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grok-imagine-image-proGrok 想象图像专业版

Agent Skill

用于辅助图像生成、图片编辑、视觉素材处理或图像模型工作流。它适合让 Agent 根据文本生成图片、处理背景、整理视觉提示词或调用相关图像工具。使用时需要确认输入图片、版权来源、输出格式和模型限制;涉及人物、品牌、商品或公开展示素材时,应额外核对授权、真实性和内容合规边界。

总安装

36,127

周安装

1,536

GitHub Stars

3

下载量

12,657
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:grok-imagine-image-pro(Grok 想象图像专业版)
来源仓库:https://github.com/nixeifoit/grok-imagine-image-pro
安装命令:
openclaw skills install grok-imagine-image-pro
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install grok-imagine-image-pro

简介

通过 xAI Grok/Flux API 使用提示、样式、长宽比和使用 Base64 输出进行批处理来生成和编辑高质量 PNG 图像。

SKILL.md

name
grok-imagine-image-pro
description
Generiert hochwertige Bilder mit xAI Grok/Flux API. Use when user asks for image generation ("mach a Bild von...", "generier PNG...", "Bild erstellen") or image editing ("ändere das Bild", "mach daraus..."). Handles Prompts, styles, aspect ratios, edits, batch generation. Outputs PNG via base64 or file.
metadata
openclaw
requires
env
bins

Grok Imagine Image Pro

API Key: $XAI_API_KEY (already configured) Save dir: ~/.openclaw/media/ (resolves to /data/.openclaw/media/ — allowed for Telegram sending)

Available Models

  • grok-imagine-image — standard quality, faster
  • grok-imagine-image-pro — higher quality (default for generation)

1. Image Generation

curl -s https://api.x.ai/v1/images/generations \
  -H "Authorization: Bearer $XAI_API_KEY" \
  -H "Content-Type: application/json" \
  --data '{
    "model": "grok-imagine-image-pro",
    "prompt": "<PROMPT>",
    "n": 1,
    "response_format": "b64_json"
  }' | python3 -c "
import json, sys, base64, os, time
os.makedirs(os.path.expanduser('~/.openclaw/media'), exist_ok=True)
r = json.load(sys.stdin)
ts = int(time.time())
for i, img in enumerate(r['data']):
    img_data = base64.b64decode(img['b64_json'])
    fpath = os.path.expanduser(f'~/.openclaw/media/generated_{ts}_{i}.png')
    with open(fpath, 'wb') as f:
        f.write(img_data)
    print(fpath)
"

Aspect Ratios

Add "aspect_ratio": "<ratio>" to the JSON body. Supported values:

RatioUse case
1:1Social media, thumbnails
16:9 / 9:16Widescreen, mobile stories
4:3 / 3:4Presentations, portraits
3:2 / 2:3Photography
2:1 / 1:2Banners, headers
autoModel picks best ratio (default)

Batch Generation

Set "n": <count> (1-10) to generate multiple images in one request.

2. Image Editing / Style Transfer

Edit an existing image by providing a source image plus an edit prompt. Uses the same /v1/images/generations endpoint with an added image_url field.

Do NOT use /v1/images/edits with multipart — xAI requires JSON.

IMPORTANT: For local files, use Python to build the payload JSON file, then curl with @file. Inline base64 in curl args causes "Argument list too long" for images >~100KB.

NOTE: This is NOT true image editing — the API generates a new image inspired by the source. It cannot make pixel-precise edits (e.g. changing only a car's color while keeping everything else identical).

Edit from local file (recommended approach):

python3 -c "
import json, base64
with open('<SOURCE_PATH>', 'rb') as f:
    b64 = base64.b64encode(f.read()).decode()
payload = {
    'model': 'grok-imagine-image',
    'prompt': '<EDIT_PROMPT>',
    'image_url': f'data:image/png;base64,{b64}',
    'n': 1,
    'response_format': 'b64_json'
}
with open('/tmp/img_edit_payload.json', 'w') as f:
    json.dump(payload, f)
print('Payload ready')
" && \
curl -s https://api.x.ai/v1/images/generations \
  -H "Authorization: Bearer $XAI_API_KEY" \
  -H "Content-Type: application/json" \
  -d @/tmp/img_edit_payload.json | python3 -c "
import json, sys, base64, os, time
os.makedirs(os.path.expanduser('~/.openclaw/media'), exist_ok=True)
r = json.load(sys.stdin)
img_data = base64.b64decode(r['data'][0]['b64_json'])
fpath = os.path.expanduser(f'~/.openclaw/media/edited_{int(time.time())}.png')
with open(fpath, 'wb') as f:
    f.write(img_data)
print(fpath)
"

Edit from URL:

curl -s https://api.x.ai/v1/images/generations \
  -H "Authorization: Bearer $XAI_API_KEY" \
  -H "Content-Type: application/json" \
  --data '{
    "model": "grok-imagine-image",
    "prompt": "<EDIT_PROMPT>",
    "image_url": "<PUBLIC_IMAGE_URL>",
    "n": 1,
    "response_format": "b64_json"
  }' | python3 -c "
import json, sys, base64, os, time
os.makedirs(os.path.expanduser('~/.openclaw/media'), exist_ok=True)
r = json.load(sys.stdin)
img_data = base64.b64decode(r['data'][0]['b64_json'])
fpath = os.path.expanduser(f'~/.openclaw/media/edited_{int(time.time())}.png')
with open(fpath, 'wb') as f:
    f.write(img_data)
print(fpath)
"

Style Transfer Examples

Use editing with a style prompt, e.g.:

  • "Render this as an oil painting in impressionist style"
  • "Make this a pencil sketch with detailed shading"
  • "Convert to pop art with bold colors"
  • "Watercolor painting with soft edges"

3. Sending to Telegram

message tool: action=send, channel=telegram, target=<id>,
  message="<caption>", filePath=~/.openclaw/media/<file>.png
  • Always include message field (required even for media-only sends)
  • Allowed media paths: /tmp/, ~/.openclaw/media/, ~/.openclaw/agents/

Notes

  • Do NOT pass size parameter — returns 400
  • Aspect ratio: pass aspect_ratio in JSON body (not size)
  • Editing: use image_url field in the generations endpoint (NOT the edits endpoint with multipart)
  • Always use "response_format": "b64_json" — URL format returns temporary URLs that often 403
  • For large images: build payload with Python → save to /tmp/ → curl with @file syntax
  • Max 10 images per request
  • Images are subject to content moderation
  • Editing is style-transfer/reimagination, NOT pixel-precise inpainting

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

OpenClaw

85.48%
按下载量换算10,819

安全审计

VirusTotal

可疑

ClawScan

通过

Static analysis

未展示

权限和风险

敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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