time_series_anomaly_detection
Detect anomalies in time series data using Prophet Framework (Meta), which frames the seasonality, trend holiday effect and other needed regressors into its model, to identify unusual surges or slumps in trends. This is a general methodology analyst can use for understanding what changes of their tracking metrics are manifesting anomalies pattern.
Packaged view
This page reorganizes the original catalog entry around fit, installability, and workflow context first. The original raw source lives below.
Install command
npx @skill-hub/cli install benchflow-ai-skillsbench-time-series-anomaly-detection
Repository
Skill path: tasks_no_script_no_ref/trend-anomaly-causal-inference/environment/skills/time_series_anomaly_detection
Detect anomalies in time series data using Prophet Framework (Meta), which frames the seasonality, trend holiday effect and other needed regressors into its model, to identify unusual surges or slumps in trends. This is a general methodology analyst can use for understanding what changes of their tracking metrics are manifesting anomalies pattern.
Open repositoryBest for
Primary workflow: Analyze Data & AI.
Technical facets: Full Stack, Data / AI.
Target audience: Development teams looking for install-ready agent workflows..
License: Unknown.
Original source
Catalog source: SkillHub Club.
Repository owner: benchflow-ai.
This is still a mirrored public skill entry. Review the repository before installing into production workflows.
What it helps with
- Install time_series_anomaly_detection into Claude Code, Codex CLI, Gemini CLI, or OpenCode workflows
- Review https://github.com/benchflow-ai/SkillsBench before adding time_series_anomaly_detection to shared team environments
- Use time_series_anomaly_detection for development workflows
Works across
Favorites: 0.
Sub-skills: 0.
Aggregator: No.