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This skill should be used when the user asks to "analyze experimental results", "generate results section", "statistical analysis of experiments", "compare model performance", "create results visualization", or mentions connecting experime...
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npx skills add https://github.com/LigphiDonk/Oh-my--paper --skill inno-experiment-analysis命令会保持在同一行。复制前请横向滚动并检查完整内容。
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Search traceable academic papers, download legally accessible PDFs from arXiv and open-access sources, convert PDFs or page images to Markdown with a PaddleOCR layout-parsing API (or local pdfminer fallback), and organize the results into an AI-readable literature library. Use when Claude Code needs to build a paper corpus, batch OCR PDFs to Markdown, ingest real literature into a knowledge base, fetch arXiv or Hugging Face paper leads, or turn a directory of papers into structured Markdown plus metadata.
Delegate complex coding tasks to Claude Code CLI
Delegate coding tasks to OpenAI Codex CLI
| id | inno-experiment-analysis |
| name | inno-experiment-analysis |
| version | 0.1.0 |
| description | This skill should be used when the user asks to "analyze experimental results", "generate results section", "statistical analysis of experiments", "compare model performance", "create results visualization", or mentions connecting experime... |
| stages | ["experiment"] |
| tools | ["read_file","search_project","write_file"] |
| summary | This skill should be used when the user asks to "analyze experimental results", "generate results section", "statistical analysis of experiments", "compare model performance", "create results visualization", or mentions connecting experime... |
| primaryIntent | evaluation |
| intents | ["evaluation","experiment"] |
| capabilities | ["evaluation-benchmarking"] |
| domains | ["general"] |
| keywords | ["research","analysis","statistics","visualization","paper writing","inno-experiment-analysis","evaluation-benchmarking","inno","experiment","this","should","be"] |
| source | builtin |
| status | verified |
| upstream | {"repo":"dr-claw","path":"skills/inno-experiment-analysis","revision":"8322dc4ef575affaa374aa7922c0a0971c6db7d7"} |
| resourceFlags | {"hasReferences":true,"hasScripts":false,"hasTemplates":false,"hasAssets":false,"referenceCount":4,"scriptCount":0,"templateCount":0,"assetCount":0,"optionalScripts":false} |
This skill should be used when the user asks to "analyze experimental results", "generate results section", "statistical analysis of experiments", "compare model performance", "create results visualization", or mentions connecting experime...
Use this skill when the user request matches its research workflow scope. Prefer the bundled resources instead of recreating templates or reference material. Keep outputs traceable to project files, citations, scripts, or upstream evidence.
references/ only when the current task needs the extra detail.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 ml-paper-writing skill:
inno-experiment-analysis handles:
ml-paper-writing handles:
Workflow Integration:
Experiments complete → inno-experiment-analysis analyzes
↓
Generate analysis report and visualizations
↓
ml-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.