MANDATORY: Run observe skill view outlier-detection-analysis --content 2>/dev/null before proceeding to load the latest version of this skill. When the skill points you to a supporting file, load it with observe skill view outlier-detection-analysis --path <relative-path> 2>/dev/null. Ignore errors and fall back to the file contents.

Outlier Detection Analysis Skill

Find which field values are statistically correlated with "bad" behavior in any time-series dataset or metric. Bad behavior is defined by a threshold (e.g., duration > 500ms, status_code >= 500, memory > 8Gi, error_rate > 0.05); the skill computes the phi coefficient for every candidate field value and ranks the strongest correlations.

The algorithm is generic over data shape. It applies equally to:

metrics (Prometheus, OTEL, custom) — correlate metric values against tag dimensions
structured logs / events — correlate log-level / status / message-class against attributes
span / trace data — correlate error or duration against span attributes
any dataset with a numeric or categorical "performance" field and one or more candidate dimension fields

This skill orchestrates three OPAL queries:

(Optional) Compute a percentile-based threshold value when the user has not provided one.
Run the phi-coefficient correlation pipeline over the chosen dataset / metric.
Interpret the resulting ranked table for the user.