timescaledb
MANDATORY when working with time-series data, hypertables, continuous aggregates, or compression - enforces TimescaleDB 2.24.0 best practices including lightning-fast recompression, UUIDv7 continuous aggregates, and Direct Compress
thrivve-mc-how-many
Thrivve Partners Monte Carlo simulation to forecast story/task completion based on historical throughput. Use when the user asks "how many stories/tasks will be completed by [date]" with historical daily throughput data. Requires at least 10 days of throughput history and a future target date. Provides probabilistic forecasts at specified confidence levels (default 85%).
detecting-anomalies
Detect anomalies in metrics and time-series data using OPAL statistical methods. Use when you need to identify unusual patterns, spikes, drops, or outliers in observability data. Covers statistical outlier detection (Z-score, IQR), threshold-based alerts, rate-of-change detection with window functions, and moving average baselines. Choose pattern based on data distribution and anomaly type.
time-series-analysis
Analyze event datasets (logs) and intervals over time using OPAL timechart. Use when you need to visualize trends, track metrics over time, or create time-series charts. Covers timechart for temporal binning, bin duration options (1h, 5m, 1d), options(bins:N) for controlling bin count, and understanding temporal output columns (_c_valid_from, _c_valid_to, _c_bucket). Returns multiple rows per group for time-series visualization. For single summaries, see aggregating-event-datasets skill.