eval-evaluator
eval-evaluator is an LLM evaluation & observability skill for AI agents, published by ZTE-AICloud in the Co-OmniSpec repository on GitHub. Skillspace rates eval-evaluator C (73/100) and it passed the safety scan; the rating is based on static analysis only.
Evaluate code generation quality using third-party judge model metrics (ICE Score + Code Judge). Use this when the user needs to assess generated code quality, compare code outputs, or evaluate AI-generated code against requirements. This skill provides automated scoring for functional correctness, usefulness, and code consistency using LLM-based evaluation.
- Grade
- C
- Quality
- 73 / 100
- Safety
- Passed
- GitHub stars
- 48
Unproven: rated by static analysis only, not yet compared head-to-head.
Our two independent classifiers differ on this skill's category, so treat the placement as provisional.
This rating describes the version of the repository we crawled; it may have changed since, so go by its current files.
Our review
- Architecture
- 93 / 100
- Maturity
- Structured: ships references or scripts
- Security scan
- Low-risk patterns
- License
- MIT
- Adoption
- Adopted by 0 other repositories, in 0 collections
What the scanner flagged
- mentions secrets in text
Not checked yet
- Not judged on the rubric yet (not a contender in its category)
- No blind task eval yet
- Scope (portable / vendor / repo-internal) not assessed; only the top of each category is read for scope
- Sibling files (scripts, references) not fetched; security scan covers SKILL.md only
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