Three quality signals
Only present signals are blended; their weights are rescaled to total 100%. A skill with benchmark and live evaluation evidence therefore gives each 50% of its score. Missing evidence does not count as a failed task.
A skill without eligible evidence shows New. A skill needs at least one signal to earn a visible score; the breakdown shows which signals support it. Leaderboard rows supported by only one signal are marked provisional.
Adoption is not a score input. Running a skill more often cannot improve its quality score without new quality evidence. User stars are also separate from the AI rating.
Usage and quality answer different questions
The public page’s Usage section shows:- Users: all-time distinct users who used the skill, across versions. A user who used it repeatedly counts once. Removing the user’s account does not remove their anonymous usage credit.
- Times used: all-time distinct skill invocations. Retrying delivery or evaluation of the same invocation does not count again.
- “84% · 50 evaluated runs · last 30 days” means 42 of 50 qualified evaluated runs passed.
- “8.2/10 · 30 AI-rated runs · last 30 days” is the mean AI quality rating of 30 distinct rated runs.
What qualifies as quality evidence
A completed request, nonempty output, acceptable latency, or successful tool call is not by itself a task-quality evaluation. Live evidence requires a task-quality evaluator with recorded model and rubric provenance. Infrastructure failures and runs without a judgeable task result do not become quality samples. An AI rating requires a valid 1–10 rating tied to a skill that was actually witnessed in the trace, with a recorded judge model and prompt hash. Repeated entries or repeated submissions for the same invocation do not create extra samples. Historical task grades can be recovered when their exact rubric and witnessed execution are provable; an unconfirmed historical judge model remains unknown. Customer-supplied grades retain their externally reported provenance; naming a model does not make the report a platform-verified benchmark. Evaluations and ratings are attributed to the source skill witnessed at execution time. Renaming a local copy, changing a subscription or uninstalling it later cannot move the earlier result to another source. Deleting private trace content preserves only the minimal anonymous credit and qualified grade needed for the public aggregate. The 30-day quality window uses the invocation’s UTC date, rather than the date on which a delayed judge finished. Late evaluation does not make an old run appear recent. When evidence expires, the sample and score eligibility expire together.Public reputation and your team’s results
An editable fork starts fresh because its contents can diverge. Qualified use of an unchanged public source can also contribute to that source’s public aggregate through its execution-time attribution.
For a public skill, your results show your team’s own pass rate, usage and ratings alongside the public SkillScore. They help you assess your workload without replacing the public aggregate.
Refresh and leaderboard behavior
New qualified grades publish an updated source aggregate and score together. A periodic refresh also removes expired evidence. The detail page, score breakdown and leaderboard use the same qualified quality samples. Only verified benchmark runs feed the benchmark signal. Self-reported benchmark numbers that have not passed verification do not qualify. Turn and token savings at equal correctness remain a separate efficiency measure. The leaderboard ranks by its selected evidence or quality axis. Read the signal breakdown and sample sizes alongside the score; a single benchmark and three corroborating signals provide different amounts of evidence even when their scores match.Related
Public Registry
Find and install public skills.
Skill evaluation
Measure the with-versus-without comparison.
Skills observability
Inspect your usage receipts and evaluated traces.