Skill

Detect Anomalies with Statistical and ML Rules

Name: Detect Anomalies with Statistical and ML Rules
Availability: OnlineOnly
Author: VibeBaza

Expert in developing and implementing anomaly detection rules using statistical methods and machine learning approaches.

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Works with github pandasnumpyscipystatsmodels

VibeBaza

Maintainer?

Spark score

out of 100

Updated 6 months ago

Version 1.0.0

Models

claude

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Why it matters

Implement robust anomaly detection systems by leveraging statistical methods and machine learning. This asset provides expert guidance and code for establishing baselines, analyzing distributions, and designing adaptive rules to minimize false positives and maximize sensitivity.

Outcomes

What it gets done

Establish statistical baselines for normal behavior using mean, median, and standard deviation.

Implement Z-score, IQR, and seasonal decomposition methods for anomaly detection.

Design cross-metric correlation rules and real-time streaming rules using EMA.

Configure multi-rule anomaly detection systems using YAML templates.

Install

Add it to your toolbox

Run in your project directory:

curl -fsSL https://spark.entire.vc/get/vb-anomaly-detection-rule | bash

Capabilities

What this skill does

Query a database

Writes and executes SQL or NoSQL queries on databases.

Extract

Pulls structured data fields from unstructured text.

Classify

Labels or categorizes text, files, or data points.

Summarize

Condenses long documents or threads into key takeaways.

Overview

Anomaly Detection Rule Expert Agent

What it does

Develop and implement anomaly detection rules with deep knowledge of statistical methods, machine learning approaches, and real-time monitoring systems. Build robust, scalable anomaly detection solutions that minimize false positives while maintaining high sensitivity to genuine anomalies. This agent can establish baselines using statistical measures, analyze data distributions, and incorporate temporal patterns and multivariate analysis. Rule design philosophy includes specificity over sensitivity, context awareness, adaptive thresholds, and confidence scoring.

For example, use Z-Score-based detection:

import numpy as np
import pandas as pd
from scipy import stats

def zscore_anomaly_detection(data, threshold=3, window=30):
    """
    Detect anomalies using rolling Z-score with adaptive baseline
    """
    rolling_mean = data.rolling(window=window, min_periods=10).mean()
    rolling_std = data.rolling(window=window, min_periods=10).std()
    
    z_scores = np.abs((data - rolling_mean) / rolling_std)
    anomalies = z_scores > threshold
    
    return {
        'anomalies': anomalies,
        'scores': z_scores,
        'baseline_mean': rolling_mean,
        'baseline_std': rolling_std
    }

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