Conciseness

The body is dense with repo-specific facts (exact paths, commands, canonical example files) and explains nothing Claude already knows, but the ~25-line "Promoted dataset evaluators" section and the "labels"/"substitutions" decision points restate promotion logic that could be tightened. Fits anchor 4 ("efficient; minor instances of over-explanation that could be trimmed"), not 5.

Actionability

Fully executable guidance throughout: exact file paths for every artifact, runnable commands (make codegen-prompts, cd js && pnpm build, the grep one-liner, the vitest invocation), and named canonical files to copy from (correctness.py, createCorrectnessEvaluator.ts, faithfulness.mdx). Copy-paste ready and covers the common cases, matching the 5 anchor.

Workflow Clarity

A linear Steps 0–8 pipeline with explicit checkpoints ("Verify the generated files look correct before moving on", "Fix any TypeScript errors before proceeding"), a genuine feedback loop (failed-examples printer → adjust prompt or benchmark → iterate to >80% accuracy), a completion checklist, and a retrospection step for drift. Matches the 5 anchor including error-recovery loops.

Progressive Disclosure

No bundle files exist and the ~210-line body is well-sectioned with the sequential workflow content appropriately inline; however, the promotion criteria/examples and the docs-section table are separable reference material that could live in a reference file, and there are no external references at all. Fits anchor 4 (good structure, minor organization gaps), not 5 which requires well-signaled one-level-deep references or a short skill needing none.

Specificity

"Add a new built-in (pre-built) LLM classification evaluator to the Phoenix evals libraries" and "promote it to a server-side dataset evaluator" name multiple concrete actions in third person that comprehensively cover everything the skill does. Not 4: there is no gap in coverage of the skill's scope.

Completeness

Both questions are answered explicitly: the "what" (add a built-in evaluator, optionally promote it) and the "when" ("Use when working in the Phoenix repo and asked to add a new built-in eval... or to promote an existing one to the dataset evaluators UI"), plus a concrete exclusion clause. Matches the 5 anchor example; not 4 because the triggers are fully explicit.

Trigger Term Quality

"built-in eval, pre-built metric, builtin evaluator, or LLM-as-a-judge template" explicitly enumerates the synonyms a user would naturally say, alongside package names (arize-phoenix-evals / @arizeai/phoenix-evals) and "dataset evaluators UI". Not 4: common variations are already listed rather than a few missing.

Distinctiveness Conflict Risk

Scoped to the Phoenix repo and named evals packages with a clear niche, and "Do not use for writing a user's own custom evaluator for their app (use phoenix-evals)" actively disambiguates from the nearest competing skill. Minimal conflict risk; not 4 because the overlap risk is explicitly foreclosed.