What 'quality-tested' actually means for a library of 394 AI skills

A library of 394 free Claude skills claims 'all quality-tested, mean 4.38/5', backed by a seven-dimension evaluation framework. Skills must pass binary assertions and a graded rubric using a model-as-judge to reach 'stable' status. The framework is documented in the repo, not marketing, and aims to provide a repeatable quality bar for media professionals.

"Quality-tested" is the kind of phrase that usually means nothing. Every tool claims it. Most mean "we tried it once and it didn't crash." So when a library of 394 free Claude skills puts "all quality-tested, mean 4.38/5" on the tin, the fair response is: prove it. Here's exactly what the claim means, including where it's soft. stable only if it clears two bars Every skill carries a status. To reach stable — the only status the library promotes — it has to pass a seven-dimension evaluation: The library mean across all stable skills is 4.38. The whole framework — dimensions, thresholds, the banned-phrase list — is in the repo, not a marketing page. Code eval is binary: it runs or it doesn't. Prose has no green checkmark, so the library tests in two layers. First, binary assertions catch the mechanical failures — did it produce the required sections, did it refuse to fabricate a quote with no source. Across thousands of these the pass rate is high, and the few "failures" were skills correctly refusing to invent content on deliberately thin inputs — the behaviour you want. Second, the graded rubric above handles the judgment calls binary checks can't. The graded scoring uses a model as judge, and models are generous: they tend to like fluent text, including fluent AI text. So the scores are treated as a filter, not a verdict . Three things keep them honest: It is not a guarantee every output is perfect. It's a documented, repeatable bar that's a lot higher than "we tried it once." Because the audience is media professionals, and they detect generic instantly. A skill library for people who notice bad writing has to be testable on exactly that axis, or the whole premise collapses. The eval framework isn't a credential — it's the thing that makes "doesn't sound like AI" a claim you can check instead of a vibe. Open the repo, open any skill, read its example, and judge for yourself. That's the test that matters.