Conciseness

Tables and steps are lean and assume Claude's competence (no explanation of pytest or RAG basics), but the "Per [de-gh][de-gh]" attribution is repeated roughly seven times, including a standalone "(Per [de-gh][de-gh].)" line, which is trimming-able padding.

Actionability

Fully executable throughout: "pip install -U deepeval", the verbatim quickstart test with LLMTestCase/GEval/assert_test, "deepeval test run test_chatbot.py", a copy-paste custom GEval example, an evaluate() batch example, and a CI command with --run-async --workers 4 covering the common cases.

Workflow Clarity

Steps 1-7 are clearly sequenced (install, first test, test-case fields, metric catalog, custom GEval, batch evaluation, CI) with an explicit exit-code checkpoint ("pytest exit code propagates: nonzero if any assert_test fails") and an anti-patterns table of fixes, but there is no error-recovery feedback loop (e.g., what to do when a judge metric errors or a threshold fails in CI).

Progressive Disclosure

A well-organized single-file skill with clear section headers, flat one-level references, and no nested reference chains; at ~185 lines it exceeds the simple-skill exception, and the metric catalog or CI detail could arguably live in a separate reference file.

Specificity

Enumerates concrete capabilities: "DeepEval LLMTestCase, built-in metrics such as G-Eval, answer relevancy, faithfulness, contextual recall and precision, hallucination, bias and toxicity, deepeval test run with assert_test, and the Confident AI dashboard" — a comprehensive inventory of the library's surface with no significant gaps, matching the 'comprehensive coverage' anchor.

Completeness

Explicitly answers 'when' with concrete triggers ("Use for any question or task about testing an LLM or RAG app with pytest, including hallucination checks in CI...") and 'what' via the detailed tool and metric enumeration.

Trigger Term Quality

Natural user phrasings like "testing an LLM or RAG app with pytest", "hallucination checks in CI", and "answers that invent facts missing from the retrieved documents" are present, but common variations such as "LLM evaluation", "evals", or "LLM-as-judge" are missing.

Distinctiveness Conflict Risk

Clear niche of pytest-based LLM evaluation anchored by named, distinctive triggers (DeepEval, assert_test, Confident AI dashboard); only minor overlap risk with closely related RAG-eval sister skills.