How do I find new machine learning optimization techniques for my model?▼

To find new machine learning optimization techniques, you can use an automated literature review tool that scans academic papers and web search results to propose actionable model improvements.

What is the best way to evaluate architectural improvements for a diffusion model?▼

The best way to evaluate architectural improvements for a diffusion model is to generate ranked proposals that include implementation steps, expected impact, and complexity ratings based on existing academic literature.

Can I research hyperparameter tuning strategies from academic papers?▼

Yes, you can research hyperparameter tuning strategies by analyzing academic papers and internal knowledge bases to extract actionable training strategies with detailed implementation steps.

Does this approach work for transformer models used in text classification?▼

Yes, this approach works for transformer models in text classification by researching and proposing optimization techniques tailored to various model types and specific machine learning tasks.

How are proposed model improvement methods prioritized?▼

Proposed model improvement methods are prioritized using a calculated score that evaluates the expected impact and implementation complexity of each technique extracted from the literature review.

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