aeon

21.8k

davila7

This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.

aeon

10.8k

K-Dense-AI

This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.

mechanic

1.8k

openclaw

Vehicle maintenance tracker and mechanic advisor. Tracks mileage, service intervals, fuel economy, costs, warranties, and recalls. Researches manufacturer schedules, estimates costs, projects service dates, tracks providers, and proactively reminds about upcoming/overdue services. Supports VIN decode and auto-population of vehicle specs, NHTSA recall monitoring, MPG tracking with anomaly detection, warranty expiration alerts, pre-trip/seasonal checklists, mileage projection, service provider history, tax deduction integration, emergency info cards, and cost-per-mile analysis. Use when discussing vehicle maintenance, oil changes, service intervals, mileage tracking, fuel economy, warranties, recalls, RV maintenance, roof sealing, generator service, slide-outs, winterization, or anything mechanic-related. Supports any vehicle type including trucks, cars, motorcycles, dirt bikes, ATVs, RVs, and boats.

azure-kusto

1.6k

microsoft

Query and analyze data in Azure Data Explorer (Kusto/ADX) using KQL for log analytics, telemetry, and time series analysis. USE FOR: KQL queries, Kusto database queries, Azure Data Explorer, ADX clusters, log analytics, time series data, IoT telemetry, anomaly detection DO NOT USE FOR: SQL databases (use azure-postgres), NoSQL queries (use azure-storage), Elasticsearch, AWS analytics tools

azure-ai-anomalydetector-java

1.6k

microsoft

Build anomaly detection applications with Azure AI Anomaly Detector SDK for Java. Use when implementing univariate/multivariate anomaly detection, time-series analysis, or AI-powered monitoring.

azure-ai-anomalydetector-java

1.6k

Microsoft Agent Skills Azure Ai Anomalydetector Java

Build anomaly detection applications with Azure AI Anomaly Detector SDK for Java. Use when implementing univariate/multivariate anomaly detection, time-series analysis, or AI-powered monitoring.

detecting-data-anomalies

1.5k

Jeremylongshore Claude Code Plugins Plus Skills Anomaly Detection System

This skill empowers Claude to identify anomalies and outliers within datasets. It leverages the anomaly-detection-system plugin to analyze data, apply appropriate machine learning algorithms, and highlight unusual data points. Use this skill when the user requests anomaly detection, outlier analysis, or identification of unusual patterns in data. Trigger this skill when the user mentions "anomaly detection," "outlier analysis," "unusual data," or requests insights into data irregularities.

anomaly-detection

756

dadbodgeoff

Rule-based anomaly detection for production systems with configurable thresholds, cooldown periods to prevent alert storms, and error pattern tracking for repeated failures.

whylabs-monitor

376

A5c Ai Babysitter Whylabs Monitor

WhyLabs integration skill for ML observability, profile logging, and anomaly detection.

azure-kusto

134

microsoft

Query and analyze data in Azure Data Explorer (Kusto/ADX) using KQL for log analytics, telemetry, and time series analysis. USE FOR: KQL queries, Kusto database queries, Azure Data Explorer, ADX clusters, log analytics, time series data, IoT telemetry, anomaly detection DO NOT USE FOR: SQL databases (use azure-postgres), NoSQL queries (use azure-storage), Elasticsearch, AWS analytics tools

aws-cloudformation-cloudwatch

126

giuseppe-trisciuoglio

Provides AWS CloudFormation patterns for CloudWatch monitoring, metrics, alarms, dashboards, logs, and observability. Use when creating CloudWatch metrics, alarms, dashboards, log groups, log subscriptions, anomaly detection, synthesized canaries, Application Signals, and implementing template structure with Parameters, Outputs, Mappings, Conditions, cross-stack references, and CloudWatch best practices for monitoring production infrastructure.

metrics-dashboard

122

LerianStudio

KPI and metrics dashboard workflow covering metric definition, data sourcing, visualization design, and anomaly detection. Delivers actionable dashboards.

Anomaly Detection

95

aj-geddes

Identify unusual patterns, outliers, and anomalies in data using statistical methods, isolation forests, and autoencoders for fraud detection and quality monitoring

error-detective

36

zenobi-us

Expert error detective specializing in complex error pattern analysis, correlation, and root cause discovery. Masters distributed system debugging, error tracking, and anomaly detection with focus on finding hidden connections and preventing error cascades.

performance-monitor

36

zenobi-us

Expert performance monitor specializing in system-wide metrics collection, analysis, and optimization. Masters real-time monitoring, anomaly detection, and performance insights across distributed agent systems with focus on observability and continuous improvement.

aeon

26

lifangda

Time series machine learning toolkit for classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use this skill when working with temporal data, performing time series analysis, building predictive models on sequential data, or implementing workflows that involve distance metrics (DTW), transformations (ROCKET, Catch22), or deep learning for time series. Applicable for tasks like ECG classification, stock price forecasting, sensor anomaly detection, or activity recognition from wearable devices.

clustering-analyzer

18

dkyazzentwatwa

Cluster data using K-Means, DBSCAN, and hierarchical clustering. Use it for customer segmentation, pattern discovery, anomaly detection, or general data grouping.

forensic-data-engineer

17

daffy0208

Expert in data forensics, anomaly detection, audit trail analysis, fraud detection, and breach investigation

aeon

15

oimiragieo

This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.

Observability with Prometheus & Grafana

10

bobmatnyc

Production-grade observability stack with Prometheus metrics, Grafana dashboards, PromQL query language, alerting rules, and AI-powered anomaly detection for modern cloud-native applications

aeon

9

jackspace

This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.

aeon

7

andikarachman

Aeon API patterns for time series machine learning -- classification, regression, clustering, anomaly detection, segmentation, and similarity search. Use when /ds:experiment needs time-series-specific ML algorithms (ROCKET, InceptionTime, DTW classifiers), or /ds:eda needs temporal feature extraction (Catch22, ROCKET features) or change point detection. For classical statistical forecasting (ARIMA/SARIMAX) use statsmodels; for tabular ML pipelines use scikit-learn; for visualization use matplotlib.

time-series

5

pluginagentmarketplace

ARIMA, SARIMA, Prophet, trend analysis, seasonality detection, anomaly detection, and forecasting methods. Use for time-based predictions, demand forecasting, or temporal pattern analysis.

azure-ai-anomalydetector-java

4

ngxtm

Build anomaly detection applications with Azure AI Anomaly Detector SDK for Java. Use when implementing univariate/multivariate anomaly detection, time-series analysis, or AI-powered monitoring.

monitor-data-integrity

2

pjt222

Design and operate a data integrity monitoring programme based on ALCOA+ principles. Covers detective controls, audit trail review schedules, anomaly detection patterns (off-hours activity, sequential modifications, bulk changes), metrics dashboards, investigation triggers, and escalation matrix definition. Use when establishing a data integrity monitoring programme for GxP systems, preparing for inspections where data integrity is a focus area, after a data integrity incident requiring enhanced monitoring, or when implementing MHRA, WHO, or PIC/S guidance.

implementing-ot-network-traffic-analysis-with-nozomi

2

mukul975

Deploy Nozomi Networks Guardian sensors for passive OT network traffic analysis to achieve comprehensive asset visibility, real-time threat detection, and vulnerability assessment across industrial control systems without disrupting operations, leveraging behavioral anomaly detection and protocol-aware monitoring.

detecting-stuxnet-style-attacks

2

mukul975

This skill covers detecting sophisticated cyber-physical attacks that follow the Stuxnet attack pattern of modifying PLC logic while spoofing sensor readings to hide the manipulation from operators. It addresses PLC logic integrity monitoring, physics-based process anomaly detection, engineering workstation compromise indicators, USB-borne attack vectors, and multi-stage attack chain detection spanning IT-to-OT lateral movement through to process manipulation.

detect-anomalies-aiops

2

pjt222

Implement AI-powered anomaly detection for operational metrics using time series analysis (Isolation Forest, Prophet, LSTM), alert correlation, and root cause analysis. Reduce alert fatigue by intelligently identifying true anomalies in system metrics, logs, and traces. Use when operations teams are overwhelmed by alert volume, when detecting complex multi-metric anomalies beyond static thresholds, when seasonal patterns make thresholds ineffective, or when needing to predict issues proactively before they impact users.

detecting-attacks-on-scada-systems

2

mukul975

This skill covers detecting cyber attacks targeting Supervisory Control and Data Acquisition (SCADA) systems including man-in-the-middle attacks on industrial protocols, unauthorized command injection into PLCs, HMI compromise, historian data manipulation, and denial-of-service against control system communications. It leverages OT-specific intrusion detection systems, industrial protocol anomaly detection, and process data analytics to identify attacks that traditional IT security tools miss.

detect-anomalies-aiops

2

pjt222

Implement AI-powered anomaly detection for operational metrics using time series analysis (Isolation Forest, Prophet, LSTM), alert correlation, and root cause analysis. Reduce alert fatigue by intelligently identifying true anomalies in system metrics, logs, and traces. Use when operations teams are overwhelmed by alert volume, when detecting complex multi-metric anomalies beyond static thresholds, when seasonal patterns make thresholds ineffective, or when needing to predict issues proactively before they impact users.

monitor-data-integrity

2

pjt222

Design and operate a data integrity monitoring programme based on ALCOA+ principles. Covers detective controls, audit trail review schedules, anomaly detection patterns (off-hours activity, sequential modifications, bulk changes), metrics dashboards, investigation triggers, and escalation matrix definition. Use when establishing a data integrity monitoring programme for GxP systems, preparing for inspections where data integrity is a focus area, after a data integrity incident requiring enhanced monitoring, or when implementing MHRA, WHO, or PIC/S guidance.

analyzing-security-logs-with-splunk

2

mukul975

Leverages Splunk Enterprise Security and SPL (Search Processing Language) to investigate security incidents through log correlation, timeline reconstruction, and anomaly detection. Covers Windows event logs, firewall logs, proxy logs, and authentication data analysis. Activates for requests involving Splunk investigation, SPL queries, SIEM log analysis, security event correlation, or log-based incident investigation.

detecting-anomalies-in-industrial-control-systems

2

mukul975

This skill covers deploying anomaly detection systems for industrial control environments using machine learning models trained on OT network baselines, physics-based process models, and behavioral analysis of industrial protocol communications. It addresses building normal behavior profiles for SCADA polling patterns, detecting deviations in Modbus/DNP3/OPC UA traffic, identifying rogue devices, and correlating network anomalies with physical process data from historians.

detecting-anomalous-authentication-patterns

2

mukul975

Detects anomalous authentication patterns using UEBA analytics, statistical baselines, and machine learning models to identify impossible travel, credential stuffing, brute force, password spraying, and compromised account behaviors across authentication logs. Activates for requests involving authentication anomaly detection, login behavior analysis, UEBA implementation, or suspicious sign-in investigation.

aeon

1

iamseungpil

This skill is intended for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use it when working with temporal or sequential data or time-indexed observations that require specialized algorithms beyond standard ML approaches. It is particularly well suited for univariate and multivariate time series analysis and exposes scikit-learn–compatible APIs.

weekly-report

1

adityawrk

Generate recurring weekly or monthly analytics reports with period-over-period comparison, anomaly detection, and executive summaries. Use when the user asks for a weekly report, monthly KPI review, recurring metrics snapshot, or needs automated period-over-period diffing. Saves templates for one-command re-runs.

azure-ai-anomalydetector-java

1

rootcastleco

Build anomaly detection applications with Azure AI Anomaly Detector SDK for Java. Use when implementing univariate/multivariate anomaly detection, time-series analysis, or AI-powered monitoring.

Azure AI Anomaly Detector SDK for Java

oki3505F

Build anomaly detection applications with the Azure AI Anomaly Detector SDK for Java. Use it when implementing univariate or multivariate anomaly detection, time-series analysis, or AI-powered monitoring.

observational-guards

littlebearapps

Anomaly detection, sliding window rate limiter, and D1 storage guard. These are observational by default — they enrich telemetry metadata without blocking requests. Load when configuring defence-in-depth monitoring for features with variable load patterns.

Medical Image Analyzer

Eli-yu-first

Analyzes medical images (X-ray, MRI, CT) with anomaly detection and measurement tools

security-monitoring-threat-detection

smkssmart1

Enterprise-grade skill for implementing 24/7 security monitoring and threat detection on OpenClaw AI agent infrastructure. Use whenever setting up logging, alerting, intrusion detection, SIEM integration, or real-time monitoring for AI agent systems. Also trigger for log analysis, anomaly detection, incident response procedures, SOC operations for AI infrastructure, security event correlation, or any continuous monitoring pattern for autonomous AI agent deployments.

azure-ai-anomalydetector-java

haniakrim21

Build anomaly detection applications with the Azure AI Anomaly Detector SDK for Java. Use it when implementing univariate or multivariate anomaly detection, time-series analysis, or AI-powered monitoring.

Anomaly Detection System

Eli-yu-first

Implements anomaly detection algorithms for time series, network traffic, and financial transactions

aeon

reikiplanet

This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.

Azure AI Anomaly Detector SDK for Java

reikiplanet

Build anomaly detection applications with the Azure AI Anomaly Detector SDK for Java. Use it when implementing univariate or multivariate anomaly detection, time-series analysis, or AI-powered monitoring.