/aliyun-wan-r2v

Use when generating reference-based videos with Alibaba Cloud Model Studio Wan R2V models (wan2.6-r2v-flash, wan2.6-r2v). Use when creating multi-shot videos from reference video/image material, preserving character style, or documenting reference-to-video request/response flows.

BOOST

cinience@cinience

GitHub

From plugin

alicloud-skills

39799 skills

Install

$ npx -y skills add cinience/alicloud-skills --skill aliyun-wan-r2v --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.
You can call itInvoke it directly when you want it.
Slash command/aliyun-wan-r2v

Context preview

The summary Claude sees to decide when to auto-load this skill.

Use when generating reference-based videos with Alibaba Cloud Model Studio Wan R2V models (wan2.6-r2v-flash, wan2.6-r2v). Use when creating multi-shot videos from reference video/image material, preserving character style, or documenting reference-to-video request/response flows.

SKILL.md

aliyun-wan-r2v.SKILL.md

name: aliyun-wan-r2v
description: Use when generating reference-based videos with Alibaba Cloud Model Studio Wan R2V models (wan2.6-r2v-flash, wan2.6-r2v). Use when creating multi-shot videos from reference video/image material, preserving character style, or documenting reference-to-video request/response flows.
version: 1.0.0

Category: provider

Model Studio Wan R2V

Validation

mkdir -p output/aliyun-wan-r2v
python -m py_compile skills/ai/video/aliyun-wan-r2v/scripts/prepare_r2v_request.py && echo "py_compile_ok" > output/aliyun-wan-r2v/validate.txt

Pass criteria: command exits 0 and `output/aliyun-wan-r2v/validate.txt` is generated.

Output And Evidence

Save reference input metadata, request payloads, and task outputs in `output/aliyun-wan-r2v/`.
Keep at least one polling result snapshot.

Use Wan R2V for reference-to-video generation. This is different from i2v (single image to video).

Critical model names

Use one of these exact model strings:

`wan2.6-r2v-flash`
`wan2.6-r2v`

Newer official releases may prefer the flash variant for lower latency and lower cost.

Prerequisites

Install SDK in a virtual environment:

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`.

Normalized interface (video.generate_reference)

Request

`prompt` (string, required)
`reference_video` (string | bytes, required)
`reference_image` (string | bytes, optional)
`duration` (number, optional)
`fps` (number, optional)
`size` (string, optional)
`seed` (int, optional)

Response

`video_url` (string)
`task_id` (string, when async)
`request_id` (string)

Async handling

Prefer async submission for production traffic.
Poll task result with 15-20s intervals.
Stop polling when `SUCCEEDED` or terminal failure status is returned.

Local helper script

Prepare a normalized request JSON and validate response schema:

.venv/bin/python skills/ai/video/aliyun-wan-r2v/scripts/prepare_r2v_request.py \
  --prompt "Generate a short montage with consistent character style" \
  --reference-video "https://example.com/reference.mp4"

Output location

Default output: `output/aliyun-wan-r2v/videos/`
Override base dir with `OUTPUT_DIR`.

Workflow

1) Confirm user intent, region, identifiers, and whether the operation is read-only or mutating. 2) Run one minimal read-only query first to verify connectivity and permissions. 3) Execute the target operation with explicit parameters and bounded scope. 4) Verify results and save output/evidence files.