nixtla-anomaly-detector
Detects anomalies in time series data using TimeGPT. Identifies outliers, level shifts, and trend breaks without model training. Use when identifying anomalies, outliers, or unusual patterns in time series. Trigger with "detect anomalies", "find outliers", "anomaly detection".
npx skills add majiayu000/claude-skill-registry --skill nixtla-anomaly-detector-intent-solutions-io-plugins-nixtla --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Nixtla Anomaly Detector Automatically detect and flag anomalies in time series data using TimeGPT. ## Overview This skill leverages TimeGPT's anomaly detection capabilities to identify outliers, level shifts, and trend breaks in time series data without requiring model training. It accepts CSV data, runs anomaly detection via the Nixtla API, and produces a detailed report with visualizations. ## Prerequisites **Required**: - Python 3.8+ - `nixtla`, `pandas`, `matplotlib` packages **Environment Variables**: - `NIXTLA_TIMEGPT_API_KEY`: Your TimeGPT API key **Installation**: ```bash pip install nixtla pandas matplotlib ``` ## Instructions ### Step 1: Prepare Input Data Ensure your CSV file has the required Nixtla schema columns: | Column | Type | Description | |--------|------|-------------| | `unique_id` | string | Series identifier | | `ds` | datetime | Timestamp | | `y` | numeric | Value to analyze | ### Step 2: Set API Key ```bash export NIXTLA_TIMEGPT_API_KEY=your_api_key_here ``` ### Step 3: Run Anomaly Detection Execute the detection script: ```bash python {baseDir}/scripts/detect_anomalies.py --input your_data.csv ``` **Available options**: - `--input`, `-i`: Input CSV file
- Overview
- Prerequisites
- Instructions
- Step 1: Prepare Input Data
- Step 2: Set API Key
- Step 3: Run Anomaly Detection
- Step 4: Review Results
- Output
- Error Handling
- Examples
- Example 1: Detect outliers in website traffic
- Example 2: Identify trend break in sales data
- Resources
pip install nixtla pandas matplotlib
export NIXTLA_TIMEGPT_API_KEY=your_api_key_here
python {baseDir}/scripts/detect_anomalies.py --input your_data.csv
python {baseDir}/scripts/detect_anomalies.py --input traffic.csv
python {baseDir}/scripts/detect_anomalies.py -i sales.csv -o sales_anomalies.csvWhat does the nixtla-anomaly-detector skill do?
Detects anomalies in time series data using TimeGPT. Identifies outliers, level shifts, and trend breaks without model training. Use when identifying anomalies, outliers, or unusual patterns in time series. Trigger with "detect anomalies", "find outliers", "anomaly detection".
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill nixtla-anomaly-detector-intent-solutions-io-plugins-nixtla --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.