Conciseness

The body is dense with tool-specific facts and executable code, with no explanation of concepts Claude already knows. Minor trimmable parts remain — the 'When to use' bullets partially restate the description, and the full 40-line CI yaml and 20-line diff script are inlined — so it is efficient but does not meet 'every token earns its place'.

Actionability

Every step provides copy-paste ready material: JSONL/CSV dataset formats, promptfoo provider YAML, DeepEval metric constructors, bash eval commands, the regression diff script, and a complete GitHub Actions workflow, covering both tool paths and the common cases. The only hand-waved step ('Generate actual_output from baseline model') is explicitly delegated to the per-tool skills.

Workflow Clarity

Six clearly sequenced steps with explicit validation checkpoints: baseline pass rate as the floor, dataset changes gated on a version bump plus CI baseline capture before becoming 'golden-current', a ratio threshold with non-zero exit on regression, and CI failing on threshold drop. Feedback loops exist ('--filter-failing' for targeted investigation) and the anti-patterns table acts as a checklist.

Progressive Disclosure

Good structure with well-signaled one-level-deep external references and a closing References section, but the skill is a single file with no bundle: the full CI workflow yaml and diff script are inlined bulk content, and the CI example invokes 'scripts/regression_diff.py' which is not actually bundled (no scripts/ directory exists). These minor organization gaps place it below the 'content appropriately split' anchor.

Specificity

Lists six-plus concrete actions — 'Builds a versioned golden dataset, deterministic runs, exact, semantic, judge and rubric checks, and a pass-rate CI gate with a baseline diff across model versions' — comprehensively covering the skill's pipeline. This matches the comprehensive-coverage anchor rather than the 'minor gaps' anchor at 4.

Completeness

Explicitly answers 'when' with 'Use for any question or task about catching LLM quality regressions, including checking a new model version or provider against the old one and prompt tweaks that break other cases' and 'what' with the concrete build actions. Both are present with concrete trigger phrases, matching the top anchor.

Trigger Term Quality

Includes natural user phrases such as 'catching LLM quality regressions', 'checking a new model version or provider against the old one', and 'prompt tweaks that break other cases'. Coverage is good but not comprehensive — common variations like 'model upgrade', 'evals', or 'before/after comparison' are missing, so it sits above anchor 3 but below anchor 5.

Distinctiveness Conflict Risk

The regression-gating niche (golden dataset, baseline diff, CI pass-rate gate) is distinct, but the broad framing 'any question or task about catching LLM quality regressions' leaves minor overlap risk with closely related evaluation skills like promptfoo-evaluation and deepeval-evaluation. Mostly distinct with minor overlap, matching anchor 4 rather than the minimal-conflict anchor 5.