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图像处理可写文件clawhub未标认证来源可访问clear审计通过

image-breaker图像破坏者

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

7,944

周安装

331

GitHub Stars

公开资料未说明

下载量

2,648
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:image-breaker(图像破坏者)
来源仓库:https://github.com/biohackerrrrrr/image-breaker
安装命令:
openclaw skills install image-breaker
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-breaker

简介

image-breaker 从 PDF、图像与 URL 提取内容并转为 Obsidian 笔记。

  • 支持结构化 Markdown 输出,便于知识管理。
  • 通过文件或链接输入触发,自动解析与分类内容。image-breaker 属于图像处理类 Skill,可作为该场景下的辅助能力补充。
  • 建议确认 OCR 精度及是否支持多语言识别。
  • 可结合来源仓库和 README 进一步核验同步机制。

SKILL.md

name
image-breaker
description
Extract and break down content from web documents, PDFs, images, and URLs into structured markdown notes stored locally and synced to Obsidian. Use when the user shares a URL, PDF, screenshot, or document and wants the content converted to organized notes with proper tagging and categorization.

Image Breaker

Convert documents, PDFs, images, and web content into structured markdown notes saved to workspace and synced to Obsidian.

Workflow

1. Extract Content

For URLs/PDFs:

Use web_fetch to extract content

For images:

Use image tool to analyze and extract text

For already-analyzed content:

User may paste content directly or you've already extracted it

2. Structure the Content

Convert raw content into organized markdown:

Sections to create:

  • Overview - What is this document/content about?
  • Key Points - Bullet list of main takeaways
  • Detailed Breakdown - Organized subsections with headers
  • Reference Ranges/Standards (if applicable) - Tables for numerical data
  • Action Items (if applicable) - What to do with this information
  • Source - Original URL or document name

Formatting guidelines:

  • Use tables for numerical data (reference ranges, standards, comparisons)
  • Use bullet lists for key points
  • Use headers (##, ###) for organization
  • Include code blocks for technical content
  • Bold important terms on first mention

3. Save and Sync

Create the markdown note with proper frontmatter and save to workspace:

# Prepare frontmatter
date = "2026-02-10"
tags = ["research", "bloodwork", "nmr"]  # Auto-assigned based on content
title = "NMR Lipid Panel Reference Ranges"

# Build full markdown content
content = f"""---
date: {date}
tags:
  - {tag1}
  - {tag2}
  - {tag3}
source: {original_url_or_source}
type: image-breaker-note
---

# {title}

## Overview
[Brief description of what this document is]

## Key Points
- Point 1
- Point 2
- Point 3

## [Main Section]
[Detailed content with subsections]

## Reference
- **Source:** [URL or document name]
- **Extracted:** {date}
"""

# Save to workspace
output_dir = "research/image-breaker-notes"  # Default
# or user-specified: "research/bloodwork", "content/references", etc.

# Write file
filepath = f"{output_dir}/{date}-{slugified-title}.md"
write(filepath, content)

# Sync to Obsidian (using obsidian-sync skill)
exec: python3 skills/obsidian-sync/scripts/sync_to_obsidian.py {filepath} /Users/biohacker/Desktop/Connections ImageBreaker

Tag Assignment

Auto-assign 3 most relevant tags based on content:

Common tags:

  • research - Academic papers, studies, references
  • bloodwork - Lab results, biomarkers, panels
  • nmr - NMR lipid panels specifically
  • cholesterol - Cholesterol and lipid-related
  • peptides - BPC-157, TB-500, etc.
  • supplements - Vitamins, minerals, compounds
  • protocols - Treatment/optimization protocols
  • founders - Business/entrepreneur health content
  • longevity - Anti-aging, healthspan
  • performance - Cognitive/physical optimization
  • training - Exercise, workouts
  • toku - Nattokinase, Toku Flow related

Prioritize specific tags over generic ones.

Output Directories

Default: research/image-breaker-notes/

Content-specific alternatives:

  • Research documents → research/papers/ or research/protocols/
  • Lab results → research/bloodwork/
  • Marketing materials → content/references/
  • Training content → research/training/
  • Business documents → projects/business-docs/

Choose the most appropriate directory based on content type.

Example Usage

User provides Labcorp NMR document URL:

  1. Extract content using web_fetch
  2. Structure into markdown with:

- Overview of what NMR measures - Key reference ranges table - Interpretation guide - Comparison to standard lipids

  1. Assign tags: bloodwork, nmr, research
  2. Save to research/image-breaker-notes/2026-02-10-nmr-lipid-panel-reference.md
  3. Sync to Obsidian vault at ImageBreaker/2026-02-10-nmr-lipid-panel-reference.md
  4. Report to user with file path and Obsidian link

Best Practices

  • Always extract content first - Use web_fetch or image tool before structuring
  • Create comprehensive notes - Include context, not just raw data
  • Use tables for data - Reference ranges, comparisons, standards
  • Tag intelligently - Maximum 3 tags, most specific/relevant
  • Choose output directory wisely - Match content type to workspace organization
  • Auto-sync by default - User wants notes in Obsidian for cross-referencing
  • Report file location - Give user both workspace and Obsidian paths

Output Message Template

After completing the workflow:

✅ **Document broken down and saved**

📝 **Title:** [Note Title]
📂 **Location:** research/image-breaker-notes/2026-02-10-note-title.md
🔗 **Obsidian:** ImageBreaker/2026-02-10-note-title.md
🏷️  **Tags:** tag1, tag2, tag3

**Sections created:**
- Overview
- Key Points  
- [Main sections listed]
- Reference

The note is now in your Obsidian vault for tagging and cross-referencing.

Integration with Other Skills

Obsidian Sync: Automatically called after note creation Paper Fetcher: If user provides DOI, use paper-fetcher first, then break down the PDF Research Automation: Can batch-process multiple documents from research runs

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

79.13%
按下载量换算2,095

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

通过

权限和风险

可写文件

该 Skill 可能写入或修改本地文件,使用前需要确认目标目录和修改范围。

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。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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