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".
npx skills add majiayu000/claude-skill-registry --skill detecting-data-anomalies --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.
# 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) - Algor
- Overview
- Prerequisites
- Instructions
- Output
- Error Handling
- Examples
- Resources
What does the detecting-data-anomalies 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".
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill detecting-data-anomalies --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.