rag-evaluator
rag-evaluator is an LLM evaluation & observability skill for AI agents, published by kumaran-is in the claude-code-onboarding repository on GitHub. Skillspace rates rag-evaluator D (68/100) and it passed the safety scan; the rating is based on static analysis only.
Use when designing, building, or operating RAG evaluation — golden sets, retrieval metrics (Recall@K, NDCG, MRR), answer metrics (faithfulness, citation accuracy), LLM-as-judge setup, CI gates for RAG, eval at scale, drift detection. Triggers on phrases like "evaluate RAG", "golden set", "RAG metrics", "RAGAS", "faithfulness evaluation", "regression test RAG", "RAG CI".
- Grade
- D
- Quality
- 68 / 100
- Safety
- Passed
- GitHub stars
- 32
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
- 100 / 100
- Maturity
- Basic: a single SKILL.md
- 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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