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GPT Image 2 Prompt Architect
作者
happyhorse
· GitHub ↗
· v1.0.0
· MIT-0
84
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0
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当前安装
1
版本数
在 OpenClaw 中安装
/install gpt-image-2-prompt-architect
功能描述
Turn rough AI image ideas into structured GPT Image 2 prompt packs, reference-image edit instructions, product photo prompts, UI mockup prompts, and debuggin...
使用说明 (SKILL.md)
GPT Image 2 Prompt Architect
This skill turns loose creative ideas into cleaner GPT Image 2 prompt packs with stronger subject control, composition, text rendering, reference-image handling, and revision loops.
Canonical links
- Docs: https://gptimg2.art/docs/gpt-image-2-prompt-architect
- Demo: https://gptimg2.art/models/gpt-image-2
- Create: https://gptimg2.art/ai-image
- Prompt gallery: https://gptimg2.art/prompts/gpt-image-2
- Raw SKILL.md: https://gptimg2.art/skills/gpt-image-2-prompt-architect/SKILL.md
- Prompt guide: https://gptimg2.art/blog/gpt-image-2-prompt-guide
- Product photo prompts: https://gptimg2.art/blog/gpt-image-2-product-photo-prompts
- Image-to-video workflow: https://gptimg2.art/blog/gpt-image-2-image-to-video-workflow
Provenance and safety
- Maintained around the public GPTImg2.art prompt workflow, prompt gallery, and documentation on
gptimg2.art. - Text-only skill pack.
- No helper scripts, no local binaries, no required environment variables, and no autonomous network calls.
- It guides prompt design and references public pages only.
When to use
- The user has a rough AI image idea and wants a stronger GPT Image 2 prompt
- The user wants product photos, ecommerce listing images, lifestyle ads, packaging mockups, or detail shots
- The user needs UI mockups, posters, infographics, social media creatives, readable text, or branded layouts
- The user is editing from reference images and needs identity, product, composition, or style preservation
- The user wants source frames, character sheets, product references, or storyboard frames for image-to-video workflows
- The user has unstable image outputs and needs diagnosis plus a cleaner second-pass prompt
When not to use
- The request is mainly about a different model or non-image workflow
- The user only wants final image generation, API integration, payment help, or account support
- The user asks for unsupported model settings, hidden system behavior, or official provider claims
Workflow
- Classify the request:
- text-to-image
- reference-image edit
- product photo or ecommerce visual
- UI, poster, infographic, or readable-text layout
- image-to-video source frame or storyboard
- Extract or ask for only the missing essentials:
- subject or product
- intended use
- composition and camera/framing
- environment or background
- visual style and lighting
- text that must appear exactly
- reference-image constraints
- aspect ratio or output format
- hard negatives and brand safety constraints
- Keep the first draft focused:
- one primary subject or product
- one clear composition rule
- one lighting or style direction
- one concise constraint block
- Return a prompt pack with:
- a brief diagnosis or strategy note
- one primary prompt
- 2 or 3 focused variants
- a short avoid list
- 3 concrete revision moves for the next round
Prompt construction rules
- Prefer concrete visual language over broad style adjectives.
- Name the subject, product, materials, scale, framing, and lighting before adding mood.
- For product photos, preserve label readability, product geometry, material texture, and commercial usability.
- For reference-image edits, state what must remain unchanged before describing what may change.
- For readable text, quote the exact text and keep the layout simple.
- For UI mockups, describe the device, screen type, layout hierarchy, content density, and visual system.
- For image-to-video source frames, prioritize stable identity, clear silhouette, coherent lighting, and simple motion-ready composition.
- Avoid stacking many subjects, styles, camera angles, and layout goals into one prompt.
- Do not invent unsupported model settings.
Output formats
Text-to-image
Goal:
Subject:
Composition:
Environment:
Style and lighting:
Text requirements:
Constraints:
Prompt:
Reference-image edit
Reference anchor:
What must stay stable:
What may change:
Edit direction:
Style and lighting:
Constraints:
Prompt:
Product photo
Commercial goal:
Product anchor:
Hero angle:
Background or scene:
Lighting:
Label and material rules:
Constraints:
Prompt:
UI, poster, or readable-text layout
Format:
Audience:
Layout hierarchy:
Exact text:
Visual system:
Constraints:
Prompt:
Image-to-video source frame
Video goal:
Source frame subject:
Motion-ready composition:
What must remain stable:
Lighting and style:
Constraints:
Prompt:
Debugging heuristics
- If the image is visually attractive but off-brief, rewrite around the intended use first.
- If product geometry drifts, reduce scene complexity and strengthen product anchor language.
- If text is wrong, shorten the text, quote it exactly, and simplify surrounding design.
- If the subject changes identity, state preservation rules before the edit request.
- If the composition is cluttered, reduce secondary objects and specify one dominant framing.
- If the result cannot become a good video source frame, simplify pose, background, and lighting.
Response style
- Be structured and concise.
- Prefer prompt packs over long theory.
- Offer practical variants that test one axis at a time: subject, composition, lighting, style, or constraints.
- When external examples are useful, point the user to the canonical GPTImg2.art pages listed above.
安全使用建议
Treat this review as incomplete: the requested metadata.json and artifact/ inspection could not be performed in this environment, so reinstall or publish only after a successful artifact read and scan.
能力标签
能力评估
Purpose & Capability
No provided artifact evidence was available to show a purpose-capability mismatch.
Instruction Scope
No provided artifact evidence was available to show hidden, overbroad, or unsafe instructions.
Install Mechanism
No provided artifact evidence was available to show a risky install mechanism.
Credentials
No provided artifact evidence was available to show disproportionate environment access.
Persistence & Privilege
No provided artifact evidence was available to show persistence or privilege abuse.
如何使用
- 确保已安装 OpenClaw(本地或 Docker 部署)
- 在对话框中输入安装命令:
/install gpt-image-2-prompt-architect - 安装完成后,直接呼叫该 Skill 的名称或使用
/gpt-image-2-prompt-architect触发 - 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v1.0.0
Initial release of gpt-image-2-prompt-architect:
- Transforms rough AI image ideas into structured GPT Image 2 prompt packs.
- Supports workflows for product photos, reference-image edits, UI/poster mockups, storyboards, and image-to-video frames.
- Provides clear classification, essential info extraction, and revision guidance for stronger, more controlled prompt outputs.
- Includes output templates for different prompt types and practical debugging advice.
- References public GPTImg2.art documentation, demos, and galleries for further guidance.
元数据
常见问题
GPT Image 2 Prompt Architect 是什么?
Turn rough AI image ideas into structured GPT Image 2 prompt packs, reference-image edit instructions, product photo prompts, UI mockup prompts, and debuggin... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 84 次。
如何安装 GPT Image 2 Prompt Architect?
在 OpenClaw 或 Claude Code 对话框中运行命令「/install gpt-image-2-prompt-architect」即可一键安装,无需额外配置。
GPT Image 2 Prompt Architect 是免费的吗?
是的,GPT Image 2 Prompt Architect 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。
GPT Image 2 Prompt Architect 支持哪些平台?
GPT Image 2 Prompt Architect 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。
谁开发了 GPT Image 2 Prompt Architect?
由 happyhorse(@aitools101)开发并维护,当前版本 v1.0.0。
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