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An experiment-driven AI research skill for literature reviews, research gap analysis, reproducible ML experiments, statistical evaluation, and evidence-based scientific writing.

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Experiment-Driven Research Skill

A skill for literature-grounded research, reproducible experiments, statistical evaluation, and evidence-based IEEE Access/Q1 manuscript preparation.

What it is designed to do

  • Structure a research question, literature review, and gap analysis.
  • Plan mathematically grounded methods and reproducible experiments.
  • Track datasets, baselines, configurations, metrics, and raw outputs.
  • Analyze results and limitations without inventing evidence.
  • Organize a manuscript for IEEE Access or another specified Q1 journal.

Important operating rule

This skill provides instructions, not guaranteed compute, data access, subscriptions, or experimental results. It requires completed experiments to be distinguished from proposed work, citations to come from verifiable sources, failed runs and limitations to be reported, and citations, measurements, and claims never to be fabricated.

Contents

  • SKILL.md — the skill metadata and full research workflow instructions.

About

An experiment-driven AI research skill for literature reviews, research gap analysis, reproducible ML experiments, statistical evaluation, and evidence-based scientific writing.

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