Use RunningHub official standard-model APIs for image-to-video generation. Trigger when the user asks to use RunningHub, 可灵, Seedance, 万相, or other RunningHu...
--- name: runninghub-video description: Use RunningHub official standard-model APIs for image-to-video generation. Trigger when the user asks to use RunningHub, 可灵, Seedance, 万相, or other RunningHub video endpoints to turn one image or start/end frames into a video, especially when local files need to be uploaded first, tasks need polling via `/openapi/v2/query`, or completed videos should be downloaded automatically. --- # RunningHub Video Use this skill to submit image-to-video jobs to RunningHub, poll task status, and download the finished video locally. ## Quick Start 1. Confirm the user wants RunningHub image-to-video rather than HTML/CSS animation or local FFmpeg edits. 2. Use `scripts/runninghub_video.py` instead of hand-writing `curl` unless the user explicitly asks for raw API calls. 3. Accept either: - a public image URL - a local image path that should be uploaded through RunningHub's binary upload endpoint first 4. Wait for `/openapi/v2/query` unless the user explicitly asks for submit-only behavior. 5. Download the returned media immediately because RunningHub-hosted outputs and uploaded media links can expire. ## Default Workflow ### 1. Pick the model Use these stable shortcuts unless the user names a different endpoint: - `wan-2.2`: default choice for general image-to-video generation in this skill - `kling-v3.0-std`: strong alternative for high-quality single-image or start/end-frame generation - `seedance-2.0-global`: quality-oriented alternative with resolution and audio switches - `seedance-2.0-global-fast`: faster/cheaper Seedance variant - `wan-2.2`: Wan 2.2 image-to-video endpoint with RunningHub's numeric field names ### 2. Prepare the inputs - If the user provides a local file path, pass it directly to the script. The script uploads it to `POST /openapi/v2/media/upload/binary` and reuses the returned `download_url`. - If the user provides a public URL or a `data:` URI, pass it through unchanged. - If the user wants stronger transition control and the chosen model supports it, include an end frame. ### 3. Submit and wait Run the helper from the skill directory: ```powershell python "C:\Users\Administrator\.codex\skills\runninghub-video\scripts\runninghub_video.py" ` --image "C:\path\to\start.png" ` --prompt "镜头缓慢推进,人物抬头微笑,风吹动头发" ` --out-dir "C:\path\to\outputs" ``` For start/end frame generation: ```powershell python "C:\Users\Administrator\.codex\skills\runninghub-video\scripts\runninghub_video.py" ` --image "C:\path\to\start.png" ` --end-image "C:\path\to\end.png" ` --prompt "从平静站立过渡到转身回望,镜头平滑推进" ` --duration 5 ` --out-dir "C:\path\to\outputs" ``` ### 4. Return useful output When you finish, report: - which model/endpoint was used - the `taskId` - whether local images were uploaded first - the saved output path(s) - any prompt or parameter choices worth remembering ## Command Patterns ### Kling 3.0 std ```powershell python "C:\Users\Administrator\.codex\skills\runninghub-video\scripts\runninghub_video.py" ` --model kling-v3.0-std ` --image "C:\path\to\image.png" ` --prompt "电影感镜头,小幅推近,人物表情逐渐变化" ` --duration 5 ` --cfg-scale 0.8 ` --sound true ``` ### Seedance 2.0 global ```powershell python "C:\Users\Administrator\.codex\skills\runninghub-video\scripts\runninghub_video.py" ` --model seedance-2.0-global ` --image "C:\path\to\image.png" ` --prompt "书页翻动时,文字化作发光蝴蝶飞散" ` --resolution 720p ` --ratio adaptive ` --audio true ` --real-person-mode true ``` ### Wan 2.2 ```powershell python "C:\Users\Administrator\.codex\skills\runninghub-video\scripts\runninghub_video.py" ` --image "C:\path\to\image.png" ` --prompt "产品绕镜头缓慢旋转,补光扫过金属表面" ` --duration 5 ` --wan-resolution auto ``` ## Parameters Worth Tuning - `--prompt`: motion, camera movement, atmosphere, and audio intent - `--duration`: model-specific duration string; keep it to values shown in the official endpoint docs - `--end-image`: use when the endpoint supports start/end-frame control - `--out-dir`: always set an explicit output directory for easier follow-up work - `--submit-only`: use when the user wants a task id without waiting - `--poll-interval` and `--timeout`: useful for long renders ## Troubleshooting - If upload fails, verify the local file exists and the API key is valid. - If the task returns moderation or content-verification errors, keep the same image but soften the prompt or remove risky wording. - If the task succeeds but no download happens, inspect the raw query response and the returned `results` array. - If the user asks for a model not covered by the helper yet, read [references/api_reference.md](references/api_reference.md), then extend the script instead of crafting a one-off request. ## References - Read [references/api_reference.md](references/api_reference.md) for the exact endpoints, payload shapes, and polling behavior used by this skill.
don't have the plugin yet? install it then click "run inline in claude" again.