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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.

89

1.62x
Quality

Does it follow best practices?

Impact

96%

1.62x

Average score across 3 eval scenarios

SecuritybySnyk

Passed

No known issues

SKILL.md
Quality
Evals
Security

Quality

Content

72%

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The body is well-structured and highly actionable with concrete EARS patterns, examples, templates, and well-organized progressive disclosure to real reference files. Its main weaknesses are mild verbosity (marketing language, an attribution paragraph, and some restated content) and the absence of an explicit validation/feedback checkpoint in the workflow.

Suggestions

Tighten conciseness: drop the 'Rolls-Royce methodology' flourish and the methodology-inspired-by attribution, and avoid restating the description's triggers in the 'When to Use' section — reference it instead.

Add an explicit validation/verification step to the workflow (e.g., a Step 5.5 'Review the enhanced prompt against the transformation checklist and quality criteria; revise any EARS statements that are non-atomic or lack measurable criteria') to create a clear feedback loop.

De-duplicate the Resources section so it only states when to load each file, since each reference's purpose is already described at its inline citation in Steps 2, 3, and Advanced Techniques.

DimensionReasoningScore

Conciseness

Mostly efficient and actionable, but includes tightening opportunities: the marketing phrase 'a Rolls-Royce methodology', the methodology-inspired-by attribution paragraph, and redundancy where 'When to Use' restates triggers already in the description and the Resources section restates reference purposes already given inline at Steps 2, 3, and Advanced Techniques. Not the top anchor because not every token earns its place.

2 / 3

Actionability

Provides concrete, copy-paste-ready guidance: the five EARS patterns with a before/after reminder-app example, a transformation checklist, domain-to-theory mappings, a full Role/Skills/Workflows/Examples/Formats output template, and explicit quality criteria. This matches the 'specific examples; copy-paste ready' anchor for an instruction-only skill.

3 / 3

Workflow Clarity

The six-step optimization workflow is clearly sequenced and includes embedded checklists (transformation checklist, do's/don'ts, quality criteria), but there is no explicit validation/verification gate or feedback loop — checkpoints are implicit via the quality criteria rather than a labeled 'validate, then proceed' step. This matches the 'sequence present but checkpoints missing or implicit' anchor rather than the explicit-feedback-loop anchor above.

2 / 3

Progressive Disclosure

The body is a concise overview pointing to four real, one-level-deep reference files (ears_syntax.md, domain_theories.md, examples.md, advanced_techniques.md — all present in references/), signaled inline at the relevant steps and consolidated in a Resources section with 'When to load references' guidance, matching the 'clear overview with well-signaled one-level-deep references' anchor.

3 / 3

Total

10

/

12

Passed

Description

100%

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

The description is strong: it states concrete capabilities grounded in the EARS methodology, uses natural trigger phrases a user would actually say, and explicitly covers both what the skill does and when to invoke it. It occupies a distinct niche with low conflict risk.

DimensionReasoningScore

Specificity

Names multiple concrete actions ('Transform vague prompts into precise, well-structured specifications using EARS... methodology', 'enhance prompts for AI-generated code, products, or documents') and grounds them in a specific named methodology, matching the 'lists multiple specific concrete actions' anchor.

3 / 3

Completeness

Explicitly answers both what ('Transform vague prompts into precise, well-structured specifications using EARS') and when ('This skill should be used when users provide loose requirements...') with an explicit 'Triggers include' clause, matching the 'clearly answers both what AND when' anchor.

3 / 3

Trigger Term Quality

Includes natural user phrases users would actually say — 'optimize my prompt', 'improve this requirement', 'make this more specific' — alongside 'loose requirements' and 'ambiguous feature descriptions', giving good coverage matching the top anchor.

3 / 3

Distinctiveness Conflict Risk

Occupies a clear niche (EARS-based prompt/requirement optimization) with distinct triggers tied to prompt refinement, making it unlikely to fire for unrelated skills; matches the 'clear niche with distinct triggers' anchor.

3 / 3

Total

12

/

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.

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
daymade/claude-code-skills
Reviewed

Table of Contents

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