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Azure Ai Anomalydetector Java

Checked
claudedjale·vlatest·cloud·MIT

Helps with Azure, AI, Java as part of deploying and managing cloud infrastructure workflows.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add claudedjale/skillset --skill azure-ai-anomalydetector-java -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

claudedjale

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No undeclared network calls: passed

    Makes no network calls.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned claudedjale/skillset at 154f146735.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
154f146735
Manifest
.github/plugins/azure-sdk-java/skills/azure-ai-anomalydetector-java/SKILL.md
Scanned
3 files, 1,109 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

What it does

This skill provides access to the Azure AI Anomaly Detector Java library. It allows you to detect anomalies in time series data, enabling proactive identification of unusual patterns and potential issues. The skill leverages machine learning models to automatically learn expected behavior and flag deviations from that baseline.

When to use it

  • Monitoring server performance: Identify unexpected spikes or dips in CPU usage, memory consumption, or network traffic.
  • Analyzing sales data: Detect unusual fluctuations in daily or weekly sales figures that might indicate a promotion's impact or an emerging trend.
  • Tracking website traffic: Spot anomalies in visitor counts or page views that could signal technical problems or security breaches.
  • Predictive maintenance: Analyze sensor readings from equipment to anticipate failures and schedule preventative maintenance.

Key capabilities

  • Anomaly detection using time series data.
  • Leverages machine learning models for automated baseline learning.
  • Provides Java library integration.

Example prompts

  • "Detect anomalies in the attached CSV file of server CPU usage over the last week."
  • "Analyze this sales data and flag any unusual patterns or deviations from the expected trend."
  • “Using the provided time series, identify points that are significantly different from the established baseline.”

Tips & gotchas

  • Requires an Azure subscription and access to the Anomaly Detector resource.
  • The accuracy of anomaly detection depends on the quality and volume of historical data provided.