The body is lean and assumes Claude's competence; it avoids explaining what AI/evals are conceptually and every section earns its place, fitting the 'every token earns its place' anchor.

Provides a concrete report template with exact sections, a worked input/output example, a copy-ready template asset, and specific checks ('Every number has a baseline, a comparison point, and a target'). Per scoring notes, absence of code is not penalized for instruction-only skills.

A 6-step sequenced workflow with a copy-paste checklist, plus an explicit validate→fix→re-check feedback loop ('Fix every miss, then re-check. Only deliver when all checks pass') matches the top anchor for clear sequences with checkpoints.

Clear overview with one-level-deep, well-signaled references — golden-dataset.md, metrics.md, stakeholder-explainer.md, report-template.md (all verified to exist) — each linked at the point of need and summarized in a Reference files section.

Lists multiple concrete actions — 'designs golden datasets, defines accuracy metrics, tracks quality across iterations, and produces stakeholder-ready summaries' — matching the highest anchor for specific capabilities.

Clearly answers both 'what' (generates reports, designs datasets, defines metrics) and 'when' via an explicit 'Use when...' clause with multiple triggers, satisfying the top anchor.

Includes natural user phrasings a person would actually say, e.g. "how good is our AI", "accuracy report", "eval results", "benchmark the model", giving strong trigger coverage.

Has a clear AI-eval-reporting niche with distinct triggers ('how good is our AI', 'benchmark the model') unlikely to fire for unrelated skills.