メニュー
Use when analyzing experimental results, validating comparisons, generating paper-ready results text, or turning model-evaluation outputs into figures, tables, and defensible claims.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
Use when checking manuscript citations, bibliography hygiene, DOI or PMID completeness, placeholder references, or BibTeX consistency before submission or revision.
Use when drafting, auditing, or revising Data Availability statements, repository plans, accession-number placement, source-data coverage, or restricted-data wording for journal submission or resubmission.
Use when starting a new manuscript project or cleaning up an existing paper directory and you need a standard structure, active source files, project memory, and venue defaults before deeper writing begins.
Use when deciding which paper-related skill to use or how to sequence manuscript work from project setup through submission and rebuttal.
Use when responding to journal or conference reviewer comments and you need a structured author response, aligned manuscript edits, and clear decisions about when to clarify, add evidence, concede, or respectfully disagree.
Use when writing or revising scientific manuscripts, abstracts, figures, or references for journal submission and you need full-paragraph prose, scientific structure, citation-style guidance, or reporting-guideline support.
| name | results-analysis |
| description | Use when analyzing experimental results, validating comparisons, generating paper-ready results text, or turning model-evaluation outputs into figures, tables, and defensible claims. |
A systematic experimental results analysis workflow connecting experimental data to paper writing.
This skill provides three core capabilities:
Use this skill when you need to:
Data Loading → Data Validation → Statistical Analysis → Visualization → Writing → Quality Check
Supported Data Formats:
Data Validation Checks:
Select appropriate tools for data loading and preliminary validation based on data format.
Basic Statistics:
Significance Tests:
Select appropriate statistical tests based on data characteristics.
Key Principles:
See references/statistical-methods.md for the complete statistical methods guide.
Comparison Dimensions:
Comparison Methods:
Systematically compare performance across different methods, ensuring fair comparison.
Publication-Quality Visualization Requirements:
Common Chart Types:
Use appropriate visualization tools to generate publication-quality figures.
See references/visualization-best-practices.md for the visualization guide.
Results Section Structure:
## Results
### Overview of Main Findings
[1-2 paragraphs summarizing core results]
### Experimental Setup
[Brief description of experimental configuration; details in appendix]
### Performance Comparison
[Comparison with baseline methods, including tables and figures]
### Ablation Study
[Validate contributions of each component]
### Statistical Significance
[Report statistical test results]
### Qualitative Analysis
[Case studies, visualization examples]
Writing Principles:
See references/results-writing-guide.md for the complete writing guide.
Checklist:
❌ Wrong approach:
✅ Correct approach:
❌ Wrong approach:
✅ Correct approach:
❌ Wrong approach:
✅ Correct approach:
See references/common-pitfalls.md for the complete error patterns and fixes.
This skill focuses on experimental results analysis and works in tandem with the writing skills in this repository:
results-analysis handles:
scientific-writing or conference-paper-writing handle:
Workflow Integration:
Experiments complete → results-analysis analyzes
↓
Generate analysis report and visualizations
↓
scientific-writing or conference-paper-writing integrates into paper
↓
Complete Results section
After analysis, the following are generated:
Analysis Report (analysis-report.md)
Visualization Files (figures/)
Results Draft (results-draft.md)
Refer to the examples/ directory for complete examples:
example-analysis-report.md - Complete analysis report exampleexample-results-section.md - Paper Results section exampleThe complete analysis pipeline includes:
See the guides in the references/ directory for detailed methods and best practices.
references/statistical-methods.md - Complete statistical methods guidereferences/results-writing-guide.md - Results section writing standardsreferences/visualization-best-practices.md - Visualization best practicesreferences/common-pitfalls.md - Common errors and fixes✅ Recommended:
❌ Prohibited:
✅ Recommended:
❌ Prohibited:
✅ Recommended:
❌ Prohibited:
This skill provides a systematic experimental results analysis workflow:
Following these principles produces high-quality, reproducible experimental results analysis that meets top conference standards.