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Agent Skill · 图像处理

image-fetch-toolkit(图像获取工具包)

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。

image-fetch-toolkit 是图像处理类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为接触敏感数据;许可证为 MIT-0,第三方扫描有提醒项,按安装量在图像处理类的 1,562 个 Skill 里排第 735。

总安装 3,011周安装 123GitHub Stars 公开资料未说明下载 964核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT-0
版本
1.0.0

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:image-fetch-toolkit(图像获取工具包)
来源仓库:https://github.com/newtontech/image-fetch-toolkit
安装命令:
openclaw skills install image-fetch-toolkit
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 image-fetch-toolkit

这个 Skill 是做什么的

图像处理类 Skill 处理生成、裁剪、压缩和批量转换图片的任务。

从互联网获取各类用途的图像资源集合。适用宿主包括 OpenClaw,接入前应确认版本、权限和运行环境要求。涵盖人物、新闻、库存、科学插图等多种类型。输入搜索关键词即可返回相关图像链接列表。使用时应遵守目标网站的内容授权政策。

image-fetch-toolkit 属于图像处理类 Skill,可作为该场景下的辅助能力补充。

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 OpenClaw;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 openclaw skills install image-fetch-toolkit;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要接触敏感数据,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

image-fetch-toolkit 怎么安装?

在终端执行 openclaw skills install image-fetch-toolkit。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

image-fetch-toolkit 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

image-fetch-toolkit 需要什么权限?

权限级别是接触敏感数据。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。

image-fetch-toolkit 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

image-fetch-toolkit 用的人多吗?

公开数据是累计安装 3,011 次,近一周安装 123 次,下载 964 次。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

image-fetch-toolkit 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

值得选的地方和要先想清楚的地方

值得选的地方

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。

先想清楚的地方

  • 需要接触敏感数据,装到常用环境前要先评估影响面。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 OpenClaw,想直接扩展它能力的人。
  • 需要处理图像处理类任务、又不想每次都手写提示词的人。

不太适合

  • 环境里有大量密钥、凭证,不方便给第三方 Skill 读取的人。
  • 需要厂商级技术支持和 SLA 的生产系统。

图像处理类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
ai-image-generation

AI 图像生成 · Codex、Claude

4,365,552382调用外部服务未明确有提醒
openai-image-gen

OpenAI 图像生成 · OpenClaw

1,906,88432需要联网MIT-0有提醒
image

图像 · OpenClaw

525,99926需要联网MIT-0通过
baoyu-cover-image

宝玉封面图 · Codex、Claude

474,24016,779只读MIT有提醒
baoyu-image-gen

宝玉图像生成器 · Codex、Claude

468,00716,778接触敏感数据MIT通过
image-fetch-toolkit

图像获取工具包 · 本页

3,011—接触敏感数据MIT-0有提醒

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

name
image-fetch-toolkit
description
Search and fetch images from the internet for any purpose - paper figures, news photos, stock images, product photos, scientific illustrations, social media images, and more. Use this skill whenever the user needs to find, retrieve, or curate images from online sources, whether for academic papers, blog posts, presentations, marketing materials, or content creation. Also covers academic figure composition (multi-panel abc labeling), scientific illustration generation, and image search API integration.

Image Fetch Toolkit

A comprehensive guide for AI agents to search, retrieve, and curate images from various online sources. Covers free stock photos, academic figures, news images, product photos, scientific illustrations, and more.

Quick Start

Before fetching images, check which tools/APIs are available in the current environment:

  1. Tavily - Web search + URL extraction (recommended, often pre-configured)
  2. Unsplash API - 3M+ free high-quality photos
  3. Pexels API - Free stock photos + videos
  4. Pixabay API - 2M+ free images, illustrations, vectors
  5. Flickr API - Photographer community images
  6. Google Custom Search API - General image search
  7. Bing Image Search API - Microsoft's image search

1. Free Stock Photo APIs

Unsplash API

  • Base URL: https://api.unsplash.com
  • Auth: Bearer token in header Authorization: Bearer <ACCESS_KEY>
  • Get key: https://unsplash.com/developers
  • Rate limit: 50 requests/hour (free tier)
# Search photos
curl -H "Authorization: Bearer $UNSPLASH_ACCESS_KEY" \
  "https://api.unsplash.com/search/photos?query=renewable+energy&per_page=10&orientation=landscape"

# Get a random photo
curl -H "Authorization: Bearer $UNSPLASH_ACCESS_KEY" \
  "https://api.unsplash.com/photos/random?query=technology&orientation=squarish"

# Download tracked photo (respects photographer attribution)
curl -H "Authorization: Bearer $UNSPLASH_ACCESS_KEY" \
  -L "https://api.unsplash.com/photos/<PHOTO_ID>/download"

Response fields: results[].urls.raw, results[].urls.full, results[].urls.regular, results[].urls.small, results[].user.name, results[].links.html

Pexels API

  • Base URL: https://api.pexels.com/v1
  • Auth: Header Authorization: <API_KEY>
  • Get key: https://www.pexels.com/api/
  • Rate limit: 200 requests/hour
# Search photos
curl -H "Authorization: $PEXELS_API_KEY" \
  "https://api.pexels.com/v1/search?query=nature&per_page=10&orientation=landscape"

# Curated photos
curl -H "Authorization: $PEXELS_API_KEY" \
  "https://api.pexels.com/v1/curated?per_page=15"

# Get photo by ID
curl -H "Authorization: $PEXELS_API_KEY" \
  "https://api.pexels.com/v1/photos/<PHOTO_ID>"

Pixabay API

  • Base URL: https://pixabay.com/api
  • Auth: Query param key=<API_KEY>
  • Get key: https://pixabay.com/api/docs/
  • Rate limit: 100 requests/min
# Search images
curl "https://pixabay.com/api/?key=$PIXABAY_API_KEY&q=mountain+landscape&image_type=photo&per_page=10&safesearch=true"

# Search vectors/illustrations
curl "https://pixabay.com/api/?key=$PIXABAY_API_KEY&q=robot&image_type=vector&per_page=10"

# Search videos
curl "https://pixabay.com/api/videos/?key=$PIXABAY_API_KEY&q=ocean&per_page=5"

Categories: backgrounds, fashion, nature, science, education, feelings, health, people, religion, places, animals, industry, computer, food, sports, transportation, travel, buildings, business, music

Flickr API

  • Base URL: https://api.flickr.com/services/rest
  • Auth: API key query param
  • Get key: https://www.flickr.com/services/apps/
  • Note: Check license! Use license=1,2,4,5,9 for CC-compatible
# Search photos (CC license only)
curl "https://api.flickr.com/services/rest/?method=flickr.photos.search&api_key=$FLICKR_API_KEY&text=sunset&license=1,2,4,5,9&per_page=10&format=json&nojsoncallback=1"

# Get photo URL: https://farm{farm}.staticflickr.com/{server}/{id}_{secret}.jpg

2. Search Engine Image APIs

Google Custom Search API (Image Search)

  • Base URL: https://www.googleapis.com/customsearch/v1
  • Auth: API key + CX (search engine ID)
  • Get key: https://developers.google.com/custom-search
  • Note: Enable "Image Search" in your Custom Search Engine settings
# Search images
curl "https://www.googleapis.com/customsearch/v1?key=$GOOGLE_API_KEY&cx=$GOOGLE_CX&searchType=image&q=AI+conference+2024&num=10&imgSize=large"

Bing Image Search API

  • Base URL: https://api.bing.microsoft.com/v7.0/images/search
  • Auth: Header Ocp-Apim-Subscription-Key: <KEY>
  • Get key: https://www.microsoft.com/en-us/bing/apis/bing-image-search-api
  • Rate limit: 1000 transactions/month (free)
curl -H "Ocp-Apim-Subscription-Key: $BING_API_KEY" \
  "https://api.bing.microsoft.com/v7.0/images/search?q=cute+cats&count=10&imageType=Photo&size=Large"

Tavily Extract (for webpage images)

# Extract content + images from a URL
node ~/.openclaw/workspace/skills/tavily-search/scripts/extract.mjs "https://example.com/article" --include-images true

3. Academic & Scientific Image Sources

Paper Search MCP (MCP Server)

  • Repo: https://github.com/openags/paper-search-mcp
  • Features: Search Semantic Scholar, arXiv, CORE, Zenodo, Google Scholar, IEEE, ACM
  • Install:
npx skills add openags/paper-search-mcp
# or with uv:
uv tool install paper-search-mcp
  • Usage: Search papers → extract figure URLs from paper pages
# Search papers
curl "https://api.semanticscholar.org/graph/v1/paper/search?query=protein+folding&limit=10&fields=title,url,openAccessPdf"

Semantic Scholar API (Free)

# Search papers with figure references
curl "https://api.semanticscholar.org/graph/v1/paper/search?query=transformer+architecture&limit=5&fields=title,url,openAccessPdf,figures"

arXiv API (Free, no key needed)

# Search papers
curl "http://export.arxiv.org/api/query?search_query=all:electron+microscopy&start=0&max_results=5"

PubMed Central (Free, no key needed)

# Search PMC for figures
curl "https://www.ncbi.nlm.nih.gov/pmc/utils/oa/oa.fcgi?id=PMC1234567"  # get full text XML with figures

Scientific Illustration Tools

  • FigureLabs: https://www.figurelabs.ai - AI-powered scientific figure generation
  • BioRender: https://biorender.com - Professional biological diagrams
  • Illustrae: AI scientific illustration generation
  • Mind the Graph: https://mindthegraph.com - Scientific infographic maker

4. Academic Figure Composition (Multi-Panel abc Labels)

For combining multiple images into a single figure with (a), (b), (c) labels - standard in academic papers.

Python (matplotlib + PIL)

from PIL import Image, ImageDraw, ImageFont
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np

def create_figure_grid(image_paths, labels=None, cols=2, label_font_size=20, 
                       label_position='top-left', padding=20, label_color='white',
                       bg_color='white', dpi=300, output_path='figure.png'):
    """
    Combine multiple images into a single academic figure with (a)(b)(c) labels.
    
    Args:
        image_paths: List of image file paths
        labels: List of label strings (default: a, b, c, ...)
        cols: Number of columns
        label_font_size: Font size for labels
        label_position: 'top-left', 'top-right', 'bottom-left', 'bottom-right'
        padding: Space between images in pixels
        label_color: Color of labels ('white' or 'black')
        bg_color: Background color
        dpi: Output DPI
        output_path: Output file path
    """
    if labels is None:
        labels = [chr(ord('a') + i) for i in range(len(image_paths))]
    
    rows = (len(image_paths) + cols - 1) // cols
    images = [Image.open(p).convert('RGB') for p in image_paths]
    
    # Find max dimensions
    max_w = max(img.width for img in images)
    max_h = max(img.height for img in images)
    
    # Create canvas
    total_w = cols * max_w + (cols + 1) * padding
    total_h = rows * max_h + (rows + 1) * padding
    canvas = Image.new('RGB', (total_w, total_h), bg_color)
    draw = ImageDraw.Draw(canvas)
    
    # Try to load a font
    try:
        font = ImageFont.truetype("/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf", label_font_size)
    except:
        font = ImageFont.load_default()
    
    for idx, (img, label) in enumerate(zip(images, labels)):
        row, col = idx // cols, idx % cols
        x = padding + col * (max_w + padding)
        y = padding + row * (max_h + padding)
        canvas.paste(img, (x, y))
        
        # Draw label
        label_text = f"({label})"
        bbox = draw.textbbox((0, 0), label_text, font=font)
        lw, lh = bbox[2] - bbox[0], bbox[3] - bbox[1]
        margin = 10
        
        if label_position == 'top-left':
            lx, ly = x + margin, y + margin
        elif label_position == 'top-right':
            lx, ly = x + max_w - lw - margin, y + margin
        elif label_position == 'bottom-left':
            lx, ly = x + margin, y + max_h - lh - margin
        else:
            lx, ly = x + max_w - lw - margin, y + max_h - lh - margin
        
        # Draw background rectangle for readability
        draw.rectangle([lx - 4, ly - 2, lx + lw + 4, ly + lh + 2], fill=label_color, outline=label_color)
        text_color = 'black' if label_color == 'white' else 'white'
        draw.text((lx, ly), label_text, fill=text_color, font=font)
    
    canvas.save(output_path, dpi=(dpi, dpi))
    print(f"Figure saved to {output_path}")
    return canvas

# Usage
create_figure_grid(
    image_paths=['fig1.png', 'fig2.png', 'fig3.png', 'fig4.png'],
    labels=['a', 'b', 'c', 'd'],
    cols=2,
    output_path='combined_figure.png'
)

Python (matplotlib subplot)

import matplotlib.pyplot as plt
import matplotlib.image as mpimg

def subplot_figure(image_paths, labels=None, cols=2, figsize=(12, 10), 
                   label_size=16, label_weight='bold', output_path='figure.pdf'):
    """Create academic figure using matplotlib subplots with (a)(b)(c) labels."""
    if labels is None:
        labels = [chr(ord('a') + i) for i in range(len(image_paths))]
    
    rows = (len(image_paths) + cols - 1) // cols
    fig, axes = plt.subplots(rows, cols, figsize=figsize)
    axes = axes.flatten() if hasattr(axes, 'flatten') else [axes]
    
    for ax, path, label in zip(axes, image_paths, labels):
        img = mpimg.imread(path)
        ax.imshow(img)
        ax.set_title(f'({label})', fontsize=label_size, fontweight=label_weight, loc='left')
        ax.axis('off')
    
    # Hide empty subplots
    for ax in axes[len(image_paths):]:
        ax.axis('off')
    
    plt.tight_layout()
    fig.savefig(output_path, dpi=300, bbox_inches='tight')
    print(f"Saved to {output_path}")

# Usage
subplot_figure(['a.png', 'b.png', 'c.png'], cols=3, output_path='fig1.pdf')

CLI one-liner (ImageMagick)

# Simple 2x2 grid
montage img1.png img2.png img3.png img4.png -tile 2x2 -geometry +5+5 output.png

# With labels (requires additional steps)
# Better to use the Python script above for labeled figures

5. News Image Sources

Google News Scraper

  • Repo: https://github.com/oxylabs/google-news-scraper
  • Scrapes Google News articles including thumbnails

Tavily News Search

node ~/.openclaw/workspace/skills/tavily-search/scripts/search.mjs "AI breakthrough 2024" --topic news --days 7

NewsAPI.org

  • Base URL: https://newsapi.org/v2
  • Auth: apiKey query param or X-Api-Key header
  • Get key: https://newsapi.org
  • Rate limit: 100 requests/day (free)
# Search news with images
curl "https://newsapi.org/v2/everything?q=AI+research&apiKey=$NEWSAPI_KEY&pageSize=10"
# Articles include urlToImage field

GNews API

  • Base URL: https://gnews.io/api/v4
  • Auth: token query param or apikey header
curl "https://gnews.io/api/v4/search?q=climate+change&token=$GNEWS_API_KEY&lang=en&max=10"

6. E-Commerce / Product Image Sources

Amazon Product Advertising API

  • Base URL: https://webservices.amazon.com/paapi5
  • Auth: Access Key + Secret Key
  • Get key: https://affiliate-program.amazon.com
# Search products with images (via PA-API)
curl -X POST "https://webservices.amazon.com/paapi5/searchitems" \
  -H "Content-Type: application/json" \
  -d '{"Keywords":"wireless headphones","SearchIndex":"Electronics","ItemCount":10}'

Scraper Alternatives (GitHub)

  • Search GitHub for amazon scraper, taobao scraper, ebay scraper
  • Use with caution regarding terms of service

7. Social Media Image Sources

X/Twitter

  • xbird-skill MCP: https://github.com/checkra1neth/xbird-skill - 34 tools for Twitter/X
  • x-search (ClawHub): Search tweets with media

Reddit

  • Reddit API: https://oauth.reddit.com/search.json?q=<query>&type=link
  • Multiple subreddits are excellent image sources:

- r/EarthPorn, r/SpacePorn, r/CityPorn - High-res photography - r/dataisbeautiful - Data visualizations - r/infographics - Infographics - r/scientific - Scientific imagery

Instagram (via Meta Graph API)

  • Base URL: https://graph.instagram.com
  • Auth: Access Token
  • Get key: https://developers.facebook.com/docs/instagram-api

8. Wikimedia Commons (Free, no key needed)

# Search images via MediaWiki API
curl "https://commons.wikimedia.org/w/api.php?action=query&generator=search&gsrnamespace=6&gsrsearch=cat+breeds&gsrlimit=10&prop=imageinfo&iiprop=url|extmetadata&format=json"

# Get random featured picture
curl "https://commons.wikimedia.org/w/api.php?action=query&list=random&rnnamespace=6&rnlimit=5&prop=imageinfo&iiprop=url&format=json"

9. AI Image Generation (When You Can't Find What You Need)

Built-in OpenClaw image generation

Use the image_generate tool directly - supports DALL-E, Gemini, and other providers.

MCP Image Generators

  • mcp-image (Nano Banana): AI image generation via Gemini

- npx mcp-image with GEMINI_API_KEY

  • fal.ai MCP: Multi-provider image generation
  • Flux MCP: Flux model image generation

10. Image Search Strategy Guide

By Use Case

Use CaseBest SourcesNotes
Academic papersSemantic Scholar, arXiv, PubMedCheck open access for figures
Blog postsUnsplash, Pexels, PixabayFree commercial use
PresentationsUnsplash, Wikimedia CommonsHigh resolution preferred
Social mediaUnsplash, Pexels, PixabaySquare/vertical formats
Product imagesPlatform APIs, scraperCheck ToS
News articlesNewsAPI, Tavily news, Google NewsurlToImage field
Scientific figuresFigureLabs, BioRender, matplotlibGenerate, don't search
Data visualizationsReddit r/dataisbeautiful, ObservableCheck licensing
Icons/illustrationsPixabay (vectors), Flaticon, Noun ProjectSVG preferred
BackgroundsUnsplash, Pixabay (backgrounds category)High resolution

Best Practices

  1. Always check licensing - Use CC0/public domain when possible for publications
  2. Attribute creators - Even when not required, it's good practice
  3. Prefer high resolution - Download raw or full size for print/publication
  4. Batch your requests - Respect API rate limits
  5. Cache results - Don't re-fetch the same images
  6. Validate URLs - Image URLs can expire; download promptly
  7. Use include_images - When extracting web content, request images

Environment Variable Reference

Set these in your shell environment or .env file:

# Free Stock Photos
UNSPLASH_ACCESS_KEY=""     # https://unsplash.com/developers
PEXELS_API_KEY=""          # https://www.pexels.com/api/
PIXABAY_API_KEY=""         # https://pixabay.com/api/docs/
FLICKR_API_KEY=""          # https://www.flickr.com/services/apps/

# Search Engines
GOOGLE_API_KEY=""          # https://developers.google.com/custom-search
GOOGLE_CX=""               # Custom Search Engine ID
BING_API_KEY=""            # https://www.microsoft.com/en-us/bing/apis/

# News
NEWSAPI_KEY=""             # https://newsapi.org
GNEWS_API_KEY=""           # https://gnews.io

# AI Generation
GEMINI_API_KEY=""          # https://aistudio.google.com
OPENAI_API_KEY=""          # https://platform.openai.com

平台分布

OpenClaw

93.24%
按下载量换算899

安全审计

VirusTotal

通过

ClawScan

可疑

Static analysis

通过

权限和风险

接触敏感数据

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

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