detecting-data-anomalies
Process identify anomalies and outliers in datasets using machine learning algorithms. Use when analyzing data for unusual patterns, outliers, or unexpected deviations from normal behavior. Trigger with phrases like "detect anomalies", "find outliers", or "identify unusual patterns".
Best use case
detecting-data-anomalies is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Process identify anomalies and outliers in datasets using machine learning algorithms. Use when analyzing data for unusual patterns, outliers, or unexpected deviations from normal behavior. Trigger with phrases like "detect anomalies", "find outliers", or "identify unusual patterns".
Teams using detecting-data-anomalies should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/detecting-data-anomalies/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How detecting-data-anomalies Compares
| Feature / Agent | detecting-data-anomalies | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Process identify anomalies and outliers in datasets using machine learning algorithms. Use when analyzing data for unusual patterns, outliers, or unexpected deviations from normal behavior. Trigger with phrases like "detect anomalies", "find outliers", or "identify unusual patterns".
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Detecting Data Anomalies
## Overview
This skill provides automated assistance for the described functionality.
## Prerequisites
Before using this skill, ensure you have:
- Dataset in accessible format (CSV, JSON, or database)
- Python environment with scikit-learn or similar ML libraries
- Understanding of data distribution and expected patterns
- Sufficient data volume for statistical significance
- Knowledge of domain-specific normal behavior
- Data preprocessing capabilities for cleaning and scaling
## Instructions
1. Load dataset using Read tool
2. Inspect data structure and identify relevant features
3. Clean data by handling missing values and inconsistencies
4. Normalize or scale features as appropriate for algorithm
5. Split temporal data if time-series analysis is needed
1. Apply selected algorithm using Bash tool
2. Generate anomaly scores for each data point
3. Classify points as normal or anomalous based on threshold
4. Extract characteristics of identified anomalies
See `{baseDir}/references/implementation.md` for detailed implementation guide.
## Output
- Total data points analyzed
- Number of anomalies detected
- Contamination rate (percentage of anomalies)
- Algorithm used and configuration parameters
- Confidence scores for detected anomalies
- Record identifier and timestamp (if applicable)
## Error Handling
See `{baseDir}/references/errors.md` for comprehensive error handling.
## Examples
See `{baseDir}/references/examples.md` for detailed examples.
## Resources
- Isolation Forest documentation and implementation examples
- One-Class SVM for novelty detection
- Local Outlier Factor (LOF) for density-based detection
- Autoencoder-based anomaly detection for deep learning approaches
- scikit-learn anomaly detection moduleRelated Skills
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