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Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionalicloud-ai-video-wan-r2vExecute the skills CLI command in your project's root directory to begin installation:
Fetches alicloud-ai-video-wan-r2v from cinience/alicloud-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate alicloud-ai-video-wan-r2v. Access via /alicloud-ai-video-wan-r2v in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
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Category: provider
mkdir -p output/alicloud-ai-video-wan-r2v
python -m py_compile skills/ai/video/alicloud-ai-video-wan-r2v/scripts/prepare_r2v_request.py && echo "py_compile_ok" > output/alicloud-ai-video-wan-r2v/validate.txt
Pass criteria: command exits 0 and output/alicloud-ai-video-wan-r2v/validate.txt is generated.
output/alicloud-ai-video-wan-r2v/.Use Wan R2V for reference-to-video generation. This is different from i2v (single image to video).
Use one of these exact model strings:
wan2.6-r2v-flashwan2.6-r2vNewer official releases may prefer the flash variant for lower latency and lower cost.
python3 -m venv .venv
. .venv/bin/activate
python -m pip install dashscope
DASHSCOPE_API_KEY in your environment, or add dashscope_api_key to ~/.alibabacloud/credentials.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)video_url (string)task_id (string, when async)request_id (string)SUCCEEDED or terminal failure status is returned.Prepare a normalized request JSON and validate response schema:
.venv/bin/python skills/ai/video/alicloud-ai-video-wan-r2v/scripts/prepare_r2v_request.py \
--prompt "Generate a short montage with consistent character style" \
--reference-video "https://example.com/reference.mp4"
output/alicloud-ai-video-wan-r2v/videos/OUTPUT_DIR.references/sources.mdPrerequisites
Time Estimate
15-45 minutes depending on use case complexity
Steps
Common Pitfalls
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💡 Pro Tips
✓ Use when
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid when
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
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alicloud-ai-video-wan-r2v fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Useful defaults in alicloud-ai-video-wan-r2v — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
We added alicloud-ai-video-wan-r2v from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Registry listing for alicloud-ai-video-wan-r2v matched our evaluation — installs cleanly and behaves as described in the markdown.
Registry listing for alicloud-ai-video-wan-r2v matched our evaluation — installs cleanly and behaves as described in the markdown.
Keeps context tight: alicloud-ai-video-wan-r2v is the kind of skill you can hand to a new teammate without a long onboarding doc.
alicloud-ai-video-wan-r2v is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
I recommend alicloud-ai-video-wan-r2v for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Keeps context tight: alicloud-ai-video-wan-r2v is the kind of skill you can hand to a new teammate without a long onboarding doc.
I recommend alicloud-ai-video-wan-r2v for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
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