Agent skill

Aliyun Wan Videoedit

A skill your agent uses when editing videos with DashScope Wan 2.7 video editing model (wan2.7-videoedit).

Install Aliyun Wan Videoedit

skills CLI

$ npx skills add cinience/alicloud-skills --skill aliyun-wan-videoedit -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI

$ gh skill install cinience/alicloud-skills aliyun-wan-videoedit --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy

$ git clone --depth 1 https://github.com/cinience/alicloud-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/ai/video/aliyun-wan-videoedit .claude/skills/aliyun-wan-videoedit && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Paste into Claude Code

Install the "aliyun-wan-videoedit" agent skill from https://github.com/cinience/alicloud-skills/tree/main/skills/ai/video/aliyun-wan-videoedit into .claude/skills/aliyun-wan-videoedit/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "aliyun-wan-videoedit", then confirm the skill loads.

Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

$skill-installer

$skill-installer install https://github.com/cinience/alicloud-skills/tree/main/skills/ai/video/aliyun-wan-videoedit

Type this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.

skills CLI

$ npx skills add cinience/alicloud-skills --skill aliyun-wan-videoedit -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install cinience/alicloud-skills aliyun-wan-videoedit --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/cinience/alicloud-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/ai/video/aliyun-wan-videoedit .agents/skills/aliyun-wan-videoedit && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "aliyun-wan-videoedit" agent skill from https://github.com/cinience/alicloud-skills/tree/main/skills/ai/video/aliyun-wan-videoedit into .agents/skills/aliyun-wan-videoedit/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "aliyun-wan-videoedit", then confirm the skill loads.

Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add cinience/alicloud-skills --skill aliyun-wan-videoedit -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install cinience/alicloud-skills aliyun-wan-videoedit --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/cinience/alicloud-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/ai/video/aliyun-wan-videoedit .cursor/skills/aliyun-wan-videoedit && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "aliyun-wan-videoedit" agent skill from https://github.com/cinience/alicloud-skills/tree/main/skills/ai/video/aliyun-wan-videoedit into .cursor/skills/aliyun-wan-videoedit/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "aliyun-wan-videoedit", then confirm the skill loads.

Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

gemini skills install

$ gemini skills install https://github.com/cinience/alicloud-skills.git --path skills/ai/video/aliyun-wan-videoedit

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add cinience/alicloud-skills --skill aliyun-wan-videoedit -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install cinience/alicloud-skills aliyun-wan-videoedit --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/cinience/alicloud-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/ai/video/aliyun-wan-videoedit .gemini/skills/aliyun-wan-videoedit && rm -rf skills-src

Use ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.

Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/

Paste into Gemini CLI

Install the "aliyun-wan-videoedit" agent skill from https://github.com/cinience/alicloud-skills/tree/main/skills/ai/video/aliyun-wan-videoedit into .gemini/skills/aliyun-wan-videoedit/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "aliyun-wan-videoedit", then confirm the skill loads.

Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

GitHub CLI

$ gh skill install cinience/alicloud-skills aliyun-wan-videoedit

Installs for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).

skills CLI

$ npx skills add cinience/alicloud-skills --skill aliyun-wan-videoedit -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/cinience/alicloud-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/ai/video/aliyun-wan-videoedit .github/skills/aliyun-wan-videoedit && rm -rf skills-src

Use ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.

GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/

Paste into GitHub Copilot

Install the "aliyun-wan-videoedit" agent skill from https://github.com/cinience/alicloud-skills/tree/main/skills/ai/video/aliyun-wan-videoedit into .github/skills/aliyun-wan-videoedit/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "aliyun-wan-videoedit", then confirm the skill loads.

GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add cinience/alicloud-skills --skill aliyun-wan-videoedit -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install cinience/alicloud-skills aliyun-wan-videoedit --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/cinience/alicloud-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/ai/video/aliyun-wan-videoedit .opencode/skills/aliyun-wan-videoedit && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "aliyun-wan-videoedit" agent skill from https://github.com/cinience/alicloud-skills/tree/main/skills/ai/video/aliyun-wan-videoedit into .opencode/skills/aliyun-wan-videoedit/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "aliyun-wan-videoedit", then confirm the skill loads.

OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

Facts

Skill name: aliyun-wan-videoedit
GitHub stars: 397
Token cost: ~2k tokens
SKILL.md length: 543 words
Files: 4 (incl. scripts, references)
Skills in repo: 96
Repo updated: Aug 11, 2026
First seen: Oct 7, 2026
Licence: MIT

At a glance

A skill your agent uses when editing videos with DashScope Wan 2.7 video editing model (wan2.7-videoedit).

Works in 4 steps: Confirm user intent: style transfer or… → Prepare media array with video… → Create async task and poll for results. → …
Editing videos with DashScope Wan 2.7 video editing model (wan2.7-videoedit)
SKILL.md covers Validation, Output And Evidence, Prerequisites and Critical model names, plus 10 more sections
Runs Python scripts from its folder; calls python and python3; reaches dashscope.aliyuncs.com; needs DASHSCOPE_API_KEY and API_KEY

What it does

Aliyun Wan Videoedit is an agent skill from cinience/alicloud-skills. 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.

Its SKILL.md is about 2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including scripts and reference files (for example `references/api_reference.md`, `references/sources.md` and `scripts/edit_video.py`).

It sits in Media & Creative, covering Video production and AI video generation. It works with Alibaba Cloud. The repository describes itself as: alibaba cloud skills，qwen ，wan and all skills. The licence is MIT.

When your agent uses it

Editing videos with DashScope Wan 2.7 video editing model (wan2.7-videoedit)
Implementing video style transfer
Instruction-based video editing with optional reference images
Video content modification via the video-synthesis async API

Example prompts

“/aliyun-wan-videoedit”

Requirements

Python 3
A credential in DASHSCOPE_API_KEY
A credential in API_KEY

Workflow steps

4 steps, taken from the first numbered list in SKILL.md.

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

What it can do on your machine

Read from SKILL.md and the folder at commit 1818263. It shows what the files ask for, not the result of running them.

Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Ships 1 file in scripts/ (Python), which the agent can run.
Shell commands in SKILL.md call:
- python
- python3
From the folder's file list and the shell code blocks in SKILL.md.
Network
Hosts in commands or code, which the agent is likely to contact:
- dashscope.aliyuncs.com
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names these keys or tokens, usually read from environment variables:
- DASHSCOPE_API_KEY
- API_KEY
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Aliyun Wan Videoedit loads about 2k tokens when it runs, and up to ~2.9k if it reads all its reference files. Until then it costs about 70 tokens; SKILL.md has 543 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~70
When it runs · the whole SKILL.md, loaded when a task matches: ~2k
With references · SKILL.md plus every file in references/, read only if the agent opens them: ~2.9k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from cinience/alicloud-skills at commit 1818263, republished under its MIT licence (© cinience). 543 words, ~1,966 tokens.

Download SKILL.mdSave it as .claude/skills/aliyun-wan-videoedit/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.

name: aliyun-wan-videoedit
description: 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.

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

Capability	Description	Required media
Style transfer	Convert video to a different visual style (clay, anime, etc.)	`video` only
Instruction editing	Edit video content with text instructions and optional reference images	`video` + 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

Resolution	Ratio	Output (W*H)
720P	16:9	1280*720
720P	9:16	720*1280
720P	1:1	960*960
720P	4:3	1104*832
720P	3:4	832*1104
1080P	16:9	1920*1080
1080P	9:16	1080*1920
1080P	1:1	1440*1440
1080P	4:3	1648*1248
1080P	3:4	1248*1648

Show full SKILL.md (204 more words)Show less

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

Error	Likely cause	Action
401/403	Missing or invalid `DASHSCOPE_API_KEY`	Check env var or credentials file
400 `InvalidParameter`	Bad resolution, missing video, too many reference images	Validate parameters
"does not support synchronous calls"	Missing `X-DashScope-Async: enable` header	Add required header
429	Rate limit or quota	Retry 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

Confirm user intent: style transfer or instruction-based editing.
Prepare media array with video (required) and optional reference images.
Create async task and poll for results.
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.

Files

SKILL.md and 3 other files (scripts, references) in skills/ai/video/aliyun-wan-videoedit of cinience/alicloud-skills.

SKILL.md
references/api_reference.md
references/sources.md
scripts/edit_video.py

Open the folder on GitHubat commit 1818263

Compare with similar skills

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Aliyun Wan Videoedit compared with similar skills
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Ergo Remotion Videoitwanger/toBeBetterJavaer	18k	—	~1.1k	Automated safety check: Pass	None	today

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Questions about Aliyun Wan Videoedit

What does Aliyun Wan Videoedit do?

A skill your agent uses when editing videos with DashScope Wan 2.7 video editing model (wan2.7-videoedit). Aliyun Wan Videoedit is an agent skill from cinience/alicloud-skills.7-videoedit).

When should I use Aliyun Wan Videoedit?

Aliyun Wan Videoedit fits situations like: editing videos with DashScope Wan 2.7 video editing model (wan2.7-videoedit); implementing video style transfer; instruction-based video editing with optional reference images; video content modification via the video-synthesis async API.

How do I install Aliyun Wan Videoedit in Claude Code?

Run `npx skills add cinience/alicloud-skills --skill aliyun-wan-videoedit -a claude-code`. Or copy the skill folder (skills/ai/video/aliyun-wan-videoedit in cinience/alicloud-skills) into .claude/skills/aliyun-wan-videoedit in your project. Claude Code loads it when a task matches its description.

How do I install Aliyun Wan Videoedit in Codex?

Run `npx skills add cinience/alicloud-skills --skill aliyun-wan-videoedit -a codex`. Or copy the skill folder (skills/ai/video/aliyun-wan-videoedit in cinience/alicloud-skills) into .agents/skills/aliyun-wan-videoedit in your project. Codex loads it when a task matches its description.

Can I use Aliyun Wan Videoedit in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add cinience/alicloud-skills --skill aliyun-wan-videoedit -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/aliyun-wan-videoedit, .gemini/skills/aliyun-wan-videoedit, .github/skills/aliyun-wan-videoedit and .opencode/skills/aliyun-wan-videoedit in your project.

What does Aliyun Wan Videoedit need to run?

Going by SKILL.md and its folder, Aliyun Wan Videoedit needs Python for the scripts in its folder, the command-line tools its instructions call (python and python3) and credentials named DASHSCOPE_API_KEY and API_KEY. Our summary lists: Python 3; A credential in DASHSCOPE_API_KEY; A credential in API_KEY.

Does Aliyun Wan Videoedit access the network?

SKILL.md names 1 domain. In commands or code: dashscope.aliyuncs.com; the agent is likely to contact it when it follows the instructions. This is read from the text; nothing was executed.

Is Aliyun Wan Videoedit safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Aliyun Wan Videoedit use?

Aliyun Wan Videoedit is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Aliyun Wan Videoedit use?

About 2k tokens (SKILL.md is roughly 7.9k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 959 tokens, read only when the agent opens those files.

What are the alternatives to Aliyun Wan Videoedit?

Skills that share tags, products or a category with Aliyun Wan Videoedit: HyperFrames Video Entry Point (heygen-com/hyperframes, 60k stars), Lanshu Create AI Presenter Video (cclank/lanshu-create-ai-presenter-video, 2.6k stars), Video Shots (eternityspring/reelbench-skills, 878 stars) and LTX-2.3 Video Generation (digitalsamba/claude-code-video-toolkit, 2.2k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Aliyun Wan Videoedit?

cinience (a GitHub user) maintains it in cinience/alicloud-skills, which has 397 GitHub stars. The repository holds 96 skills in this directory. The repository was last updated on August 11, 2026.

Source: cinience/alicloud-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Install Aliyun Wan Videoedit

Facts

At a glance

What it does

When your agent uses it

Example prompts

Requirements

Workflow steps

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

Wan 2.7 Video Editing

Validation

Output And Evidence

Prerequisites

Critical model names

Capabilities

API endpoint (async only)

Normalized interface

Request

Media input limits

Resolution output table

Response (task creation)

Response (task result)

Quick start (Python + HTTP)

Usage examples

Error handling

Output location

Anti-patterns

Workflow

References

Files

Compare with similar skills

Similar skills

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Related

Questions about Aliyun Wan Videoedit