The body is code-forward and assumes Claude's competence — it does not patronizingly explain what JSONL, cosine similarity, or regression suites are, and uses inline doc-citation links to offload provenance rather than prose. It is not the score-2 case of unnecessary explanation; for a six-step complex skill each section earns its place.

It provides fully executable, copy-paste-ready guidance throughout — promptfoo YAML configs, DeepEval GEval constructors, CLI commands, a runnable regression_diff.py script, and a GitHub Actions workflow — matching the score-3 anchor rather than the pseudocode/describes-but-doesn't-instruct lower anchors.

Six steps are explicitly sequenced (structure dataset -> configure eval -> layer assertions -> capture baseline -> diff candidate vs baseline -> CI gating) with validation checkpoints: baseline-as-floor capture, a diff script that sys.exit(1) on regression, and a --filter-failing feedback loop for investigation, matching the score-3 anchor with explicit validation and error-recovery feedback.

The body is a large single-file document (~360 lines) with well-organized sections but no bundle files in references/, scripts/, or assets/; content that could be split — notably the inlined scripts/regression_diff.py referenced by name and the full assertion-layer examples — stays inline. This fits the score-2 anchor (some structure, content that should be separate is inline) rather than the score-3 anchor requiring well-signaled one-level-deep reference files.

The description lists multiple concrete actions ('structures a versioned JSONL/CSV golden dataset, configures deterministic eval runs (temperature 0, seed), wires assertion layers (exact, semantic similarity, LLM-as-judge, rubric), enforces a pass-rate threshold with diff reporting... and gates CI on regression'), matching the score-3 anchor of multiple specific concrete actions rather than the domain-only score-2 anchor.

It explicitly answers both what (the full build/gate workflow) and when via an explicit 'Use when upgrading an LLM provider model and needing a repeatable before/after quality gate, or when a prompt regression suite must track output quality across model versions over time' clause, matching the score-3 anchor rather than the score-2 anchor where 'when' is only implied.

Natural user-facing terms are covered ('upgrading an LLM provider model', 'before/after quality gate', 'prompt regression suite', 'output quality across model versions'), which a user would plausibly say when needing this skill; not merely technical jargon as in the score-1 anchor.

The niche is narrow and specific — versioned golden-dataset regression across model upgrades with baseline diffing and CI gating — with triggers unlikely to fire for general eval skills; it is not generic like the score-1 anchor 'Helps with code and documents'.