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gpt-image-2

Generate or edit images with OpenAI's gpt-image-2 via GMI Cloud's async request-queue API (console.gmicloud.ai). One dual-mode script: with NO input image it runs `gpt-image-2-generate` (text-to-image); with one or more input images it runs `gpt-image-2-edit` (one request per image). Submits the job, polls until done, and downloads every result image to the shared project-root `outputs/` folder. Defaults: quality=medium, output_format=png, size=1920x1088, n=2. API key is read from a `.gmi_api_key` file in the skill folder (also accepts `--api-key` or `$GMI_API_KEY`). ACTIVATION IS BY EXPLICIT MODEL NAME ONLY. This skill ACTUALLY GENERATES / EDITS real images and spends API credits, so TRIGGER ONLY when the user explicitly names this model / provider — "gpt-image-2", "gpt image 2", "GMI", "GMI Cloud" — e.g. "用 gpt-image-2 生圖", "用 gpt-image-2 改圖", "GMI 生圖", "GMI Cloud 生圖", "跑 gpt-image-2", "gpt image 2 編輯", "gpt image 2 生成". DO NOT TRIGGER on generic image wording that does NOT name gpt-image-2 / GMI — e.g. "編輯圖片", "圖片編輯", "載入圖片編輯", "圖片編輯技能", "改圖", "修圖", "編輯這張圖", "轉為黑白線稿", "圖片生成", "生圖", "image editing", "edit image", "image generation". Those are PROMPT-WRITING requests, NOT generation: • editing an image / a hypothetical visual change → use `prompt-master-image-editor` (writes an English edit prompt only, never generates an actual image). • other prompt tasks → the relevant `prompt-master-*` skill. Also DO NOT TRIGGER for: Stable Diffusion / TensorArt generation (use `sd-image-gen`), Gemini / nano banana editing (use `nano-banana-pro-edit`), upscaling (use `sd-upscaler`), or image selection / judging (use `image-judge`).

中文简介正在准备中。当前显示原文说明。

Kouzui-HUANG/biz-ai-video-course星标 4(GitHub 4 + cccskills 0)
集成工具OpenAIGemini

实测评分 未实测

查看安装方法

包含的文件（7）

  • SKILL.md7.4 KB
  • docs/examples_zh-TW.md3.4 KB
  • docs/flags_zh-TW.md5.5 KB
  • docs/SKILL_zh-TW.md6.8 KB
  • references/examples.md3.3 KB
  • references/flags.md5.3 KB
  • scripts/gpt_image_2.py20.9 KB

SKILL.md（原文）

安装前可以查看交给 AI 代理的指令内容。

gpt-image-2

Run scripts/gpt_image_2.py to call GMI Cloud's gpt-image-2 models. The script is dual-mode and auto-detecting:

  • No input image → generate (gpt-image-2-generate, text-to-image).
  • One or more input images → edit (gpt-image-2-edit, one request per image).

Every result image is saved to the shared outputs/ folder at the project root, prefixed gpt_. The drop-zone for edit sources is the shared uploads/ folder next to it. Both are defined once in .claude/lib/media_paths.py and shared with the gemini-3-pro-image and seedance-2-0 skills:

<project root>/uploads/        drop edit-source images here
<project root>/uploads/done/   sources move here after a successful run
<project root>/outputs/        every result lands here (gpt_ / gemini_ / seedance_)

Core directives

  1. Never hard-code the API key. It is resolved in this order: --api-key flag → $GMI_API_KEY (incl. project .env) → .gmi_api_key file in the skill folder (this is where the copied key lives). If none is found the script exits with a clear message. The same GMI key powers the gemini-3-pro-image skill.
  2. Run from project root — CWD is /Users/kouzuimac/Documents/反重力/0527課用教材/biz-ai-video-course. (The shared folders resolve from the script's own location, so another CWD still works; project root just keeps the relative paths below readable.)
  3. Ask for the prompt. No default prompt. If the user doesn't supply one, ask before running.
  4. Mode is decided by whether an image is supplied. Pass image path(s)/URL(s) as positional args (or --image) → edit. Pass none → generate. --mode generate|edit forces it.
  5. Long prompts → @file.txt. For prompts longer than a couple of lines, save to a file under project root and pass --prompt @prompts/x.txt. Avoids shell-quoting traps.
  6. Defaults: quality=medium, png, size=1920x1088, n=2. Override via --quality / --output-format / --size / --n only when asked.
  7. Size is flexible (verified live). gpt-image-2 accepts any WxH with edges multiple of 16, longest edge ≤ 3840, aspect ≤ 3:1, total pixels 0.65M–8.3M — so up to 4K (3840x2160). Default 1920x1088 (native 16:9 HD). Exact 1920x1080 is rejected (1080 isn't ÷16) — use 1920x1088. auto also works.
  8. Don't chain into posting. Report saved paths; do not auto-invoke multi-sns-post.
  9. Default source folder — the shared uploads/. Edit-source images live in the project-root drop-zone uploads/, shared with the other two generation skills. When the user wants to edit an image but doesn't give a full path (or just pastes/drops one), look there first. The script resolves bare filenames and a directory path against this folder automatically (also CWD, project root, and uploads/done/), so you can pass just 2026-06-10-23.png, or point at the whole uploads/ directory to edit every image inside it in one run (a directory ref lists only its top level, so already-archived images in done/ are not re-edited). Match the output --size to the source aspect ratio (e.g. a 9:16 portrait → 1088x1920) so the edit isn't stretched or cropped.
  10. Used sources self-archive. After a successful edit, sources that were sitting in uploads/ are moved to uploads/done/, so the drop-zone only ever shows what still needs doing. A failed edit leaves its source in place for a retry. Pass --keep-refs when the user wants several prompts on the same image; a re-run still accepts the bare filename either way.

Knowledge hub

Read only the reference file relevant to the task:

  • Need CLI flags, modes, exit codes, the GMI API contract (endpoints, payload fields, polling, response shape), output naming → references/flags.md
  • Need concrete invocation examples (generate, edit single/many, file-based prompt, URL input, overrides, stdout shape) → references/examples.md

Standard operating procedure

  1. Get the prompt from the user (inline or @file).
  2. Decide mode: did the user provide an image — a path, or one sitting in the shared uploads/ drop-zone? → edit, else generate. For edits without an explicit path, check uploads/ first.
  3. Run the script (see references/examples.md for forms): 
    # generate (no image)
python3 .claude/skills/gpt-image-2/scripts/gpt_image_2.py \
  --prompt "<text-or-@file>"

# edit (one or more images)
python3 .claude/skills/gpt-image-2/scripts/gpt_image_2.py \
  --prompt "<text-or-@file>" path/to/source.png
  4. Watch stdout: [mode] ..., [submit] ..., [poll] status=..., [✓] ..., 📦 ... (source archived), [done] ....
  5. Report saved outputs/ paths to the user.

Output protocol

  • Files land in the shared project-root outputs/ folder (auto-created):
    • generate: gpt_{YYYYmmdd_HHMMSS}[_k].{png|jpg}
    • edit: gpt_{YYYYmmdd_HHMMSS}_{source_stem}[_k].{png|jpg}
    • _k index suffix is added only when n > 1.
    • The gpt_ prefix is what tells these apart from gemini_ / seedance_ results in the same folder. An existing file is never overwritten — a colliding name gets a _1, _2… suffix.
  • Stdout prefixes: [mode] / [submit] / [poll] / [✓] / [batch] / [done] / [!]. Parse [✓] lines for paths when chaining into other tooling.
  • Exit codes: 0 all good · 1 config/validation error (bad key, invalid size/quality/format/n) · 2 job ran but ≥1 image failed.

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Kouzui-HUANG/biz-ai-video-course42026年10月27日 更新

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