Skill Detail
Score RAG answer quality and retrieval quality before rollout with Ragas
Measure whether a RAG change actually improved answers and retrieval, instead of guessing from a few spot checks.
Security & VerificationMulti-Framework
Security & Verification
Multi-Framework
Security Reviewed
Tool match: ragas
⭐ 13.4k GitHub stars
Apache-2.0 license
INSTALL WITH ANY AGENT
npx skills add agentskillexchange/skills --skill score-rag-answer-quality-and-retrieval-quality-before-rollout-with-ragas
Works best when you want a reusable capability, not another fragile one-off prompt.
At a glance
Tools required
Python environment, Ragas package, model provider credentials, evaluation dataset or testset generation inputs, access to the target RAG workflow
Install & setup
Install Ragas in a Python environment, configure a supported model provider, prepare evaluation samples or generate a testset, then run the documented evaluation flow against the target RAG pipeline.
Author
Vibrant Labs AI
Publisher
Organization
Last updated
Apr 15, 2026
Quick brief
Use Ragas when an agent needs a structured evaluation loop for a RAG system after changing prompts, retrievers, chunking, indexes, or models. The job is specific: assemble or generate evaluation data, score answer quality and retrieval quality with RAG-focused metrics, then compare runs before rollout. That scope boundary, RAG evaluation rather than general observability or broad LLM experimentation, keeps this skill-shaped instead of reading like a plain framework listing.