metric-selection
metric-selection is a Time series & forecasting skill for AI agents, published by skforecast in the skforecast repository on GitHub. Skillspace rates metric-selection D (64/100) and it passed the safety scan; the rating is based on static analysis only.
Guides selection of the most appropriate evaluation metric(s) for a forecasting task based on forecaster type, prediction output type, data characteristics, and multi-series aggregation needs. Use when the user asks which metric to use, how to evaluate forecast quality, or needs help configuring the `metric` parameter in backtesting or hyperparameter search.
- Grade
- D
- Quality
- 64 / 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
- 82 / 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. Choosing metrics for the public skforecast library's backtesting functions
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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