Marketplace

detecting-data-anomalies

Process identify anomalies and outliers in datasets using machine learning algorithms. Use when analyzing data for unusual patterns, outliers, or unexpected deviations from normal behavior. Trigger with phrases like "detect anomalies", "find outliers", or "identify unusual patterns".

allowed_tools: Read, Bash(python:*), Grep, Glob

$ 安裝

git clone https://github.com/jeremylongshore/claude-code-plugins-plus-skills /tmp/claude-code-plugins-plus-skills && cp -r /tmp/claude-code-plugins-plus-skills/plugins/ai-ml/anomaly-detection-system/skills/detecting-data-anomalies ~/.claude/skills/claude-code-plugins-plus-skills

// tip: Run this command in your terminal to install the skill

SKILL.md

View on GitHub →

name: detecting-data-anomalies description: | Process identify anomalies and outliers in datasets using machine learning algorithms. Use when analyzing data for unusual patterns, outliers, or unexpected deviations from normal behavior. Trigger with phrases like "detect anomalies", "find outliers", or "identify unusual patterns".

allowed-tools: Read, Bash(python:*), Grep, Glob version: 1.0.0 author: Jeremy Longshore jeremy@intentsolutions.io license: MIT

Detecting Data Anomalies

Overview

This skill provides automated assistance for the described functionality.

Prerequisites

Before using this skill, ensure you have:

Dataset in accessible format (CSV, JSON, or database)
Python environment with scikit-learn or similar ML libraries
Understanding of data distribution and expected patterns
Sufficient data volume for statistical significance
Knowledge of domain-specific normal behavior
Data preprocessing capabilities for cleaning and scaling

Instructions

Load dataset using Read tool
Inspect data structure and identify relevant features
Clean data by handling missing values and inconsistencies
Normalize or scale features as appropriate for algorithm
Split temporal data if time-series analysis is needed
Apply selected algorithm using Bash tool
Generate anomaly scores for each data point
Classify points as normal or anomalous based on threshold
Extract characteristics of identified anomalies

See {baseDir}/references/implementation.md for detailed implementation guide.

Output

Total data points analyzed
Number of anomalies detected
Contamination rate (percentage of anomalies)
Algorithm used and configuration parameters
Confidence scores for detected anomalies
Record identifier and timestamp (if applicable)

Error Handling

See {baseDir}/references/errors.md for comprehensive error handling.

Examples

See {baseDir}/references/examples.md for detailed examples.

Resources

Isolation Forest documentation and implementation examples
One-Class SVM for novelty detection
Local Outlier Factor (LOF) for density-based detection
Autoencoder-based anomaly detection for deep learning approaches
scikit-learn anomaly detection module