pyod: A Python library for anomaly detection across tabular, time series, graph, text, image, and audio data

by yzhao062 · Agent Tool · ★ 10.1k

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

Security audit verdict: SAFE · quality 53/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

agentic-aianomaly-detectiondata-miningdata-sciencedeep-learningfoundation-modelsfraud-detectiongraph-anomaly-detectionimage-anomaly-detectionmachine-learning

Quick Facts

Stars10,081
Forks1,520
LanguagePython
CategoryAgent Tool
LicenseBSD-2-Clause
Quality Score53/100
Open Issues227
Last Updated2026-10-28
Created2017-10-03
Platformspython
Est. Tokens~25k

About pyod

.. image:: https://raw.githubusercontent.com/yzhao062/pyod/master/brand/pyod-icon.svg :target: https://pyod.dev :alt: PyOD Ecosystem :width: 84px Python Outlier Detection (PyOD) 3 ================================== PyOD 3: Agentic Anomaly Detection At Scale .. image:: https://img.shields.io/badge/website-pyod.dev-990000 :target: https://pyod.dev :alt: Website ..

Compatible Skills

These tools work well together with pyod for enhanced workflows:

  • PyOmniTS — semantic(0.35)+complementary+rare_topics+same_lang+shared_platform (57%)

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. It is a first-layer scan, not a manual audit — we say so rather than overstate it.

The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.

Sources & who's responsible:

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