prompt-optimizer
Transform vague prompts into precise, well-structured specifications using EARS (Easy Approach to Requirements Syntax) methodology. This skill should be used when users provide loose requirements, ambiguous feature descriptions, or need to enhance prompts for AI-generated code, products, or documents. Triggers include requests to "optimize my prompt", "improve this requirement", "make this more specific", or when raw requirements lack detail and structure.
88
83%
Does it follow best practices?
Impact
96%
1.62xAverage score across 3 eval scenarios
Passed
No known issues
Quality
Discovery
82%Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.
This is a solid description that clearly communicates both what the skill does and when to use it, with good trigger terms that match natural user language. Its main weaknesses are a lack of multiple specific concrete actions (it mostly describes one high-level transformation) and some potential overlap with general prompt improvement or writing enhancement skills. The EARS methodology mention helps differentiate it but may not be widely recognized by users.
Suggestions
Add more specific concrete actions beyond 'transform' — e.g., 'categorize requirements by type (ubiquitous, event-driven, state-driven), identify ambiguities, generate structured requirement statements'
Narrow the scope or add clearer boundaries to reduce conflict risk — e.g., specify that this is for software/system requirements specifically, not general writing improvement
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | The description names the domain (prompt/requirement transformation) and mentions the EARS methodology, but the concrete actions are limited to 'transform vague prompts into precise, well-structured specifications' — it doesn't list multiple distinct actions like parsing, structuring, categorizing, or outputting in specific formats. | 2 / 3 |
Completeness | Clearly answers both 'what' (transform vague prompts into precise specifications using EARS methodology) and 'when' (explicit 'Use when' equivalent with trigger phrases and situational descriptions like 'when raw requirements lack detail and structure'). | 3 / 3 |
Trigger Term Quality | Includes strong natural trigger terms: 'optimize my prompt', 'improve this requirement', 'make this more specific', 'loose requirements', 'ambiguous feature descriptions'. These are phrases users would naturally say when needing this skill. | 3 / 3 |
Distinctiveness Conflict Risk | The EARS methodology reference adds distinctiveness, but terms like 'optimize my prompt' and 'improve this requirement' could overlap with general prompt engineering or writing improvement skills. The scope is somewhat broad covering code, products, and documents. | 2 / 3 |
Total | 10 / 12 Passed |
Implementation
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-structured skill with strong actionability and workflow clarity. The six-step process is clearly defined with concrete examples, EARS patterns, and structured output templates. The main weakness is moderate verbosity—the attribution paragraph, some explanatory framing, and the 'When to Use' section could be trimmed without losing clarity. The progressive disclosure is well-designed with clear references, though the referenced files aren't present in the bundle.
Suggestions
Remove the attribution paragraph and 'Methodology inspired by' section—it consumes tokens without adding actionable value for Claude.
Tighten the 'When to Use' section into the overview or remove it entirely, as Claude can infer applicability from the skill description and content.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is moderately efficient 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 and 'When to Use' section add modest value but pad the content. However, the core workflow steps are reasonably lean. | 2 / 3 |
Actionability | The skill provides highly concrete, actionable guidance: specific EARS patterns with before/after examples, a transformation checklist, domain theory mappings, a structured output template, and a complete presentation format. Each step has clear instructions with real examples rather than abstract descriptions. | 3 / 3 |
Workflow Clarity | The six-step workflow is clearly sequenced with explicit inputs/outputs at each stage. The transformation checklist in Step 2 serves as a validation checkpoint, and the final Step 6 provides a structured output format that acts as a completeness check. For a non-destructive prompt optimization task, this level of workflow clarity is excellent. | 3 / 3 |
Progressive Disclosure | The skill provides a clear overview with well-signaled one-level-deep references to four specific reference files (ears_syntax.md, domain_theories.md, examples.md, advanced_techniques.md). Each reference is described with its contents and when to load it. The main SKILL.md contains enough to work independently while pointing to deeper resources. However, no bundle files were provided to verify these references exist. | 3 / 3 |
Total | 11 / 12 Passed |
Validation
100%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 11 / 11 Passed
Validation for skill structure
No warnings or errors.
- Repository
- daymade/claude-code-skills
- Commit
bbf87f3
- Reviewed
Table of Contents
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