Forecast Operational Metrics
技能 已验证 活跃Forecast infrastructure and application metrics using Prophet or statsmodels for capacity planning, cost optimization, and proactive scaling. Visualize predictions in Grafana and set up alerts for projected resource exhaustion. Use when forecasting infrastructure capacity needs for CPU, memory, or disk, planning hardware procurement for next quarter, predicting cost trends to optimize cloud spending, or setting up proactive scaling policies based on predicted load.
Forecast future infrastructure and application metrics to enable proactive capacity planning, cost optimization, and automated scaling policies.
功能
- Forecast metrics using Prophet and statsmodels
- Load historical data from Prometheus
- Calculate capacity exhaustion dates
- Visualize predictions and alerts in Grafana
- Automate daily forecast generation
使用场景
- Forecasting infrastructure capacity needs (CPU, memory, disk)
- Planning hardware procurement for upcoming quarters
- Predicting cost trends to optimize cloud spending
- Setting up proactive scaling policies based on predicted load
非目标
- Real-time anomaly detection (complements `detect-anomalies-aiops`)
- Infrastructure provisioning or automated scaling execution (provides forecasts for these actions)
- Directly managing Grafana dashboards (exports data for visualization)
安装
/plugin install agent-almanac@pjt222-agent-almanac质量评分
已验证类似扩展
OraClaw Forecast
100AI 代理的时间序列预测。ARIMA 和 Holt-Winters 预测(含置信区间)。预测收入、流量、价格或任何序列数据。推理延迟低于 5 毫秒。
TimesFM Forecasting
100Zero-shot time series forecasting with Google's TimesFM foundation model. Use for any univariate time series (sales, sensors, energy, vitals, weather) without training a custom model. Supports CSV/DataFrame/array inputs with point forecasts and prediction intervals. Includes a preflight system checker script to verify RAM/GPU before first use.
Plan Capacity
99Perform capacity planning using historical metrics and growth models. Use predict_linear for forecasting, identify resource constraints, calculate headroom, and recommend scaling actions before saturation. Use before seasonal traffic spikes or product launches, during quarterly capacity reviews, when resource utilization trends upward, or before budget planning cycles.
Alterlab Aeon
98Part of the AlterLab Academic Skills suite. This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.
TimesFM Forecasting
96Part of the AlterLab Academic Skills suite. Zero-shot time series forecasting with Google's TimesFM foundation model. Use for any univariate time series (sales, sensors, energy, vitals, weather) without training a custom model. Supports CSV/DataFrame/array inputs with point forecasts and prediction intervals. Includes a preflight system checker script to verify RAM/GPU before first use.
Aeon Time Series Machine Learning
95This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.