Content
85%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
The body is tight, project-specific, and well-structured with an explicit before/after validation loop, but it stops short of giving the concrete benchmarking steps needed to fully execute its own core workflow.
Suggestions
Inline a minimal benchmark command or the essential ai-evals invocation shape so the workflow is executable without hopping skills.
Add a short before/after checklist or 'definition of done' (e.g. finalContextTokens delta reported) to make the validation step unambiguous.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Lean and project-specific — per-iteration overhead, the shared-refactor regression trap, and 'finalContextTokens first' are non-obvious insights Claude would not already know, with no basic-concept padding. | 3 / 3 |
Actionability | Concrete invariants are present ('return { success, message }', 'Strip dead params', optimize finalContextTokens first) with named files/functions, but the core benchmarking execution is deferred to the 'ai-evals' skill, leaving key steps unstated. | 2 / 3 |
Workflow Clarity | A clear sequence with an explicit validation checkpoint is given: 'Run the affected mode before your change and after, same model(s), same cases', plus ordering ('window first, cumulative second'). | 3 / 3 |
Progressive Disclosure | Under 50 lines with four well-organized section headers and a single one-level-deep external reference (the 'ai-evals' skill), which fits the simple-skill allowance. | 3 / 3 |
Total | 11 / 12 Passed |