Content
72%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
Well-organized and concise package-specific reference, but the inline code is only a sketch and the guidance is decision-oriented rather than a sequenced, validated workflow. Strengthening executable examples and adding a verification step would raise the lower dimensions.
Suggestions
Replace the partial Core Pattern snippet with a complete, copy-paste-ready example (define `options`, show the surrounding Result context) or inline a runnable example from `examples/`.
Add a brief sequenced workflow for the common task (e.g., choose routing strategy -> configure stats store -> wire balancer -> verify behavior) with an explicit validation/checkpoint step.
Show at least one concrete usage line for key API surface entries (e.g., how to construct `AxBalancerStatsStore` or call `set_usage_observer`) instead of listing names only.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Dense but lean: every line conveys package-specific knowledge Claude lacks (AxBalancer, AxBalancerAdaptiveStrategy, Thompson sampling, AxIR) with no padding about basic concepts and no time-sensitive dates in the body. | 3 / 3 |
Actionability | The Core Pattern is a non-executable sketch ("let llm = ai("openai", options)?;" with undefined `options` and no Result context) and the API surface is a list of names rather than usage; full runnable examples are only deferred to examples/. | 2 / 3 |
Workflow Clarity | Clear decision guidance (router vs balancer vs adaptive) is provided, but there is no sequenced multi-step workflow with explicit validation checkpoints or a fix-retry feedback loop. | 2 / 3 |
Progressive Disclosure | A single, well-organized SKILL.md with clearly labeled sections and no nested references; the mentioned files (API.md, examples/) are package artifacts rather than deep skill-internal references. | 3 / 3 |
Total | 10 / 12 Passed |