data-anomaly-detection
Detect anomalies and outliers in research data using statistical methods
Profile →npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill data-anomaly-detection --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.
# Data Anomaly Detection A skill for identifying anomalies, outliers, and suspicious patterns in research datasets. Combines classical statistical methods with modern machine learning approaches to flag data points that deviate significantly from expected distributions, helping researchers maintain data integrity and uncover genuine scientific findings. ## Overview Anomalous data points in researc
What does the data-anomaly-detection skill do?
Detect anomalies and outliers in research data using statistical methods
How do I install it?
Run `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill data-anomaly-detection --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 brycewang-stanford/Auto-Empirical-Research-Skills, a repository with 3,244 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.