iminuit-statistical-fitter
iminuit statistical fitting skill for physics data analysis with proper error handling and profile likelihood
Best use case
iminuit-statistical-fitter is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
iminuit statistical fitting skill for physics data analysis with proper error handling and profile likelihood
Teams using iminuit-statistical-fitter 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/iminuit-statistical-fitter/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How iminuit-statistical-fitter Compares
| Feature / Agent | iminuit-statistical-fitter | 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?
iminuit statistical fitting skill for physics data analysis with proper error handling and profile likelihood
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
# iminuit Statistical Fitter ## Purpose Provides expert guidance on iminuit for statistical fitting in physics, including proper error estimation and profile likelihood calculations. ## Capabilities - MINUIT minimization algorithms - HESSE error matrix calculation - MINOS asymmetric error estimation - Profile likelihood computation - Constrained fitting - Simultaneous fit orchestration ## Usage Guidelines 1. **Model Definition**: Define cost function for minimization 2. **Minimization**: Run MIGRAD for parameter estimation 3. **Error Analysis**: Use HESSE and MINOS for uncertainties 4. **Profile Likelihood**: Compute profile likelihood for parameters 5. **Simultaneous Fits**: Combine multiple datasets in fits ## Tools/Libraries - iminuit - probfit - zfit
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