Content
57%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-structured skill that effectively organizes a complex methodology (EARS-based prompt optimization) with good progressive disclosure to reference files. Its main weaknesses are moderate verbosity (the attribution paragraph, some redundant explanations), lack of validation checkpoints in the workflow, and actionability that leans more toward framework/template guidance than fully concrete, copy-paste-ready examples. The domain theory matching step in particular remains somewhat abstract.
Suggestions
Add explicit validation checkpoints between workflow steps, e.g., 'Verify each EARS statement is atomic and testable before proceeding to Step 3' and 'Review enhanced prompt against quality criteria; if any criterion fails, iterate on the relevant step.'
Remove the attribution paragraph and tighten the overview — Claude doesn't need to know the methodology's inspiration source to apply it effectively.
Make Step 3 (Domain Theories) more actionable by providing a concrete worked example showing how a theory maps to specific EARS requirements, rather than just listing the selection process abstractly.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is reasonably well-structured but includes some unnecessary verbosity. The attribution paragraph and some explanatory text (e.g., explaining what EARS stands for, the domain theory selection process) could be tightened. The methodology overview repeats information that appears in the detailed steps. However, it's not egregiously padded. | 2 / 3 |
Actionability | The skill provides concrete examples of EARS transformations (before/after for a reminder app) and a clear transformation checklist, which is good. However, the guidance is more of a meta-framework for prompt optimization rather than executable code/commands. The output templates use markdown placeholders rather than fully worked examples, and the domain theory matching process remains somewhat abstract ('match to 2-4 complementary theories'). | 2 / 3 |
Workflow Clarity | The six-step workflow is clearly sequenced and logically ordered. However, there are no explicit validation checkpoints or feedback loops — no step says 'verify the EARS transformation is complete before proceeding' or 'if the enhanced prompt doesn't meet quality criteria, iterate on steps 2-4.' For a multi-step process that transforms requirements, missing validation/iteration guidance is a notable gap. | 2 / 3 |
Progressive Disclosure | The skill effectively uses progressive disclosure with a clear overview in the main file and well-signaled references to four specific reference files (ears_syntax.md, domain_theories.md, examples.md, advanced_techniques.md). The 'When to load references' section provides clear guidance on when to access each file. References are one level deep and clearly organized. | 3 / 3 |
Total | 9 / 12 Passed |