Conciseness

The body is a lean, directive list ('ALWAYS include meaningful baselines', 'Use MULTIPLE random seeds (minimum 3, ideally 5)', 'change only ONE thing per comparison') with no padding or explanation of concepts Claude already knows, matching the lean-and-efficient anchor; it never drifts toward the verbose level-1 example.

Actionability

Although there is no code, this is an instruction/best-practice skill and the guidance is concrete and actionable with specific thresholds ('minimum 3, ideally 5 seeds', 'at least one classical method baseline', 'Report mean +/- std'), so per the code_vs_instruction scoring note the absence of code is not penalized; it stays above level 2 because the directives are complete rather than missing key details.

Workflow Clarity

The numbered items list clear practices but they are parallel best-practice directives rather than a tightly sequenced process, and there are no validation/feedback checkpoints (e.g., verify baselines reproduce before reporting), matching the 'steps listed but validation gaps; checkpoints missing or implicit' anchor; it is below level 3 which requires explicit validation steps and error-recovery loops.

Progressive Disclosure

The skill is under 50 lines, single-purpose, needs no external references, and is organized with one clear heading and a numbered list, satisfying the simple-skills scoring note that progressive disclosure can score 3 with just well-organized sections; it is above level 2 because there is no inline bloat or poorly-signaled referencing.

Specificity

Quotes 'Best practices for designing reproducible ML experiments' and 'planning ablations, baselines, or controlled experiments' — it names the domain and several concrete activities, but the lead 'best practices for designing' framing is more abstract than a list of concrete capabilities, matching the 'Names domain and some actions, but not comprehensive' anchor rather than the multiple-specific-actions level 3.

Completeness

It answers both 'what' ('Best practices for designing reproducible ML experiments') and 'when' with an explicit 'Use when...' clause, matching the clearly-answers-both-what-AND-when anchor; it is above level 2 because the trigger is explicit rather than implied.

Trigger Term Quality

The 'Use when planning ablations, baselines, or controlled experiments' clause supplies natural terms an ML practitioner would actually say, giving good coverage of common variations; it is not level 2 because the terms are not merely 'some relevant keywords' but a genuine spread of user-facing triggers.

Distinctiveness Conflict Risk

The niche (reproducible ML experimental design) and its triggers (ablations, baselines, controlled experiments) are distinctive and unlikely to fire for unrelated skills, matching the clear-niche-with-distinct-triggers anchor.