Best use case
regression-analyzer is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Skill for comprehensive regression analysis and modeling
Teams using regression-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/regression-analyzer/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How regression-analyzer Compares
| Feature / Agent | regression-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?
Skill for comprehensive regression analysis and modeling
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
# Regression Analyzer Skill ## Purpose Perform comprehensive regression analyses including model fitting, diagnostics, and interpretation for scientific data. ## Capabilities - Fit linear models - Build generalized models - Perform diagnostics - Handle multicollinearity - Generate predictions - Interpret coefficients ## Usage Guidelines 1. Prepare data 2. Specify model 3. Fit regression 4. Run diagnostics 5. Refine if needed 6. Interpret results ## Process Integration Works within scientific discovery workflows for: - Relationship modeling - Prediction generation - Effect estimation - Variable selection ## Configuration - Model specifications - Diagnostic tests - Variable selection - Output formatting ## Output Artifacts - Model summaries - Diagnostic plots - Coefficient tables - Prediction outputs
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