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gpt-image-2-prompt-engine

面向电商设计师、海报美工、品牌视觉、UI设计师、信息图编辑、商业摄影师、内容创作者等需要高质量可控出图的角色,在需要用 GPT-Image-2 生成电商主图、电影海报、信息图、品牌视觉、UI截图、古籍国风、角色IP等场景时,通过「Prompt as Code」原子化Schema+20+工业JSON模板+四步工作流,产出结构化、可复用、可批量的生图提示词,再调用 image_generation 出图。不适用于随意生图或简单风景照。

72

Quality

88%

Does it follow best practices?

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SecuritybySnyk

Passed

No findings from the security scan

SKILL.md
Quality
Evals
Security

Quality

Content

85%Weight 40%Scale 1-5

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

A well-structured, actionable skill body with a clear validated four-step workflow and good progressive disclosure into the style-library reference. The main improvement area is trimming the inline template verbosity for tighter token efficiency.

Suggestions

Reduce the three full inline JSON templates to one representative template plus a pointer to references/style-library.md for the rest, to improve conciseness.

Consider collapsing the 资源引用 and 注意事项 sections, keeping only the one-line upstream attribution and the critical 403-拦截 fallback note.

Tighten the 万能结构公式 and 原子化 Schema table, which slightly overlap, into a single consolidated reference.

DimensionReasoningScore

Conciseness

The body is mostly lean and avoids explaining concepts Claude already knows, but the three full inline JSON templates and the resource/attribution sections add length that could be trimmed slightly.

4 / 5

Actionability

Concrete executable guidance is provided — real bash commands (python3 scripts/query_templates.py --category ecommerce), a real query script, variable-filled JSON templates, and an atomic Schema table — with only minor gaps around the placeholder variables.

4 / 5

Workflow Clarity

The four-step workflow is clearly sequenced and Step 4 includes explicit validation checkpoints (检查文字是否准确、构图是否符合、比例是否正确) with a feedback loop (不满足则调整 Schema 维度后重新生成), satisfying the batch-iteration validation requirement.

5 / 5

Progressive Disclosure

The body is an overview that inlines a representative subset (Schema table, 3 templates, 5 tips) and clearly signals one-level-deep references to the verified real file references/style-library.md and scripts/query_templates.py, with easy navigation.

5 / 5

Total

18

/

20

Passed

Description

92%Weight 40%Scale 1-5

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

A strong description that names concrete methods, explicit trigger scenarios, and a clear negative boundary, all in third person. It could be marginally stronger on trigger-term synonym coverage but otherwise answers what, when, and how it differs from basic image generation.

DimensionReasoningScore

Specificity

The description lists multiple concrete actions — 「原子化Schema」拆解, 「20+工业JSON模板」, 「四步工作流」, 「产出结构化、可复用、可批量的生图提示词」, 「调用 image_generation 出图」 — giving comprehensive coverage of capabilities rather than vague claims.

5 / 5

Completeness

It explicitly answers both 'what' (产出结构化可复用提示词并调用 image_generation 出图) and 'when' (在需要用 GPT-Image-2 生成电商主图/海报/信息图等场景时), plus a negative boundary (不适用于随意生图或简单风景照).

5 / 5

Trigger Term Quality

Natural scenario terms a user would say are present (电商主图、电影海报、信息图、品牌视觉、UI截图、古籍国风、角色IP), but a few common synonyms/variations are missing, so it falls just short of comprehensive coverage.

4 / 5

Distinctiveness Conflict Risk

The GPT-Image-2 structured-prompt niche with specific scenario triggers and an explicit 不适用于 boundary makes it clearly distinguishable from the generic image_generation skill with minimal conflict risk.

5 / 5

Total

19

/

20

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

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

15

/

16

Passed

Repository
anbeime/skill
Reviewed

Table of Contents

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