Content
87%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
A compact, well-structured analysis workflow that respects token budget and gives a concrete framework. The main gap is the absence of explicit validation/verification checkpoints in the multi-step process.
Suggestions
Add an explicit validation checkpoint (e.g., verify files are complete and metrics are present before building the comparison table) and a feedback loop for handling missing or malformed results.
Specify how to parse the JSON/CSV results (e.g., a concrete command or library) so the "Locate Results" step is copy-paste ready rather than directional.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Lean and efficient — uses terse bullets, names specific directories and metrics, and assumes Claude's competence without explaining basic concepts like JSON or statistics. | 3 / 3 |
Actionability | Provides a concrete framework for an instruction-only task: specific directories to check, variables to organize by, statistical practices, and a four-part insight structure with a defined output format. | 3 / 3 |
Workflow Clarity | Five steps are clearly sequenced, but there are no explicit validation checkpoints or feedback loops; checks like "flag outliers" are implicit rather than structured validate-then-retry gates. | 2 / 3 |
Progressive Disclosure | A short (<50 line), single-purpose skill with well-organized sections and no need for external references, which satisfies the simple-skill allowance for a top score. | 3 / 3 |
Total | 11 / 12 Passed |