iminuit-statistical-fitter
iminuit statistical fitting skill for physics data analysis with proper error handling and profile likelihood
npx skills add a5c-ai/babysitter --skill iminuit-statistical-fitter --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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
- Purpose
- Capabilities
- Usage Guidelines
- Tools/Libraries
What does the iminuit-statistical-fitter skill do?
iminuit statistical fitting skill for physics data analysis with proper error handling and profile likelihood
How do I install it?
Run `npx skills add a5c-ai/babysitter --skill iminuit-statistical-fitter --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From a5c-ai/babysitter, a repository with 1,642 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.