Conciseness

The body is efficient — lean tables, copy-paste code, no explanations of basics Claude already knows — but repeated 'Per [de-gh][de-gh]' attribution lines, the Overview doc-quote, and the filler '(Per [de-gh][de-gh].)' line after the run command are trimmable, matching anchor 4 rather than the every-token-earns-its-place anchor 5.

Actionability

Fully executable throughout: 'pip install -U deepeval', 'deepeval login', a complete verbatim quickstart (imports, GEval with criteria/params/threshold, LLMTestCase with populated fields, assert_test), a run command, a full custom-GEval snippet, and a CI command with flags. Only Step 6's evaluate() call uses illustrative placeholders, comparable to the illustrative line in the anchor-5 example, so this is not the minor-gaps state of anchor 4.

Workflow Clarity

Steps 1-7 are clearly sequenced (install → first test → fields → metrics → custom → batch → CI) with validation signals ('pytest exit code propagates: nonzero if any assert_test fails', the score ≥ threshold pass rule, and an Anti-patterns table with fixes). It lacks an explicit error-recovery loop for failed metrics, so it fits anchor 4 rather than the feedback-loop-complete anchor 5; the batch-operation cap does not apply since assert_test plus exit-code propagation is built-in validation.

Progressive Disclosure

No bundle files exist, and the single-file structure is well-organized with clear section headers, tables, and one-level-deep references (external URLs and sister skills) with no nesting. The ~180-line body inlines the metric catalog — reasonable as a decision guide, but it could split into a references file as the skill grows, matching anchor 4's 'minor organization gaps' rather than the fully-split anchor 5.

Specificity

Lists ~10 concrete named components ("LLMTestCase", "G-Eval", "answer relevancy, faithfulness, contextual recall and precision, hallucination, bias and toxicity", "assert_test", "Confident AI dashboard") but presents them as a noun catalog with minor gaps (no batch evaluate or custom-criteria mention), matching anchor 4 rather than the comprehensive action-verb coverage of anchor 5.

Completeness

Explicitly answers both: "Use when LLM or RAG evaluation should run as pytest tests" (when) plus a detailed capability list (what). The when-clause is a single condition without additional concrete trigger phrases, so it sits at anchor 4 rather than the multi-trigger anchor 5 and well above the when-missing anchor 3.

Trigger Term Quality

Natural phrases like "LLM or RAG evaluation", "pytest tests", "faithfulness", "hallucination", and "Confident AI dashboard" give good keyword coverage, but common variations such as "LLM-as-judge", "benchmark", or "eval framework" are missing, fitting anchor 4 rather than the synonym-complete anchor 5.

Distinctiveness Conflict Risk

DeepEval, pytest integration, and Confident AI pin a clear niche with distinct triggers, but the generic opening "LLM or RAG evaluation" overlaps the domain of closely related sister skills (ragas-evaluation, promptfoo-evaluation), placing it between the minor-overlap anchor 4 and the minimal-conflict anchor 5.