How do I install the Anomaly Detection skill?

Run npx skillmds@latest add llama-farm/anomaly-detection in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Anomaly Detection safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Anomaly Detection?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Anomaly Detection free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Anomaly Detection?

llama-farm (@llama-farm) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Anomaly Detection by llama-farm — Agent Skill

Name: Anomaly Detection
Author: llama-farm

File contents

Anomaly Detection Skill

Guide for training and using anomaly detection models in LlamaFarm.

When to Load

Load this skill when the user:

Wants to detect anomalies or outliers in data
Asks about anomaly detection backends or algorithms
Needs to train an anomaly model
Wants to score data for anomalies
Asks about contamination rates or thresholds
Needs to manage anomaly detection models

API Overview

Endpoint	Method	Description
`/v1/ml/anomaly/fit`	POST	Train a model on data
`/v1/ml/anomaly/score`	POST	Score data points (anomaly likelihood)
`/v1/ml/anomaly/detect`	POST	Detect anomalies (binary labels + scores)
`/v1/ml/anomaly/save`	POST	Save a trained model
`/v1/ml/anomaly/load`	POST	Load a previously saved model
`/v1/ml/anomaly/models`	GET	List available trained models
`/v1/ml/anomaly/backends`	GET	List available detection backends

Backend Quick Reference

Backend	Speed	Accuracy	Best For
`iforest`	Fast	High	General purpose, tabular data
`ecod`	Very Fast	Good	High-dimensional data, streaming
`lof`	Medium	High	Cluster-based anomalies
`knn`	Medium	High	Local density anomalies
`ocsvm`	Medium	High	Well-separated anomalies
`copod`	Very Fast	Good	Multivariate data, fast screening
`hbos`	Very Fast	Moderate	Histogram-based, fast baseline
`loda`	Very Fast	Moderate	Lightweight, streaming-friendly
`cblof`	Medium	High	Cluster-based with labels
`mcd`	Medium	High	Gaussian-distributed data
`pca`	Fast	Good	Dimensionality reduction
`autoencoder`	Slow	Very High	Complex patterns, deep learning

Decision Tree: Choosing a Backend

Need speed above all? -> ecod or hbos
General tabular data? -> iforest (best default)
High-dimensional data? -> ecod or pca
Cluster-shaped anomalies? -> lof or cblof
Complex non-linear patterns? -> autoencoder
Need interpretability? -> ecod or copod
Streaming use case? -> ecod, hbos, or loda

Quick Start

Train a model

curl -X POST http://localhost:14345/v1/ml/anomaly/fit \
  -H "Content-Type: application/json" \
  -d '{
    "data": [[1.0, 2.0], [1.1, 2.1], [1.2, 1.9], [10.0, 10.0]],
    "backend": "iforest",
    "contamination": 0.1,
    "normalization": "standardize"
  }'

Detect anomalies

curl -X POST http://localhost:14345/v1/ml/anomaly/detect \
  -H "Content-Type: application/json" \
  -d '{
    "model_name": "my_model",
    "data": [[1.0, 2.0], [50.0, 50.0]]
  }'

List models

curl http://localhost:14345/v1/ml/anomaly/models

Normalization Methods

Method	When to Use
`standardize`	Default -- centers data, works for most cases
`zscore`	When you need strict z-score scaling
`raw`	When data is already normalized or normalization would distort it

Progressive Disclosure

For detailed guidance:

backends.md - Per-backend algorithm details, parameters, and examples
data-preparation.md - Normalization, encoding, handling mixed data types
model-lifecycle.md - Saving, loading, versioning, managing models
tuning.md - Contamination rates, thresholds, validation, A/B testing

---name: anomaly-detectiondescription: Batch anomaly detection with 12+ backends. Train models, score data, detect outliers using isolation forests, autoencoders, and more.---# Anomaly Detection SkillGuide for training and using anomaly detection models in LlamaFarm.## When to LoadLoad this skill when the user:- Wants to detect anomalies or outliers in data- Asks about anomaly detection backends or algorithms- Needs to train an anomaly model- Wants to score data for anomalies- Asks about contamination rates or thresholds- Needs to manage anomaly detection models## API Overview| Endpoint | Method | Description ||----------|--------|-------------|| `/v1/ml/anomaly/fit` | POST | Train a model on data || `/v1/ml/anomaly/score` | POST | Score data points (anomaly likelihood) || `/v1/ml/anomaly/detect` | POST | Detect anomalies (binary labels + scores) || `/v1/ml/anomaly/save` | POST | Save a trained model || `/v1/ml/anomaly/load` | POST | Load a previously saved model || `/v1/ml/anomaly/models` | GET | List available trained models || `/v1/ml/anomaly/backends` | GET | List available detection backends |## Backend Quick Reference| Backend | Speed | Accuracy | Best For ||---------|-------|----------|----------|| `iforest` | Fast | High | General purpose, tabular data || `ecod` | Very Fast | Good | High-dimensional data, streaming || `lof` | Medium | High | Cluster-based anomalies || `knn` | Medium | High | Local density anomalies || `ocsvm` | Medium | High | Well-separated anomalies || `copod` | Very Fast | Good | Multivariate data, fast screening || `hbos` | Very Fast | Moderate | Histogram-based, fast baseline || `loda` | Very Fast | Moderate | Lightweight, streaming-friendly || `cblof` | Medium | High | Cluster-based with labels || `mcd` | Medium | High | Gaussian-distributed data || `pca` | Fast | Good | Dimensionality reduction || `autoencoder` | Slow | Very High | Complex patterns, deep learning |## Decision Tree: Choosing a Backend1. **Need speed above all?** -> `ecod` or `hbos`2. **General tabular data?** -> `iforest` (best default)3. **High-dimensional data?** -> `ecod` or `pca`4. **Cluster-shaped anomalies?** -> `lof` or `cblof`5. **Complex non-linear patterns?** -> `autoencoder`6. **Need interpretability?** -> `ecod` or `copod`7. **Streaming use case?** -> `ecod`, `hbos`, or `loda`## Quick Start### Train a model```bashcurl -X POST http://localhost:14345/v1/ml/anomaly/fit \ -H "Content-Type: application/json" \ -d '{ "data": [[1.0, 2.0], [1.1, 2.1], [1.2, 1.9], [10.0, 10.0]], "backend": "iforest", "contamination": 0.1, "normalization": "standardize" }'```### Detect anomalies```bashcurl -X POST http://localhost:14345/v1/ml/anomaly/detect \ -H "Content-Type: application/json" \ -d '{ "model_name": "my_model", "data": [[1.0, 2.0], [50.0, 50.0]] }'```### List models```bashcurl http://localhost:14345/v1/ml/anomaly/models```## Normalization Methods| Method | When to Use ||--------|------------|| `standardize` | Default -- centers data, works for most cases || `zscore` | When you need strict z-score scaling || `raw` | When data is already normalized or normalization would distort it |## Progressive DisclosureFor detailed guidance:- **backends.md** - Per-backend algorithm details, parameters, and examples- **data-preparation.md** - Normalization, encoding, handling mixed data types- **model-lifecycle.md** - Saving, loading, versioning, managing models- **tuning.md** - Contamination rates, thresholds, validation, A/B testing

Frequently asked questions

Anomaly Detection