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Aliyun Wan Videoedit

Community
cinience
aliyun-wan-videoedit

Use when editing videos with DashScope Wan 2.7 video editing model (wan2.7-videoedit). Use when implementing video style transfer, instruction-based video editing with optional reference images, or video content modification via the video-synthesis async API.

Overview

Publishercinience
Repositoryalicloud-skills
Skill namealiyun-wan-videoedit
Stars
397
Forks
34
Bundled files
3
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 3 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by cinience on GitHub. Read the source before you install it.

Installation

Install the Aliyun Wan Videoedit AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/cinience/alicloud-skills/tree/main/skills/ai/video/aliyun-wan-videoedit
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/cinience/alicloud-skills.git /tmp/alicloud-skills
mkdir -p .claude/skills
cp -r /tmp/alicloud-skills/skills/ai/video/aliyun-wan-videoedit .claude/skills/aliyun-wan-videoedit
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Aliyun Wan Videoedit in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Aliyun Wan Videoedit on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Aliyun Wan Videoedit is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Wan 2.7 Video Editing

Validation

bash
mkdir -p output/aliyun-wan-videoedit
python -m py_compile skills/ai/video/aliyun-wan-videoedit/scripts/edit_video.py && echo "py_compile_ok" > output/aliyun-wan-videoedit/validate.txt

Pass criteria: command exits 0 and output/aliyun-wan-videoedit/validate.txt is generated.

Output And Evidence

  • Save task IDs, polling responses, and final video URLs to output/aliyun-wan-videoedit/.
  • Keep at least one end-to-end run log for troubleshooting.

Prerequisites

  • Install SDK (recommended in a venv):
bash
python3 -m venv .venv
. .venv/bin/activate
python -m pip install dashscope
  • Set DASHSCOPE_API_KEY in your environment, or add dashscope_api_key to ~/.alibabacloud/credentials.

Critical model names

  • wan2.7-videoedit — supports style transfer and instruction-based video editing

Capabilities

CapabilityDescriptionRequired media
Style transferConvert video to a different visual style (clay, anime, etc.)video only
Instruction editingEdit video content with text instructions and optional reference imagesvideo + optional reference_image (up to 3)

API endpoint (async only)

POST https://dashscope.aliyuncs.com/api/v1/services/aigc/video-generation/video-synthesis

Required headers:

  • Authorization: Bearer $DASHSCOPE_API_KEY
  • Content-Type: application/json
  • X-DashScope-Async: enable

Singapore endpoint: replace dashscope.aliyuncs.com with dashscope-intl.aliyuncs.com.

Normalized interface

Request

  • prompt (string, optional) — up to 5000 characters, describes desired editing
  • negative_prompt (string, optional) — up to 500 characters
  • media (array, required) — media objects with type and url fields:
    • type: video (required, exactly 1) | reference_image (optional, up to 3)
    • url: public URL (HTTP/HTTPS) or OSS temporary URL
  • resolution (string, optional) — 720P or 1080P (default: 1080P)
  • ratio (string, optional) — output aspect ratio: 16:9, 9:16, 1:1, 4:3, 3:4. If omitted, follows input video ratio.
  • duration (integer, optional) — truncate input video to this length in seconds, range [2, 10]. Default 0 (use input video duration).
  • audio_setting (string, optional) — auto (default, AI decides) or origin (keep original audio)
  • prompt_extend (boolean, optional) — AI prompt rewriting (default: true)
  • watermark (boolean, optional) — add "AI generated" watermark (default: false)
  • seed (integer, optional) — range [0, 2147483647]

Media input limits

Video (type=video):

  • Formats: mp4, mov
  • Duration: 2-10s
  • Resolution: [240, 4096] pixels per side
  • Aspect ratio: 1:8 to 8:1
  • Max size: 100MB

Reference images (type=reference_image):

  • Formats: JPEG, JPG, PNG (no transparency), BMP, WEBP
  • Resolution: [240, 8000] pixels per side
  • Aspect ratio: 1:8 to 8:1
  • Max size: 20MB
  • Maximum 3 reference images

Resolution output table

ResolutionRatioOutput (W*H)
720P16:91280*720
720P9:16720*1280
720P1:1960*960
720P4:31104*832
720P3:4832*1104
1080P16:91920*1080
1080P9:161080*1920
1080P1:11440*1440
1080P4:31648*1248
1080P3:41248*1648

Response (task creation)

  • output.task_id (string) — use for polling, valid 24 hours
  • output.task_status (string) — PENDING | RUNNING | SUCCEEDED | FAILED | CANCELED
  • request_id (string)

Response (task result)

  • output.video_url (string) — edited video URL
  • usage.video_count (integer)
  • usage.video_duration (integer) — duration in seconds

Quick start (Python + HTTP)

python
import os
import json
import time
import requests

API_KEY = os.getenv("DASHSCOPE_API_KEY")
BASE_URL = "https://dashscope.aliyuncs.com/api/v1"

def create_videoedit_task(req: dict) -> str:
    """Create a video editing task and return task_id."""
    payload = {
        "model": "wan2.7-videoedit",
        "input": {
            "prompt": req.get("prompt", ""),
            "media": req["media"],
        },
        "parameters": {
            "resolution": req.get("resolution", "1080P"),
            "prompt_extend": req.get("prompt_extend", True),
            "watermark": req.get("watermark", False),
        },
    }
    if req.get("negative_prompt"):
        payload["input"]["negative_prompt"] = req["negative_prompt"]
    if req.get("ratio"):
        payload["parameters"]["ratio"] = req["ratio"]
    if req.get("duration"):
        payload["parameters"]["duration"] = req["duration"]
    if req.get("audio_setting"):
        payload["parameters"]["audio_setting"] = req["audio_setting"]
    if req.get("seed") is not None:
        payload["parameters"]["seed"] = req["seed"]

    resp = requests.post(
        f"{BASE_URL}/services/aigc/video-generation/video-synthesis",
        headers={
            "Authorization": f"Bearer {API_KEY}",
            "Content-Type": "application/json",
            "X-DashScope-Async": "enable",
        },
        json=payload,
    )
    resp.raise_for_status()
    data = resp.json()
    return data["output"]["task_id"]


def poll_task(task_id: str, interval: int = 15) -> dict:
    """Poll until task completes. Returns final response."""
    while True:
        resp = requests.get(
            f"{BASE_URL}/tasks/{task_id}",
            headers={"Authorization": f"Bearer {API_KEY}"},
        )
        resp.raise_for_status()
        data = resp.json()
        status = data["output"]["task_status"]
        if status in ("SUCCEEDED", "FAILED", "CANCELED"):
            return data
        time.sleep(interval)

Usage examples

python
# Style transfer — convert to clay style
media = [{"type": "video", "url": "https://example.com/input.mp4"}]
task_id = create_videoedit_task({
    "prompt": "将整个画面转换为黏土风格",
    "media": media,
    "resolution": "720P",
})

# Instruction editing with reference image
media = [
    {"type": "video", "url": "https://example.com/input.mp4"},
    {"type": "reference_image", "url": "https://example.com/hat.jpg"},
]
task_id = create_videoedit_task({
    "prompt": "为人物换上酷闪的衣服,再戴参考图里的帽子",
    "media": media,
    "audio_setting": "origin",
})

Error handling

ErrorLikely causeAction
401/403Missing or invalid DASHSCOPE_API_KEYCheck env var or credentials file
400 InvalidParameterBad resolution, missing video, too many reference imagesValidate parameters
"does not support synchronous calls"Missing X-DashScope-Async: enable headerAdd required header
429Rate limit or quotaRetry with backoff

Output location

  • Default output: output/aliyun-wan-videoedit/videos/
  • Override base dir with OUTPUT_DIR.

Anti-patterns

  • Do not use model names other than wan2.7-videoedit.
  • Do not call this API synchronously — async header is required.
  • Do not pass more than 1 video or more than 3 reference images.
  • Video URLs expire after 24 hours; download and persist immediately.
  • Do not use this API for video generation — use aliyun-wan-i2v instead.

Workflow

  1. Confirm user intent: style transfer or instruction-based editing.
  2. Prepare media array with video (required) and optional reference images.
  3. Create async task and poll for results.
  4. Download and save edited video before URL expiration.

References

  • See references/api_reference.md for full HTTP API details.
  • See references/sources.md for source links.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Aliyun Wan Videoedit AI skill do?

Use when editing videos with DashScope Wan 2.7 video editing model (wan2.7-videoedit). Use when implementing video style transfer, instruction-based video editing with optional reference images, or video content modification via the video-synthesis async API.

Why use Aliyun Wan Videoedit on TypingMind?

Because you install it once and use it with any model. Aliyun Wan Videoedit is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Aliyun Wan Videoedit in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/cinience/alicloud-skills/tree/main/skills/ai/video/aliyun-wan-videoedit. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Aliyun Wan Videoedit?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Aliyun Wan Videoedit?

As many as you like. As long as a model supports skills, you can use Aliyun Wan Videoedit with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Aliyun Wan Videoedit AI skill free?

Yes. It is published on GitHub by cinience under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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