root-data-analyzer
ROOT/CERN data analysis skill for high-energy physics data processing, histogramming, and statistical analysis
Best use case
root-data-analyzer is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
ROOT/CERN data analysis skill for high-energy physics data processing, histogramming, and statistical analysis
Teams using root-data-analyzer should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/root-data-analyzer/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How root-data-analyzer Compares
| Feature / Agent | root-data-analyzer | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
ROOT/CERN data analysis skill for high-energy physics data processing, histogramming, and statistical analysis
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# ROOT Data Analyzer ## Purpose Provides expert guidance on ROOT data analysis for high-energy physics, including TTree manipulation, histogram fitting, and statistical modeling with RooFit. ## Capabilities - TTree/TChain manipulation - Histogram creation and fitting - RooFit statistical modeling - TCanvas visualization - ROOT macro development - PyROOT integration ## Usage Guidelines 1. **Data Access**: Use TTree and TChain for efficient data access 2. **Histogramming**: Create and fill histograms with proper binning 3. **Fitting**: Use RooFit for advanced statistical modeling 4. **Visualization**: Create publication-quality plots with TCanvas 5. **Python Integration**: Use PyROOT for Python-based analysis ## Tools/Libraries - ROOT - RooFit - RooStats - uproot
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