aiml-pyod-detection

Detect anomalies in text embeddings using PyOD's IsolationForest.

934|152|Updated Mar 1, 2026

Search Tags:#templates #nlp #anomaly-detection #machine-learning #pyod #isolationforest

Ready to run

npx skills add https://github.com/wuyoscar/ISC-Bench --skill aiml-pyod-detection

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: aiml-pyod-detection
Source: https://github.com/wuyoscar/ISC-Bench/tree/main/templates/aiml_pyod
Command: npx skills add https://github.com/wuyoscar/ISC-Bench --skill aiml-pyod-detection

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pyod, sentence-transformers, numpy.

What problem does it solve?

ISC PyOD detection template provides a guided workflow to validate anomaly detection on text embeddings by using IsolationForest to distinguish normal samples from outliers in an AI safety evaluation dataset.

Core Features & Use Cases

Deterministic anomaly detection: employs PyOD's IForest on textual embeddings to flag anomalous samples.
Polarity-based verification: includes a semantic polarity mechanism to ensure outliers differ from normal samples.
Reproducible evaluation template: provides script scaffolding and sample prompts for consistent testing.

Quick Start

Run the anomaly-detection template on a sample dataset to validate the PyOD integration.

Frequently Asked Questions about aiml-pyod-detection

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I detect anomalies in text embeddings for AI safety evaluation?▼

To detect anomalies in text embeddings, you can use PyOD's IsolationForest within an AI safety evaluation workflow to distinguish normal samples from outliers. This template provides script scaffolding to validate the integration and produce verifiable outliers.

Do I need SentenceTransformers to use PyOD for anomaly detection on text?▼

Yes, you need SentenceTransformers to generate the text embeddings required by the PyOD IsolationForest model. The template requires Python, PyOD, SentenceTransformers, and a minimal embedding dataset to produce deterministic results.

How does the semantic polarity mechanism verify detected outliers?▼

The semantic polarity mechanism verifies outliers by ensuring they differ from normal samples in the evaluation dataset. This polarity-based verification confirms that the flagged anomalous samples are semantically distinct from the baseline.

Can I use this template to validate anomaly detection pipelines across prompts?▼

Yes, the template is designed for researchers and engineers validating frontier-model evaluation pipelines across text data and prompts. It provides reproducible script scaffolding and sample prompts for consistent testing.

What is the best way to reproduce anomaly detection results with PyOD?▼

The best way to reproduce anomaly detection results is by using the template's reproducible evaluation scaffolding and sample prompts. It employs PyOD's IsolationForest on textual embeddings to deliver deterministic results for outlier detection.