time-series-featureslisted

# Time Series Features A time-series feature engineering skill. Produces lag, rolling, calendar, and decomposition features with rigorous protection against temporal data leakage. ## When to use this skill Activate when the user has a **time-ordered dataset** and wants to prepare features for forecasting or temporal modeling. Typical signals: - "Create lag features for this sales data" - "Generate rolling window features" - "Prepare this time series for forecasting" - "What's the seasonality in this data?" - "Génère des features temporelles", "fais du feature engineering pour des séries temporelles" **Pre-conditions:** - The dataset MUST have a datetime column (or a column convertible to datetime) - The dataset SHOULD be sorted chronologically (skill will verify and sort if not) - The dataset SHOULD have a regular frequency (daily, hourly, monthly) — skill will detect and warn if irregular **Do NOT activate this skill for:** - Cross-sectional datasets without time order → use `feature-engineer` - Pure exploration → use `eda-explorer` - Single time series without features (just the target) → suggest `feature-engineer` first to add covariates ## Workflow For every dataset, follow these 7 phases in order. **Never skip phase 1 or 7 — they prevent data leakage.** ### Phase 1 — Detect & validate temporal structure Run these checks first: 1. **Identify the datetime column**. If user specified, use it. Otherwise, auto-detect by trying to parse each column as datetime; the o