Process identify anomalies and outliers in datasets using machine learning algorithms.
SkillScore82Maintained
Automatic
◆Data Analyst pack
Last verified 8/14/2026
What it changes
Passed 16 of 20 test cases with the skill, and 12 of 20 without it.
Without skill59%
With skill82%
Checks it passed that the model alone missed
Includes a t-SNE cluster plot.
Includes a score distribution histogram.
Includes a scatter plot of anomaly scores.
and 4 more in the full report
5 fixed13 passed either way4 still failing0 broke
Pass-rate lift
+23 pp
Tokens
+36%
Agent turns
0%
Response time
-24%
Model gemini-3.6-flashMeasured 8/14/2026v118 of 22 cases passed with skill · 20 comparable
Tokens, turns and time describe resource use, not a dollar price.
See it in action
One of 5 test cases the skill fixed. Same model, same prompt, with and without the skill.
1 of 5
Prompt
When outputting visual analytics for `datacenter_power_draw.csv`, what specific three visualization plot types are required in the pipeline report?
What was checked, without and with the skill
Checked
Without skill
With skill
Includes a scatter plot of anomaly scores.
Missed
Passed
Includes a score distribution histogram.
Missed
Passed
Includes a t-SNE cluster plot.
Missed
Passed
case-16 · gemini-3.6-flash · 8/14/2026 · answers are capped and redacted
What's inside
The instructions your model reads when the skill loads.
SKILL.md70 lines7 filesincludes scripts
Detecting Data Anomalies
Overview
Identify anomalies and outliers in datasets using statistical and machine learning algorithms including Isolation Forest, One-Class SVM, Local Outlier Factor, and autoencoders. This skill handles the full detection pipeline from data ingestion and feature scaling through algorithm selection, threshold tuning, and result interpretation with anomaly scoring.
Prerequisites
Python 3.9+ with scikit-learn >= 1.3 (pip install scikit-learn)
pandas and NumPy for data manipulation (pip install pandas numpy)
matplotlib or seaborn for anomaly visualizations (pip install matplotlib seaborn)
Dataset in CSV, JSON, Parquet, or database-queryable format
Minimum 500 data points for statistical significance (1000+ recommended)
Optional: PyTorch or TensorFlow for autoencoder-based detection on complex patterns
Instructions
Load the dataset using the Read tool and verify schema, column types, and row count
Profile feature distributions using descriptive statistics to understand baseline behavior
Handle missing values via imputation (median for numeric, mode for categorical) or row exclusion
Apply StandardScaler or MinMaxScaler to numeric features to normalize magnitude differences
Select the detection algorithm based on data characteristics:
Isolation Forest: high-dimensional data, no assumptions on distribution
One-Class SVM: well-defined normal class with clear decision boundary
Local Outlier Factor: density-varying data with local anomaly patterns
Autoencoder: complex temporal or image data with non-linear relationships
Set the contamination parameter to the expected anomaly proportion (start with 0.01-0.05)
Fit the model on the training partition and generate anomaly scores for each data point
Apply the decision threshold to classify points as normal (-1) or anomalous (1)
Analyze flagged anomalies for common characteristics, temporal clusters, or feature correlations
Generate a summary report with detection counts, score distributions, and visualization plots
See ${CLAUDE_SKILL_DIR}/references/implementation.md for the detailed implementation guide.
Output
Anomaly detection summary: total points, anomaly count, contamination rate
Per-record anomaly scores with classification labels
Algorithm configuration: model type, contamination, distance metric, threshold
Feature importance ranking showing which dimensions drive anomaly flags