Token导航 LogoToken导航TokenDH.com
效率敏感数据clawhub未标认证来源可访问clear审计提醒

mm-output毫米输出

Agent Skill

mm-output 用于整理文档、README、Markdown 和说明材料,适合在 OpenClaw 中需要把零散信息整理成结构清晰的文档时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

7,463

周安装

299

GitHub Stars

1

下载量

2,416
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:mm-output(毫米输出)
来源仓库:https://github.com/yuriyanzexuan/mm-output
安装命令:
openclaw skills install mm-output
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 mm-output

简介

将 PDF/Markdown 文件解析为结构化 HTML 海报,支持多格式导出及图像生成。

  • 适合整理文档、README 和说明材料,快速生成可视化内容如海报或幻灯片。
  • 可结合 Gemini 图像生成器创建视觉素材,输出 PDF、PNG、DOCX、PPTX 等格式。
  • 使用前需确认权限范围、维护状态及是否触发联网或文件读写操作。
  • 安装命令:openclaw skills install mm-output,仅适用于 OpenClaw 宿主。

SKILL.md

name
postergen-parser
description
>-

PosterGen Parser Unit

Parse PDF or Markdown documents into styled HTML posters with LLM-based rendering, then convert to PDF, PNG, DOCX, and PPTX. Additionally, generate poster images and slides images using Gemini's native image generation API.

Prerequisites

  • Python 3.12+
  • LLM API key (OpenAI, Gemini, or Qwen)
  • System dependencies (fonts, Chromium libs)

Quick Start

1. Install

bash install.sh

This will:

  • Install UV (Python package manager)
  • Install Python 3.12
  • Install system dependencies (fonts, Chromium libraries)
  • Create virtual environment and install dependencies
  • Install Playwright Chromium browser
  • Create .env configuration file

2. Configure

Edit .env file with your API credentials:

# OpenAI
TEXT_MODEL="gpt-4.1-2025-04-14"
OPENAI_API_KEY="your-key"
OPENAI_BASE_URL="https://api.openai.com/v1"

# Qwen (MAAS)
TEXT_MODEL="qwen3-vl-235b-a22b-instruct"
OPENAI_API_KEY="your-key"
OPENAI_BASE_URL="https://maas.devops.xiaohongshu.com/v1"

# Gemini
TEXT_MODEL="gemini-3-pro-preview"
RUNWAY_API_KEY="your-key"

3. Run

# Full pipeline: PDF → HTML → PDF/PNG/DOCX
uv run python run.py --pdf_path input.pdf --output_dir ./output

# Markdown → HTML with template
uv run python run.py --md_path input.md --output_dir ./output --template templates/doubao.txt

# Generate poster image (Gemini)
uv run python run.py --md_path input.md --output_dir ./output \
  --output_type poster_image --style academic --density medium

# Generate slides images (Gemini)
uv run python run.py --md_path input.md --output_dir ./output \
  --output_type slides_image --style doraemon --slides_length medium

# Generate XHS slides
uv run python run.py --md_path input.md --output_dir ./output \
  --output_type xhs_slides --style academic --slides_length short

# Convert HTML to multi-modal
uv run python -m mm_output.cli input.html --format all --output-dir ./mm_outputs

4. Run Tests

bash run.sh

Command Reference

Main Entry: run.py

CommandDescription
uv run python run.py --pdf_path FILE --output_dir DIRParse PDF to HTML + multi-modal outputs
uv run python run.py --md_path FILE --output_dir DIRParse Markdown to HTML + multi-modal outputs
--output_type poster_imageGenerate poster image (Gemini)
--output_type slides_imageGenerate slides images (Gemini, 16:9)
--output_type xhs_slidesGenerate XHS slides (Gemini, 9:16 + HTML)
--template templates/NAME.txtUse specific template
--style {academic,doraemon,minimal}Visual style for image generation
--density {sparse,medium,dense}Content density for poster_image
--slides_length {short,medium,long}Slide count: short=5-8, medium=8-12, long=12-15
--text_model MODELOverride LLM model
--language {auto,zh,en}Output language

Multi-modal Conversion: mm_output.cli

# Convert HTML to specific format
uv run python -m mm_output.cli input.html --format pdf --output-dir ./out

# Convert to all formats
uv run python -m mm_output.cli input.html --format all --output-dir ./out

# Supported formats: pdf, png, docx, pptx, all

Environment Setup (UV)

This project uses UV for Python package management.

Manual Setup (if install.sh fails)

Note: The uv.lock file is renamed to uv.lock.txt to avoid tracking. Before using UV, rename it back: ``bash mv uv.lock.txt uv.lock ``
# Install UV
curl -LsSf https://astral.sh/uv/install.sh | sh

# Install Python 3.12
uv python install 3.12

# Create virtual environment
uv venv .venv --python 3.12

# Rename lock file back and sync dependencies
mv uv.lock.txt uv.lock
uv sync

# Install Playwright browsers
uv run playwright install chromium

Dependency Management

# Sync dependencies from pyproject.toml
uv sync

# Add new dependency
uv add package_name

# Lock dependencies
uv lock

Templates

Available templates in templates/:

TemplateDescription
doubao.txtDefault style
doubao_dark.txtDark theme
doubao_minimal.txtMinimal clean
doubao_newspaper.txtMulti-column layout
doubao_enterprise_blue.txtCorporate style
doubao_refine.txtRefined style
report_web.txtWeb report style
report_web_reduced.txtSimplified web style

System Dependencies

Required for non-Docker installation:

# Fonts
fonts-noto-cjk fonts-wqy-zenhei fonts-wqy-microhei

# Chromium libraries
libnss3 libnspr4 libatk1.0-0 libatk-bridge2.0-0 libcups2
libdrm2 libxcomposite1 libxdamage1 libxrandr2 libgbm1
libxshmfence1 libasound2 libpangocairo-1.0-0 libgtk-3-0
libx11-xcb1 libxcursor1 libxi6 libxss1 libxtst6

Install via:

bash install.sh

Troubleshooting

  • Chrome not found: Leave CHROME_EXECUTABLE_PATH empty to use Playwright's Chromium
  • Chinese characters garbled: Install fonts-noto-cjk
  • LLM API errors: Check .env has correct TEXT_MODEL and API key
  • Slow first run: Model downloads are cached; subsequent runs are faster

Project Structure

.
├── run.py              # Main entry point
├── run.sh              # Test script
├── parser_unit.py      # PDF/Markdown parsing
├── renderer_unit.py    # HTML rendering

├── mm_output/          # Multi-modal conversion (HTML→PDF/PNG/DOCX/PPTX)
├── paper2slides/       # Image generation (Poster/Slides via Gemini)
├── templates/          # HTML templates
├── install.sh          # Setup script (no options needed)
├── pyproject.toml      # UV configuration
├── uv.lock             # Locked dependencies
└── README.md           # Documentation

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

85.57%
按下载量换算2,067

安全审计

VirusTotal

通过

ClawScan

可疑

Static analysis

通过

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

继续浏览同类 Skills