The body is lean and assumes Claude's competence — no RAG/library tutorials, no padding — and handles time-sensitive API drift by deferring to the canonical doc rather than restating signatures, matching the score-3 anchor.

It provides executable pip commands, a complete copy-paste-ready DiscreteMetric quickstart, a CI snippet with concrete threshold assertions, and an anti-pattern table with fixes, matching the fully-executable score-3 anchor.

Steps 1–6 are clearly sequenced (install → quickstart → pick metrics → dataset shape → integration → CI), with the CI assertions serving as an explicit verification checkpoint; this is evaluation rather than destructive/batch work, so no mid-flow validation cap applies.

SKILL.md is an overview that offloads the full metric catalog to a clearly signaled, one-level-deep references/metrics.md (verified present on disk), with external doc links organized in a References section, matching the score-3 anchor.

'Authors and runs Ragas' plus an enumerated taxonomy of metric families (Faithfulness, Context Precision/Recall, Tool Call F1, Aspect Critic) lists multiple concrete actions, matching the score-3 anchor and exceeding the domain-only score-2 anchor.

It explicitly answers both what ('authors and runs Ragas … with metrics organized into …') and when via an explicit 'Use when the user evaluates a RAG pipeline …' clause, satisfying the score-3 anchor.

Natural trigger terms a RAG-eval user would say are present — 'evaluates a RAG pipeline (retriever + generator)', 'LLM-eval', 'metric variety' — giving good coverage rather than a single keyword.

Scoped to RAG pipelines and positioned as 'deepest metric variety in the OSS LLM-eval space', with correct third-person voice, giving it a clear niche unlikely to trigger for the wrong skill.