Best use case
robust-statistics-toolkit is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Robust statistical methods resistant to outliers
Teams using robust-statistics-toolkit 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/robust-statistics-toolkit/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How robust-statistics-toolkit Compares
| Feature / Agent | robust-statistics-toolkit | 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?
Robust statistical methods resistant to outliers
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
# Robust Statistics Toolkit ## Purpose Provides robust statistical methods resistant to outliers and model violations for reliable inference. ## Capabilities - M-estimators (Huber, Tukey) - Trimmed and winsorized estimators - Robust regression (MM-estimation) - Breakdown point analysis - Influence function computation - Robust covariance estimation ## Usage Guidelines 1. **Outlier Detection**: Identify potential outliers first 2. **Estimator Selection**: Choose based on expected contamination 3. **Breakdown Point**: Consider required breakdown point 4. **Efficiency**: Balance robustness and efficiency ## Tools/Libraries - robustbase (R) - scikit-learn - statsmodels
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