Content
82%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.
A well-structured, highly actionable skill body with copy-paste commands and concrete authoring guidance. The main gaps are minor framing verbosity and the absence of an explicit user-facing validation feedback loop.
Suggestions
Trim framing prose like "The goal is to test current production guidance with realistic user requests — not to pin one exact implementation shape." to tighten conciseness.
Add an explicit run-then-inspect-failures feedback loop (e.g., re-run only failing case IDs after fixing prompts) to strengthen workflow clarity for the batch run operation.
Consider a short pointer to where the judge model/key is configured so the separate Anthropic judge call is discoverable without reading the README.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is lean and mostly assumes competence (concrete commands, Good/Bad prompt lists), with only minor framing prose such as "The goal is to test current production guidance..." that could be trimmed. | 4 / 5 |
Actionability | Provides copy-paste ready, executable commands (bun install, bun run cli -- run global ...), concrete env-var setup, and concrete Good/Bad prompt examples covering common authoring cases. | 5 / 5 |
Workflow Clarity | A clear run sequence (install -> list models/cases -> run with env vars) and a numbered authoring-rules list are present, but there is no explicit validate/fix/retry feedback loop for the user since validation is internal to the runner. | 4 / 5 |
Progressive Disclosure | Well-organized into clearly headed sections (Running benchmarks, Authoring core rules, Prompt writing, Deterministic validation, Judge checklist) with a single well-signaled one-level reference to ai_evals/README.md; no bundle files exist to split further. | 4 / 5 |
Total | 17 / 20 Passed |