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Anomaly Detection

Checked
aj-geddes·vlatest·data·MIT

This Anomaly Detection skill identifies unusual patterns in data, alerting you to potential problems and improving proactive monitoring.

Repository

Install on your platform

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

Installs under a different name. Its SKILL.md names it Anomaly Detection, not the name it is listed under, so the command below uses that name.
  1. 1

    Run this in your project

    terminal
    $ npx skills add aj-geddes/useful-ai-prompts --skill "Anomaly Detection" -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

aj-geddes

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 aj-geddes/useful-ai-prompts at 3f5182cfd7.

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

Commit
3f5182cfd7
Manifest
skills/anomaly-detection/SKILL.md
Scanned
3 files, 352 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 empowers AI agents to identify unusual patterns, outliers, or deviations within datasets that differ significantly from expected norms. It enables automated analysis of data streams to flag potential errors, fraud, or critical system anomalies without manual intervention.

When to use it

  • Monitoring financial transactions to detect fraudulent activity or unauthorized spending spikes.
  • Analyzing server logs to identify unusual traffic patterns indicating a cyberattack or hardware failure.
  • Reviewing manufacturing sensor data to spot equipment malfunctions before they cause downtime.
  • Scanning customer feedback streams to find sudden drops in sentiment or emerging crisis topics.

Key capabilities

  • Identifies statistical outliers and deviations from baseline behavior.
  • Processes structured and unstructured data for irregularities.
  • Generates alerts based on detected anomalies.
  • Supports real-time stream analysis and batch data processing.

Example prompts

  • "Analyze this sales dataset and highlight any transactions that deviate significantly from the monthly average."
  • "Scan these server logs for unusual error codes or traffic spikes occurring outside business hours."
  • "Review the attached sensor readings and report any values that fall outside the standard operating range."

Tips & gotchas

Ensure your input data includes a clear baseline or historical context so the agent can accurately determine what constitutes 'normal' behavior. For best results, provide specific thresholds if you have known limits for acceptable variance in your domain.