Conciseness

The body is mostly efficient: the 'The model:' summary, the dataset-field table, and the anti-patterns table all earn their tokens, and there is no re-explanation of concepts Claude already knows. It sits at anchor 4 rather than 5 because the verbatim Step 2 quickstart carries boilerplate (the asyncio main/__main__ scaffolding) that could be trimmed to just the DiscreteMetric pattern.

Actionability

Concrete, executable guidance throughout: install commands, a complete DiscreteMetric example, an EvaluationDataset.from_list example, and a CI snippet with threshold asserts. It matches anchor 4 rather than 5 because the judge-LLM setup (llm_factory) is shown only for the custom-metric path and never wired into the primary evaluate() flow, leaving a minor gap in copy-paste readiness for the main use case.

Workflow Clarity

A clear six-step sequence (Install, metric pattern, pick metrics, dataset shape, framework integration, CI) with threshold asserts as checkpoints and an anti-patterns table that covers error recovery (e.g., NaN when retrieved_contexts is missing). This is anchor 4 rather than 5 because validation guidance is preventive and tabular rather than an explicit inline validate-then-fix-then-retry loop within the step sequence.

Progressive Disclosure

The body is an overview that moves the 30+ metric catalog into references/metrics.md (verified to exist), clearly signaled with a markdown link and source attribution, exactly one level deep. This matches the top anchor for a clear overview with well-signaled, one-level-deep references and easy navigation.

Specificity

The description lists multiple concrete actions ('scoring an answer against its contexts', 'telling retrieval problems from generation problems after a change such as chunk size', 'which metrics to use') and comprehensively enumerates the metric names (faithfulness, response relevancy, context precision and recall, noise sensitivity, factual correctness, tool-call accuracy, agent goal accuracy). This matches the anchor for multiple specific concrete actions with comprehensive coverage; it is not the level below because coverage of the domain's capabilities is thorough rather than minor-gapped.

Completeness

It explicitly answers 'when' with a 'Use for...' clause containing concrete trigger phrases ('any question or task about RAG or agent evaluation metrics', 'after a change such as chunk size') and 'what' via the enumerated scoring capabilities and metric names. This matches the top anchor for both what and when with concrete triggers; it is not the level below because the 'when' is explicit and specific rather than merely adequate.

Trigger Term Quality

Good natural keyword coverage: 'RAG', 'agent evaluation metrics', 'faithfulness', 'context precision and recall', 'chunk size', and the tool name 'Ragas' itself. It falls between the 4 and 5 anchors because a few natural terms users would plausibly say are missing (e.g., 'hallucination', 'groundedness', 'RAG eval'), so it lacks the synonym-level comprehensiveness of the top anchor.

Distinctiveness Conflict Risk

It carves a clear niche (RAG/agent evaluation via Ragas) with triggers anchored on Ragas-specific metric names (noise sensitivity, agent goal accuracy, tool-call accuracy), and the skill body explicitly scopes sister skills to non-RAG prompt evals and pytest-native evals. Conflict risk is minimal, matching the top anchor; it is not the level below because the enumerated metric names make mistaken triggering unlikely.