inno-experiment-analysis

Analyze experimental results and generate a publication-ready Results section.

Updated Apr 1, 2026

Search Tags:#analysis #visualization #statistics #reproducibility #experiments #results #paper-writing

Ready to run

npx skills add https://github.com/xiaoyu-ops/the_work_of_dedup --skill inno-experiment-analysis-xiaoyu-ops

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: inno-experiment-analysis
Source: https://github.com/xiaoyu-ops/the_work_of_dedup/tree/main/.claude/skills/inno-experiment-analysis
Command: npx skills add https://github.com/xiaoyu-ops/the_work_of_dedup --skill inno-experiment-analysis-xiaoyu-ops

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This skill streamlines the process of analyzing experimental results, performing statistical validation, and drafting publication-ready Results sections for ML/AI research.

Core Features & Use Cases

Experimental Data Analysis: Read results from CSV/JSON/TensorBoard logs and compute key statistics.
Statistical Validation: Perform normality tests, variance checks, t-tests/ANOVA, and report effect sizes with multiple-comparison corrections.
Results Drafting: Generate analysis reports and a Results section draft that integrates tables, figures, and narratives for papers.
Workflow Integration: Works with the paper-writing workflow, enabling seamless handoff to the ml-paper-writing skill.
Use Case: Researchers analyze a multi-model experiment to quantify performance differences, validate significance, and prepare figures and text for the Results section.

Quick Start

Provide the path to your experiment results to trigger analysis and generate an analysis-report.md and results-draft.md.

Frequently Asked Questions about inno-experiment-analysis

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

FAQPage Schema

How do I analyze experimental results and write a Results section for my paper?▼

Analyze experimental results by loading CSV, JSON, or TensorBoard logs to compute statistics, run significance tests, and generate a publication-ready Results section draft with integrated figures and tables.

What statistical tests should I use to validate ML model performance differences?▼

To validate ML performance differences, use normality tests, variance checks, t-tests or ANOVA, and report effect sizes with multiple-comparison corrections to ensure rigorous statistical validation across models and datasets.

Can I generate publication-ready figures and tables directly from TensorBoard logs?▼

Yes, you can generate publication-ready figures and tables directly from TensorBoard logs by loading the standard log files, computing key statistics, and visualizing performance differences across multiple models and datasets.

How do I ensure reproducibility when analyzing multi-model experiments across different datasets?▼

Ensure reproducibility in multi-model experiment analysis by using pre-defined seeds, standard data formats like CSV or JSON, and a structured workflow that validates data and tracks statistical testing steps consistently.

Does this approach integrate with existing paper-writing workflows?▼

Yes, this approach integrates with existing paper-writing workflows by generating an analysis report and a Results section draft that can seamlessly hand off to dedicated ml-paper-writing skills for final publication assembly.

What is the best way to report effect sizes and multiple comparisons in an ML research paper?▼

The best way to report effect sizes and multiple comparisons in an ML research paper is to perform statistical tests like ANOVA, apply multiple-comparison corrections, and draft narrative text integrating these quantitative metrics into the Results section.