statistics-skills
10 statistical analysis skills. Trigger: statistical tests, Bayesian analysis, hypothesis testing, sampling. Design: method guides covering assumptions, code, and result interpretation.
Best use case
statistics-skills is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
10 statistical analysis skills. Trigger: statistical tests, Bayesian analysis, hypothesis testing, sampling. Design: method guides covering assumptions, code, and result interpretation.
Teams using statistics-skills 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/statistics/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How statistics-skills Compares
| Feature / Agent | statistics-skills | 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?
10 statistical analysis skills. Trigger: statistical tests, Bayesian analysis, hypothesis testing, sampling. Design: method guides covering assumptions, code, and result interpretation.
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
# Statistical Analysis — 10 Skills Select the skill matching the user's need, then `read` its SKILL.md. | Skill | Description | |-------|-------------| | [bayesian-statistics-guide](./bayesian-statistics-guide/SKILL.md) | Bayesian inference methods including prior selection, MCMC, and model comparison | | [data-anomaly-detection](./data-anomaly-detection/SKILL.md) | Detect anomalies and outliers in research data using statistical methods | | [hypothesis-testing-guide](./hypothesis-testing-guide/SKILL.md) | Statistical hypothesis testing, power analysis, and significance reporting | | [meta-analysis-guide](./meta-analysis-guide/SKILL.md) | Conduct systematic meta-analyses with effect size pooling and heterogeneity | | [ml-experiment-tracker](./ml-experiment-tracker/SKILL.md) | Plan reproducible ML experiment runs with parameters and metrics tracking | | [modeling-strategy-guide](./modeling-strategy-guide/SKILL.md) | Strategic statistical modeling, experimentation, and causal inference | | [nonparametric-tests-guide](./nonparametric-tests-guide/SKILL.md) | Apply Mann-Whitney, Kruskal-Wallis, and other nonparametric methods | | [power-analysis-guide](./power-analysis-guide/SKILL.md) | Sample size calculation and statistical power analysis guide | | [sem-guide](./sem-guide/SKILL.md) | Structural equation modeling with latent variables guide | | [survival-analysis-guide](./survival-analysis-guide/SKILL.md) | Conduct Kaplan-Meier, Cox regression, and time-to-event analyses |
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