Skills azure-ai-anomalydetector-java

📦

azure-ai-anomalydetector-java

Name: azure-ai-anomalydetector-java
Author: sickn33

Content revision r2 Safe ⚙️ External commands🌐 Network access🔑 Env variables

Build Java Anomaly Detection with Azure AI

Time-series monitoring requires reliable patterns for detecting unusual behavior. This skill provides Java examples for Azure AI client setup, analysis, training, inference, and model management.

Supports: Claude Codex Code(CC)

🥉 79 Bronze

Install with my Agent

Copy this request to your Agent. It includes the canonical Skill page and manifest.

Agent request

Review the Skillstore skill "azure-ai-anomalydetector-java" from https://skillstore.io/skills/sickn33-azure-ai-anomalydetector-java.md and its manifest at https://skillstore.io/api/skills/sickn33-azure-ai-anomalydetector-java/manifest. Verify the artifact. You may proceed after verification, subject to the environment's own policy.

Your Agent should still show its plan and request any confirmation required by the security policy.

Agent-readable resources

Use these links when an AI agent, crawler, or script needs clean context instead of reading the full page.

Markdown detail GET /skills/sickn33-azure-ai-anomalydetector-java.md Signed manifest GET /api/skills/sickn33-azure-ai-anomalydetector-java/manifest Signed lockfile GET /api/skills/sickn33-azure-ai-anomalydetector-java/lockfile

Test it

Using "azure-ai-anomalydetector-java". Detect anomalies in a daily series and explain each reported value.

Expected outcome:

A Java implementation outline with client setup, twelve-point validation, sensitivity settings, anomaly indexes, expected values, and upper and lower margins.

Using "azure-ai-anomalydetector-java". Plan multivariate detection for correlated equipment sensors.

Expected outcome:

Prepare aligned sensor files in Azure Blob Storage.
Train a model for the selected time range and sliding window.
Check training status before starting batch inference.
Report anomaly severity and the highest contributing variables.

Using "azure-ai-anomalydetector-java". Add resilient error handling to an existing detector call.

Expected outcome:

A focused handling plan for Azure HTTP failures, status reporting, safe messages, retry decisions, and operational logging.

Security Audit

Safe

v5 • 7/23/2026 Open versioned report

All 32 static findings are false positives. The external-command alerts identify Markdown delimiters, while the URL and environment-variable alerts identify placeholder Azure configuration without embedded credentials or suspicious data handling.

Files scanned

267

Lines analyzed

Review items

False positives ignored

Risk Factors

No confirmed security findings were detected by the latest completed static and semantic audit. This does not prove the skill has no side effects.

Audited by: codex View Audit History →

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Academic citations (APA · BibTeX · CFF)

APA citation

sickn33. (2026). azure-ai-anomalydetector-java security audit report (audit version 5) [Author version unspecified]. Skillstore. https://skillstore.io/skills/sickn33-azure-ai-anomalydetector-java/audits/5

BibTeX citation

@techreport{sickn33-sickn33-azure-ai-anomalydetector-java-2026,
  author = {sickn33},
  title = {azure-ai-anomalydetector-java security audit report (audit version 5)},
  institution = {Skillstore},
  year = {2026},
  number = {5},
  url = {https://skillstore.io/skills/sickn33-azure-ai-anomalydetector-java/audits/5},
  note = {Author version unspecified}
}

CITATION.cff

cff-version: 1.2.0
message: "If you use this Skill, cite its author and this versioned security audit report."
title: "azure-ai-anomalydetector-java security audit report (audit version 5)"
version: "unspecified"
type: report
authors:
  - name: "sickn33"
date-released: "2026-07-23"
url: "https://skillstore.io/skills/sickn33-azure-ai-anomalydetector-java/audits/5"
identifiers:
  - type: other
    value: "skillstore:sickn33-azure-ai-anomalydetector-java:audit:5"
    description: "Skillstore immutable audit report identifier"

Skillstore Score

Why this score Evidence Confidence: High

Architecture

Maintainability

Content

Community

Spec Compliance

What You Can Build

Add Application Monitoring

Implement Java detection for unusual values in application metrics and report expected ranges.

Detect Operational Changes

Analyze recent telemetry for anomalies and change points that may indicate service degradation.

Build Multivariate Models

Train models on correlated signals and inspect which variables contribute most to each anomaly.

Try These Prompts

Create a Client

Create a minimal Java example that builds an Azure Anomaly Detector univariate client. Use environment variables for the endpoint and API key.

Detect Batch Anomalies

Implement daily univariate batch detection for my Java time series. Show input validation, sensitivity configuration, and readable anomaly output.

Compare Latest Points and Changes

Design Java methods for latest-point anomaly detection and change-point detection. Explain required history, granularity choices, and result interpretation.

Design a Multivariate Workflow

Design an end-to-end Java workflow for multivariate training and inference. Include Blob data sources, status checks, contributors, error handling, and model cleanup.

Best Practices

Keep credentials outside source code and prefer DefaultAzureCredential when the deployment supports managed identity.
Match TimeGranularity to the source frequency and validate minimum history before calling the service.
Validate model and inference status before reading results from long-running multivariate operations.

Avoid

Do not place real API keys or SAS tokens in prompts, source files, or logs.
Do not treat every unusual value as an incident without reviewing sensitivity, expected ranges, and business context.
Do not train multivariate models on misaligned timestamps or inconsistent variable histories.

Frequently Asked Questions

Which Java dependency does this skill use?

It demonstrates com.azure:azure-ai-anomalydetector version 3.0.0-beta.6.

Does it support univariate and multivariate detection?

Yes. It covers univariate batch, latest-point, and change-point detection, plus multivariate training and inference.

How are Azure credentials supplied?

Examples use named environment variables or DefaultAzureCredential. Production deployments should use the strongest available secret-management option.

Can it detect anomalies in streaming data?

It demonstrates latest-point detection using a recent history window. Your application must collect and supply that history.

What data does multivariate detection require?

It expects prepared, time-aligned variables available through an Azure Blob Storage data source.

Does the skill deploy or manage Azure infrastructure?

No. It provides SDK usage patterns and model-management calls, but resource provisioning remains outside its scope.

Developer Details

Author

sickn33

License

MIT

Skillstore revision

Version notice

The author did not declare a version.

Repository

https://github.com/sickn33/antigravity-awesome-skills/tree/main/skills/azure-ai-anomalydetector-java

Ref

86d877f219e2131f05dd5b37c5e329c71c7b8ec4

Maintenance freshness

7/26/2026

Usage

5 downloads · 103 views

File structure

📄 SKILL.md