feature-engineering
feature-engineering is a Time series & forecasting skill for AI agents, published by skforecast in the skforecast repository on GitHub. Skillspace rates feature-engineering D (62/100) and it passed the safety scan; the rating is based on static analysis only.
Creates features for time series forecasting: calendar features with skforecast's `CalendarFeatures` (cyclical, onehot, or spline encoding) — either delegated to the forecaster via the `calendar_features` parameter or built manually as exog — holiday distance features with `calculate_distance_from_holiday`, rolling statistics with `RollingFeatures`, differencing, and categorical exogenous variables. Use when the user wants to improve model accuracy through feature engineering or asks about exogenous variable creation.
- Grade
- D
- Quality
- 62 / 100
- Safety
- Passed
- GitHub stars
- 1,512
Unproven: rated by static analysis only, not yet compared head-to-head.
This rating describes the version of the repository we crawled; it may have changed since, so go by its current files.
Our review
- Architecture
- 78 / 100
- Maturity
- Structured: ships references or scripts
- Security scan
- No issues found
- License
- BSD-3-Clause
- Adoption
- Adopted by 1 other repositories, in 0 collections
- Scope
- Vendor-specific: built around one product. Feature engineering with the public skforecast library's APIs
Not checked yet
- Not judged on the rubric yet (not a contender in its category)
- No blind task eval yet
- Sibling files (scripts, references) not fetched; security scan covers SKILL.md only
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