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intelligent-prompt-generator

智能提示词生成器 v2.0 - 支持人像/跨domain/设计三种模式,语义理解、常识推理、一致性检查

27

Quality

18%

Does it follow best practices?

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SecuritybySnyk

Passed

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tessl review fix ./.claude/skills/intelligent-prompt-generator/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

27%

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

The body is an over-long monolith with heavy redundancy and no progressive disclosure, though it does offer concrete Python code and mapping tables. It needs aggressive trimming, removal of duplicate sections, and extraction of detail into reference files.

Suggestions

Collapse the repeated Goku/Kamehameha walkthroughs into one concise example and delete concept explanations Claude already knows.

Split detailed material into reference files (e.g. framework config, element-selection tables, v2.0 mode docs) and link one level deep from SKILL.md.

Remove the hardcoded /Users/serva/... path and duplicate step headers, and add explicit validation checkpoints (verify imports, check candidate match) to the workflow.

DimensionReasoningScore

Conciseness

At ~1340 lines the body is heavily padded: it restates the same Goku/Kamehameha example four or more times, repeats principles, and explains concepts (what anime/cyberpunk styles mean) that Claude already knows.

1 / 3

Actionability

It provides concrete executable Python snippets and detailed mapping tables, but imports reference unverified local modules and a hardcoded absolute path (/Users/serva/...), making the code not reliably copy-paste ready.

2 / 3

Workflow Clarity

Steps 1–6 are sequenced, but validation checkpoints are implicit (consistency checks described without a validate→fix→retry loop) and the structure is muddled with duplicate/contradictory '步骤4:返回提示词' headers.

2 / 3

Progressive Disclosure

It is a monolithic ~1340-line single file with no references/scripts/assets bundle; content that should be split (v2.0 features, framework config, knowledge tables, examples) is all inline.

1 / 3

Total

6

/

12

Passed

Description

10%

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 mostly a product tagline listing modes and abstract capabilities rather than a skill description; it lacks explicit usage triggers and natural keywords users would say. It needs concrete deliverables and a 'Use when...' clause to be discoverable and distinguishable.

Suggestions

Rewrite in third person as concrete actions, e.g. 'Generates image prompts combining database elements with Claude-supplied semantic descriptions across portrait, cross-domain, and design modes.'

Add an explicit trigger clause: 'Use when the user asks to generate image prompts, describe a scene/character for an image model, or requests prompt engineering for image generation.'

Replace internal jargon (v2.0, 跨domain) with natural terms users would actually say (提示词, 图像生成提示词, 人像/设计/多场景).

DimensionReasoningScore

Specificity

Names concrete modes (人像/跨domain/设计) and capabilities, but the listed actions (语义理解/常识推理/一致性检查) are abstract capabilities rather than concrete deliverables, so it does not reach the 'multiple specific concrete actions' level.

2 / 3

Completeness

It gives a partial 'what' (modes + abstract capabilities) but entirely lacks any 'when to use' / 'Use when...' clause, and the 'what' is weak, so it does not explicitly answer when Claude should use it.

1 / 3

Trigger Term Quality

The description contains no natural user-facing trigger terms such as '提示词'/'prompt'/'生成图像提示词'; instead it uses internal jargon like 'v2.0' and '跨domain' that users would not naturally say.

1 / 3

Distinctiveness Conflict Risk

The generic '提示词生成器' phrasing with no niche-specific anchors (file types, concrete use-cases) would overlap and conflict with many other prompt-generation skills.

1 / 3

Total

5

/

12

Passed

Validation

93%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

skill_md_line_count

SKILL.md is long (1346 lines); consider splitting into references/ and linking

Warning

Total

15

/

16

Passed

Repository
huangserva/skill-prompt-generator
Reviewed

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