據 AlphaMoat 統計,截至 2026-09,MuleRouter 累計下載 2,227 次、獲 2 個星標,在全部 5,078 個 AI 技能中排名第 4,271 位,在「設計創意」分類(512 個)中排名第 424 位。
| 平台 | 累計安裝 | 累計下載 | 星標 | 評論 |
|---|---|---|---|---|
| ClawHub ↗ | 64 | 2193 | 2 | - |
| SkillHub ↗ | 143 | 2227 | 2 | — |
各平台的計數口徑不同,統計時間不一致。
該技能使用配置的 API 端點處理提示詞和媒體。API 金鑰會隨請求傳送,指定的本地圖片也可能被上傳。除非已完全信任,否則不要使用自定義 BASE_URL;先檢查端點、模型引數和待上傳檔案,並確保金鑰安全。 需要 Python、uv、MULEROUTER_API_KEY 以及 MULEROUTER_BASE_URL 或 MULEROUTER_SITE。
資料釋出於 2026-10-29
以下是根據技能文件生成的示例,不代表實際執行結果。
2026-09 起統計方法調整:同名技能按作者分別統計,並覆蓋全部技能。圖中虛線為調整前的月份,與之後的資料不直接比較,也不計算跨越調整的增長。
| 月份 | 累計下載 | 環比 | 星標 |
|---|---|---|---|
| 2026-09 | 2227 | — | 2 |
| 2026-08 | 2074 | +6.0% | 2 |
| 2026-07 | 1957 | +13.8% | 2 |
| 2026-06 | 1719 | +9.5% | 2 |
| 2026-05 | 1570 | — | 2 |
| 2026-03 | 1173 | — | 2 |
以下是作者釋出的原始檔案,按純文字顯示,本站未做修改或稽核;其中的連結不可點選,命令請核實後再執行。抓取於 2026-10-23。
---
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:
| Variable | Required | Description |
|----------|----------|-------------|
| `MULEROUTER_API_KEY` | **Yes** | API key for authentication ([get one here](https://www.mulerouter.ai/app/api-keys?utm_source=github_claude_plugin)) |
| `MULEROUTER_BASE_URL` | **Yes*** | Custom API base URL (e.g., `https://api.mulerouter.ai`). Takes priority over SITE. |
| `MULEROUTER_SITE` | **Yes*** | 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:
```bash
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:
```env
# 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
```bash
uv run python scripts/list_models.py
```
### 2. Check model parameters
```bash
uv run python models/alibaba/wan2.6-t2v/generation.py --list-params
```
### 3. Generate content
**Text-to-Video:**
```bash
uv run python models/alibaba/wan2.6-t2v/generation.py --prompt "A cat walking through a garden"
```
**Text-to-Image:**
```bash
uv run python models/alibaba/wan2.6-t2i/generation.py --prompt "A serene mountain lake"
```
**Image-to-Video:**
```bash
uv run python models/alibaba/wan2.6-i2v/generation.py --prompt "Gentle zoom in" --image "https://example.com/photo.jpg" #remote image url
```
```bash
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.
```bash
# 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`:
```bash
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
- [REFERENCE.md](references/REFERENCE.md) - API configuration and CLI options
- [MODELS.md](references/MODELS.md) - Complete model specifications
ContextWeave 架構圖一鍵生成
用 Nano Banana Pro 生成或編輯圖片的指令碼技能。
用 whisper 命令列工具在本機將音訊轉成文字或進行翻譯。
透過 MCP 服務生成與編輯圖表的技能。
面向介面設計的思路與落地建議技能,覆蓋佈局、互動流程、設計令牌與前端改動。
面向多家影片模型 API 的 AI 影片生成與編排技能。
用 ffmpeg 從影片裡取出畫面幀,或生成便於快速檢視的縮圖。
用本地 Whisper 把音訊檔案轉成文字,模型下載完成後可完全離線執行。
資料月份:2026-09 · 下載/星標/安裝數聚合自公開技能註冊庫(ClawHub、SkillHub),按月更新。口徑說明見資料方法。