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statistical-analysis

Compute descriptive statistics, trend signals, and anomaly indicators for numeric datasets.

Updated Apr 25, 2026
Search Tags:#cybersecurity#data-quality#anomaly-detection#hypothesis-testing#statistical-analysis#trend-analysis#descriptive-statistics
One-click install
npx skills add https://github.com/nmoralescyber/claude-skill-optimization --skill statistical-analysis-nmoralescyber
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: statistical-analysis
Source: https://github.com/nmoralescyber/claude-skill-optimization/tree/main/skills/data/statistical-analysis
Command: npx skills add https://github.com/nmoralescyber/claude-skill-optimization --skill statistical-analysis-nmoralescyber

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Provides statistical analysis capabilities for numeric data, enabling identification of trends, anomalies, and significance beyond simple averages.

Core Features & Use Cases

  • Descriptive statistics (mean, median, mode, variance, standard deviation) with per-series context.
  • Trend and relationship analysis (linear regression, seasonality, correlation, non-parametric options).
  • Anomaly and outlier detection (IQR, z-score, time-series anomalies) and hypothesis testing options.
  • Cybersecurity-ready baselines (per-user or per-geo baseline, anomaly scoring, alert-ready summaries).
  • Use case: Data-driven decision support for product metrics and operational dashboards.

Quick Start

Analyze the latest numeric dataset and report descriptive statistics, trends, and anomalies.

Frequently Asked Questions about statistical-analysis

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I detect anomalies and outliers in numeric time-series data?▼

Anomaly detection in numeric time-series data uses IQR, z-score, and time-series methods to flag outliers, providing alert-ready summaries with per-category baselines and uncertainty estimates.

What descriptive statistics can I generate for per-category analytics dashboards?▼

Descriptive statistics for per-category analytics include mean, median, mode, variance, and standard deviation, calculated with per-series context and uncertainty estimates for dashboard reporting.

Can I use hypothesis testing and check data normality on cybersecurity baselines?▼

Hypothesis testing and data normality checks apply to cybersecurity baselines by providing p-values, effect sizes, and per-user or per-geo anomaly scoring for alert-ready summaries.

What is the best way to identify trend signals and seasonality in a numeric dataset?▼

Identifying trend signals and seasonality in a numeric dataset involves applying linear regression, correlation, and non-parametric options to report trend signals and relationships with uncertainty estimates.

Does statistical analysis report effect sizes, p-values, and sample size limitations?▼

Statistical analysis reports sample size n, effect sizes, and p-values when applicable, and clearly states limitations while applying robust error handling for numeric data processing.

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