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Detecting anomalies and outliers in data v1.0.0

Use this resource to detect anomalies and outliers in a dataset with machine learning methods

New

  • 527 downloads
  • 128 likes
  • 113 saves

Free

Install with the CLI

In a terminal, from the folder you want:

npx --yes palatekit@latest install data-anomaly-detection

With no option, the skill goes to ~/.agents/skills/; add --agent claude-code (or codex, cursor…) to target an agent.

First time: connect the CLI with

npx --yes palatekit@latest login

The agent prepares the data, picks an algorithm, tunes the threshold and interprets anomaly scores. You get a list of suspicious observations and control charts.

Mostly picked by

Updated November 2, 2026

Card for the “Detecting anomalies and outliers in data” skill

Details

Works in
Install guides
Generates with
Your agent's model
Licence
MIT

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What you get

  • SKILL.md: detection pipeline, algorithm choice and threshold tuning
  • references/implementation.md and references/examples.md: Python code and use cases

Good fit for

  • You look for unusual transactions in an export
  • Your sensor readings show unexplained spikes

Not for

    Tags

    • anomalies
    • outliers
    • machine learning
    • python

    About

    You say

    “Find the abnormal orders in this CSV file with Isolation Forest and chart the scores.”

    Card for the “Detecting anomalies and outliers in data” skill

    Skill published by Jeremy Longshore <jeremy@intentsolutions.io> under the MIT license, delivered in its original version.

    Show more

    How to use it

    1. Install the skill in your agent's skills folder
    2. Ask your agent to detect anomalies and outliers in a dataset with machine learning methods
    3. Review and adjust the result

    Content preview

    Files

    • assets/README.md0.4 KB
    • LICENSE1.1 KB
    • NOTICE-PALATEKIT.md0.5 KB
    • references/errors.md1.2 KB
    • references/examples.md12.8 KB
    • references/implementation.md1.9 KB
    • references/README.md0.1 KB
    • scripts/README.md0.6 KB
    • SKILL.md5.5 KB

    Origin and licence

    Listed unmodified

    Author
    Jeremy Longshore <jeremy@intentsolutions.io>
    Source
    github.com/jeremylongshore/tons-of-skills-marketplace/tree/ab85a061f7bcc039dac13ee193de7a4cd4388571/skills/.curated/detecting-data-anomalies
    Licence
    MIT

    Notice

    « detecting-data-anomalies » (Jeremy Longshore <jeremy@intentsolutions.io>, MIT) livré dans sa version d'origine : fiche en français et en anglais par PalateKit.

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