llm-eval-type-selector
llm-eval-type-selector is an LLM evaluation & observability skill for AI agents, published by latitude-dev in the eval-skills repository on GitHub. Skillspace rates llm-eval-type-selector D (67/100) and it passed the safety scan; the rating is based on static analysis only.
Use this skill when a developer wants to decide what type of evaluation to build for their AI system. Triggers on: "should I use a rule or a judge", "what type of eval should I build", "decide eval type", "judge vs programmatic rule", "LLM-as-judge vs rule-based eval", "which evaluation type should I use", "how do I evaluate [X]", "what eval should I use for this failure", "is this a rule or a judge", "how should I evaluate my AI automatically", "what kind of eval fits this issue". Takes one or more failure modes or quality dimensions and returns a concrete type recommendation — programmatic rule, LLM-as-judge, or composite — with rationale and a suggested implementation path.
- Grade
- D
- Quality
- 67 / 100
- Safety
- Passed
- GitHub stars
- 20
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
- 76 / 100
- Maturity
- Packaged: licensed, with a version or references
- Security scan
- No issues found
- License
- MIT
- Adoption
- Adopted by 0 other repositories, in 0 collections
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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