Content
90%Weight 40%Scale 1-5Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
An efficient, highly actionable skill body: executable commands, concrete good/bad examples, and project-specific gotchas with zero padding. The only gaps are the absence of an error-recovery loop for benchmark runs and a single unverifiable external reference.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Lean and dense with project-specific knowledge only (workspace-limit gotcha, backend proxy requirement, .env key handling); no padding and no explanation of concepts Claude already knows. Every token earns its place. | 5 / 5 |
Actionability | Copy-paste ready commands for install, listing models/cases, and running with the required env vars, plus concrete good/bad prompt and judge-checklist examples. Fully executable guidance covering the common cases. | 5 / 5 |
Workflow Clarity | Clear run sequence (install, list, run with backend env vars) and well-structured authoring rules; the harness's validation ('deterministic validation, then LLM judging') is described but no operator error-recovery feedback loop is given. Not 5 for the missing validation checkpoint guidance; well above 3 because the sequences themselves are explicit. | 4 / 5 |
Progressive Disclosure | Well-organized sections with case-format detail deferred via a clearly signaled one-level reference ('See ai_evals/README.md for the full case format'). Not 5 because no bundle files exist to verify the referenced path resolves, a minor organization gap. | 4 / 5 |
Total | 18 / 20 Passed |