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图像处理可写文件unknown未标认证来源可访问许可证需确认审计未展示

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

194

周安装

8

下载量

63
Local Agent

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。

来源数

2

许可证

unknown

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://skills.volces.com
仓库路径:image-breaker
安装命令:
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。当前暂无明确安装命令,请以来源页面说明为准。

简介

image-breaker 用于辅助图像生成和图片编辑,支持视觉素材处理和模型工作流。

  • 它能根据文本提示生成图片、处理背景或优化视觉提示词,适用于创意项目。
  • 使用时需明确输入图片、输出格式和版权要求,Agent 将调用相关图像工具。
  • 涉及人物、品牌或商业素材时应核对授权和真实性,避免违规内容输出。
  • 安装前请确认模型限制和输出边界,确保符合内容安全政策。

SKILL.md

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

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

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

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

Local Agent

73.55%
按下载量换算46

安全审计

暂无安全审计结果可展示。

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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