Content
85%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is a well-crafted skill with strong actionability, clear workflow sequencing, and excellent progressive disclosure to supporting references and related skills. The content is mostly concise but could trim some explanatory text about what each build type measures, as Claude can infer this. Overall, it's a high-quality skill that provides clear, executable guidance for a complex multi-step benchmarking process.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is mostly efficient and avoids explaining basic concepts, but some sections could be tightened. For example, the explanation of what zero-change builds measure and what cached clean builds represent adds useful context but borders on verbose for Claude. The 'Inputs To Collect' section is somewhat padded. | 2 / 3 |
Actionability | Provides a concrete executable command with specific flags, clear workflow steps with exact iteration counts, specific flag names like `--no-cached-clean` and `--touch-file`, and precise output requirements (median, min, max). The benchmark_builds.py invocation is copy-paste ready. | 3 / 3 |
Workflow Clarity | The workflow is clearly sequenced (warm-up → clean builds → cached clean builds → zero-change builds → incremental builds → save → report) with explicit validation (warm-up build to validate command succeeds), conditional steps (compilation cache detection), and clear stopping criteria. The worktree consideration adds an important pre-flight check. | 3 / 3 |
Progressive Disclosure | Excellent structure with a clear overview in the main file and well-signaled one-level-deep references to benchmarking-workflow.md, benchmark-artifacts.md, the JSON schema, and related specialist skills. Content is appropriately split between the skill overview and reference materials. | 3 / 3 |
Total | 11 / 12 Passed |