Animate a still image into a short video via OpenRouter. Default model google/veo-3.1-fast.
npx skills add https://github.com/QinghongLin/data2story-skill --skill openrouter-image2video
Image + motion-prompt → video via OpenRouter. Default model: google/veo-3.1-fast.
Use this when you already have a strong still image and want to bring it to life with subtle motion (camera pan, parallax, gentle animation) while preserving the composition. For motion-from-scratch, use openrouter-text2video instead.
The script accepts either a remote image URL or a local image path; local files are base64-encoded and inlined into the request as a data URL.
Resolve TOOL_DIR = the directory containing this SKILL.md. Commands below use TOOL_DIR as a symbolic placeholder; replace it with the resolved, quoted path before running Bash.
export OPENROUTER_API_KEY=sk-or-v1-...
# From a local image you already generated with text2image
python3 TOOL_DIR/scripts/generate_video_from_image.py \
--image PROJECT_DIR/assets/teaser.png \
--prompt "slow parallax push-in, soft drift of ambient particles, no camera shake" \
--duration 5 \
--aspect-ratio 16:9 \
--download PROJECT_DIR/assets/teaser.mp4
# Or from a remote URL
python3 TOOL_DIR/scripts/generate_video_from_image.py \
--image-url "https://example.com/still.png" \
--prompt "subtle camera dolly forward, gentle depth-of-field shift" \
--download PROJECT_DIR/assets/scene.mp4
| Flag | Default | Description |
|---|---|---|
| --prompt | required | Motion prompt — describe what should move and how |
| --download | required | Output MP4 path |
| --image | one of --image / --image-url required | Local image path (PNG/JPG); will be base64-encoded |
| --image-url | one of --image / --image-url required | Remote image URL |
| --model | google/veo-3.1-fast | Any OpenRouter image-to-video-capable model |
| --duration | 5 | Seconds |
| --aspect-ratio | 16:9 | 16:9, 9:16, 1:1, 4:3, 3:4, 21:9, 9:21 |
| --resolution | 720p | Model-dependent (e.g. 480p, 720p, 1080p) |
| --frame-role | first | first or last — anchor frame role for the input image |
| --generate-audio | off | Generate audio with video (if model supports) |
| --poll-interval | 5 | Seconds between polls |
| --max-wait | 600 | Max total wait time |
POST /api/v1/videos with body: {
"model": "google/veo-3.1-fast",
"prompt": "...motion prompt...",
"aspect_ratio": "16:9",
"duration": 5,
"resolution": "720p",
"frame_images": [
{
"type": "image_url",
"frame_type": "first_frame",
"image_url": {"url": "data:image/png;base64,..." }
}
]
}
The --frame-role first|last flag maps to frame_type: "first_frame"|"last_frame".
GET /api/v1/videos/{id} every 5s until status == "completed"GET /api/v1/videos/{id}/content → raw MP4 bytesframe_images with roles first and last. The current script wires only one anchor frame; extend body["frame_images"] to add a second.Create beautiful visual art in .png and .pdf documents using design philosophy. You should use this skill when the user asks to create a poster, piece of art, design, or other static piece. Create original visual designs, never copying existing artists' work to avoid copyright violations.
Creating algorithmic art using p5.js with seeded randomness and interactive parameter exploration. Use this when users request creating art using code, generative art, algorithmic art, flow fields, or particle systems. Create original algorithmic art rather than copying existing artists' work to avoid copyright violations.
Improves the quality of images, especially screenshots, by enhancing resolution, sharpness, and clarity. Perfect for preparing images for presentations, documentation, or social media posts.
Downloads videos from YouTube and other platforms for offline viewing, editing, or archival. Handles various formats and quality options.
Lightweight WSI tile extraction and preprocessing. Use for basic slide processing tissue detection, tile extraction, stain normalization for H&E images. Best for simple pipelines, dataset preparation, quick tile-based analysis. For advanced spatial proteomics, multiplexed imaging, or deep learning pipelines use pathml.
Microscopy data management platform. Access images via Python, retrieve datasets, analyze pixels, manage ROIs/annotations, batch processing, for high-content screening and microscopy workflows.
Python library for working with DICOM (Digital Imaging and Communications in Medicine) files. Use this skill when reading, writing, or modifying medical imaging data in DICOM format, extracting pixel data from medical images (CT, MRI, X-ray, ultrasound), anonymizing DICOM files, working with DICOM metadata and tags, converting DICOM images to other formats, handling compressed DICOM data, or processing medical imaging datasets. Applies to tasks involving medical image analysis, PACS systems, radiology workflows, and healthcare imaging applications.
This skill should be used when working with pre-trained transformer models for natural language processing, computer vision, audio, or multimodal tasks. Use for text generation, classification, question answering, translation, summarization, image classification, object detection, speech recognition, and fine-tuning models on custom datasets.
Take qinghonglin/openrouter-image2video from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.