Conciseness

The body is dense and free of padding, assuming Claude's competence (e.g., no explanation of what a dataset or prompt is; the opening definition of "experiment" is domain-specific, not generic filler). A few rhetorical sentences like "Experiments turn 'this prompt feels better' into evidence" could be trimmed, matching anchor 4 ("minor instances of over-explanation") rather than 5.

Actionability

Concrete decision rules are present (context belongs in `input` never `reference`, change exactly one axis, trust aggregates only at zero errors, read metadata then write back the whole object), but there are no executable mechanics anywhere: no commands, API calls, or code for actually running, reading, or comparing experiments — all delegated to sibling skills. Anchor 3 ("concrete guidance but incomplete; missing key details") fits better than 4, which requires concrete code or commands.

Workflow Clarity

A nine-step sequenced workflow with explicit validation checkpoints ("Make sure the starting prompt is well formed before running it", "Trust aggregates only when the run is complete with zero errors"), a genuine feedback loop (step 9's hypothesis → run → compare → report iteration), and a closing "Things To Avoid" checklist. The batch-operation validation cap does not apply since the zero-error checkpoint is explicit — matches anchor 5.

Progressive Disclosure

No bundle files exist, and the ~80-line body is cleanly sectioned (Before You Start / Workflow / Recording / Boundaries / Avoid) with mechanics appropriately delegated to sibling skills rather than inlined. It exceeds the under-50-line simple-skill exemption, so not a 5, but nothing is inlined that clearly belongs in a separate file — anchor 4 ("good structure; most content appropriately placed; minor organization gaps").

Specificity

"Run, read, and compare dataset-backed experiments" plus "read experiment quality/latency/cost" and "decide whether a change actually helped" name multiple specific concrete actions that comprehensively cover the skill's domain. Fits anchor 5 better than 4, since no capability area of the skill is left uncovered at the description level.

Completeness

Both what ("Run, read, and compare dataset-backed experiments") and when ("Trigger when the user wants to iterate over a dataset with experiments...") are explicit, and it goes further with load timing ("load this skill when dataset-backed work begins... before starting the recorded run, not only when reading results") and an explicit Do-NOT-trigger list. Clearly matches anchor 5.

Trigger Term Quality

Natural user phrases are well covered ("iterate over a dataset with experiments", "compare experiment runs", "decide whether a change actually helped"), but common synonyms such as "evals", "benchmark", or "A/B test" are missing. Anchor 4 ("good keyword coverage; a few natural terms missing") is the closest fit.

Distinctiveness Conflict Risk

The explicit negative-trigger list disambiguates three adjacent skills by name (`playground`, `evaluators`, `phoenix-error-analysis`), giving a clear dataset-backed-experiment niche with minimal conflict risk. Matches anchor 5.