| 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} |
inno-experiment-analysis
Canonical 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...
Trigger Rules
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.
Resource Use Rules
- Read from
references/ only when the current task needs the extra detail.
Execution Contract
- Resolve every relative path from this skill directory first.
- Prefer inspection before mutation when invoking bundled scripts.
- If a required runtime, CLI, credential, or API is unavailable, explain the blocker and continue with the best manual fallback instead of silently skipping the step.
- Do not write generated artifacts back into the skill directory; save them inside the active project workspace.
Upstream Instructions
Results Analysis for ML/AI Research
A systematic experimental results analysis workflow connecting experimental data to paper writing.
Core Features
This skill provides three core capabilities:
- Experimental Data Analysis - Read and analyze experimental data in various formats
- Statistical Validation - Perform statistical significance tests and performance comparisons
- Paper Content Generation - Generate text and visualizations for the Results section
When to Use
Use this skill when you need to:
- Analyze experimental results (CSV, JSON, TensorBoard logs)
- Generate the Results section of a paper
- Compare performance across multiple models
- Perform statistical significance tests
- Create publication-quality visualizations
- Validate the reliability of experimental results
Workflow
Standard Analysis Pipeline
Data Loading → Data Validation → Statistical Analysis → Visualization → Writing → Quality Check
Step 1: Data Loading and Validation
Supported Data Formats:
- CSV files - Tabular data
- JSON files - Structured results
- TensorBoard logs - Training curves
- Python pickle - Complex objects
