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muleroutermulerouter 视频

Agent Skill

mulerouter 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。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

38,408

周安装

1,633

GitHub Stars

2

下载量

13,456
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:mulerouter(mulerouter 视频)
来源仓库:https://github.com/misaka43fd/mulerouter
安装命令:
openclaw skills install mulerouter
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 mulerouter

简介

MuleRouter 提供多模态 API 支持,用于生成图像与视频内容,涵盖文本到图像、图像到视频等多种模式。

  • 适用于 OpenClaw 中需要快速调用 AI 生成视觉内容的场景,如创意设计、媒体制作等。
  • 通过集成 MuleRouter,Agent 可直接调用其接口完成内容创作,无需额外配置外部服务。
  • 安装命令为 openclaw skills install mulerouter,需确保具备网络访问权限以连接后端 API。
  • 使用前应检查目标环境是否支持相关依赖,并注意输出资源的存储与版权合规性。

SKILL.md

name
mulerouter
description
Generates images and videos using MuleRouter or MuleRun multimodal APIs. Text-to-Image, Image-to-Image, Text-to-Video, Image-to-Video, video editing (VACE, keyframe interpolation). Use when the user wants to generate, edit, or transform images and videos using AI models like Wan2.6, Veo3, Nano Banana Pro, Sora2, Midjourney.
compatibility
Requires Python 3.10+, uv, MULEROUTER_API_KEY env var, and one of MULEROUTER_BASE_URL or MULEROUTER_SITE env var. Needs network access to api.mulerouter.ai or api.mulerun.com. The API key is sent in Authorization headers to the configured endpoint.
homepage
https://github.com/mulerouter/mulerouter-skills
allowed-tools
Bash(uv run *) Bash(uv sync *) Read
metadata
clawdbot
requires
env
["MULEROUTER_API_KEY"]
env_one_of
["MULEROUTER_BASE_URL", "MULEROUTER_SITE"]
bins
["uv", "python3"]
primaryEnv
MULEROUTER_API_KEY
install
uv sync
files
["scripts/*", "models/*", "core/*", "pyproject.toml"]

MuleRouter API

Generate images and videos using MuleRouter or MuleRun multimodal APIs.

Required Environment Variables

This skill requires the following environment variables to be set before use:

VariableRequiredDescription
MULEROUTER_API_KEYYesAPI key for authentication (get one here)
MULEROUTER_BASE_URLYes*Custom API base URL (e.g., https://api.mulerouter.ai). Takes priority over SITE.
MULEROUTER_SITEYes*API site: mulerouter or mulerun. Used if BASE_URL is not set.

*At least one of MULEROUTER_BASE_URL or MULEROUTER_SITE must be set.

The API key is included in Authorization: Bearer headers when making network calls to the configured API endpoint.

If any of these variables are missing, the scripts will fail with a configuration error. Check the Configuration section below to set them up.

Configuration Check

Before running any commands, verify the environment is configured:

Step 1: Check for existing configuration

Run the built-in config check script:

uv run python -c "from core.config import load_config; load_config(); print('Configuration OK')"

If this prints "Configuration OK", skip to Step 3. If it raises a ValueError, proceed to Step 2.

Step 2: Configure if needed

If the variables above are not set, ask the user to provide their API key and preferred endpoint.

Create a .env file in the skill's working directory:

# Option 1: Use custom base URL (takes priority over SITE)
MULEROUTER_BASE_URL=https://api.mulerouter.ai
MULEROUTER_API_KEY=your-api-key

# Option 2: Use site (if BASE_URL not set)
# MULEROUTER_SITE=mulerun
# MULEROUTER_API_KEY=your-api-key

Note: MULEROUTER_BASE_URL takes priority over MULEROUTER_SITE. If both are set, MULEROUTER_BASE_URL is used.

Note: The skill only loads variables prefixed with MULEROUTER_ from the .env file. Other variables in the file are ignored.

Important: Do NOT use export shell commands to set credentials. Use a .env file or ensure the variables are already present in your shell environment before invoking the skill.

Step 3: Using uv to run scripts

The skill uses uv for dependency management and execution. Make sure uv is installed and available in your PATH.

Run uv sync to install dependencies.

Quick Start

1. List available models

uv run python scripts/list_models.py

2. Check model parameters

uv run python models/alibaba/wan2.6-t2v/generation.py --list-params

3. Generate content

Text-to-Video:

uv run python models/alibaba/wan2.6-t2v/generation.py --prompt "A cat walking through a garden"

Text-to-Image:

uv run python models/alibaba/wan2.6-t2i/generation.py --prompt "A serene mountain lake"

Image-to-Video:

uv run python models/alibaba/wan2.6-i2v/generation.py --prompt "Gentle zoom in" --image "https://example.com/photo.jpg" #remote image url
uv run python models/alibaba/wan2.6-i2v/generation.py --prompt "Gentle zoom in" --image "/path/to/local/image.png" #local image path

Image Input

For image parameters (--image, --images, etc.), prefer local file paths over base64.

# Preferred: local file path (auto-converted to base64)
--image /tmp/photo.png

--images ["/tmp/photo.png"]

Local file paths are validated before reading: only files with recognized image extensions (.png, .jpg, .jpeg, .gif, .bmp, .webp, .tiff, .tif, .svg, .ico, .heic, .heif, .avif) are accepted. Paths pointing to sensitive system directories or non-image files are rejected. Valid image files are converted to base64 and sent to the API, avoiding command-line length limits that occur with raw base64 strings.

Workflow

  1. Check configuration: verify MULEROUTER_API_KEY and either MULEROUTER_BASE_URL or MULEROUTER_SITE are set
  2. Install dependencies: run uv sync
  3. Run uv run python scripts/list_models.py to discover available models
  4. Run uv run python models/<path>/<action>.py --list-params to see parameters
  5. Execute with appropriate parameters
  6. Parse output URLs from results

Model Selection

When listing models, each model's tags (e.g., [SOTA]) are displayed by default next to its name. Tags help identify model characteristics at a glance — for example, SOTA indicates a state-of-the-art model.

You can also filter models by tag using --tag:

uv run python scripts/list_models.py --tag SOTA

If you are unsure which model to use, present the available options to the user and let them choose. Use the AskUserQuestion tool (or equivalent interactive prompt) to ask the user which model they prefer. For example, if the user asks to "generate an image" without specifying a model, list the relevant image generation models with their tags and descriptions, and ask the user to pick one.

Tips

  1. For an image generation model, a suggested timeout is 5 minutes.
  2. For a video generation model, a suggested timeout is 15 minutes.

References

适合场景

01

文本生成图片

02

图片风格化

03

产品图和创意图

04

需要 FLUX 模型时

能力概览

能力 1

调用 FLUX 图像模型

能力 2

支持文本生图和图像改写

能力 3

覆盖 LoRA 或风格适配

能力 4

适合创意视觉生成

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

89.46%
按下载量换算12,038

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

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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。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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