Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Generate 10 perspective/angle variations from a single image for multi-shot UGC videos. ✅ USE WHEN: - Have a hero image and need camera angle variations - Creating multi-scene UGC videos (need different shots) - Want close-ups, wide shots, side angles from one source - Building a video with scene changes ❌ DON'T USE WHEN: - Don't have a hero image yet → use morpheus-fashion-design first - Need completely different scenes/locations → use Morpheus multiple times - Just need one image → skip this step - Want to edit images manually → use nano-banana-pro INPUT: Single image (person with product) OUTPUT: 10 PNG variations with different perspectives TYPICAL PIPELINE: Morpheus → multishot-ugc → select best 4 → veed-ugc each → Remotion edit
Generate 10 perspective/angle variations from a single image for multi-shot UGC videos. ✅ USE WHEN: - Have a hero image and need camera angle variations - Creating multi-scene UGC videos (need different shots) - Want close-ups, wide shots, side angles from one source - Building a video with scene changes ❌ DON'T USE WHEN: - Don't have a hero image yet → use morpheus-fashion-design first - Need completely different scenes/locations → use Morpheus multiple times - Just need one image → skip this step - Want to edit images manually → use nano-banana-pro INPUT: Single image (person with product) OUTPUT: 10 PNG variations with different perspectives TYPICAL PIPELINE: Morpheus → multishot-ugc → select best 4 → veed-ugc each → Remotion edit
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
Generate 10 perspective variations of an image using ComfyDeploy's MULTISHOT-UGC workflow.
Multishot-UGC takes a single image and generates 10 different variations exploring different perspectives, angles, and compositions. These variations are designed to be used in VEED lip-sync workflows to create dynamic UGC-style promotional videos with varied camera shots.
Endpoint: https://api.comfydeploy.com/api/run/deployment/queue Deployment ID: 9ccbb29a-d982-48cc-a465-bae916f2c7fd
InputDescriptionDefaultinput_imageURL or path to the source imageRequiredtextDescription for exploration"Explora distintas perspectivas de esta escena"resolutionOutput resolution"2K"aspect_ratioOutput aspect ratio"9:16"
uv run ~/.clawdbot/skills/multishot-ugc/scripts/generate.py \ --image "./person-with-product.png" \ --output-dir "./multishot-output" \ [--text "Custom exploration prompt"] \ [--resolution 1K|2K|4K] \ [--aspect-ratio 9:16|16:9|1:1|4:3|3:4]
uv run ~/.clawdbot/skills/multishot-ugc/scripts/generate.py \ --image "https://example.com/image.png" \ --output-dir "./variations"
The workflow generates 10 PNG images with variations: 1_00001_.png through 10_00001_.png Each image explores a different perspective/angle of the original scene while maintaining subject identity and composition coherence.
Generate hero image with Morpheus/Ad-Ready uv run morpheus... --output hero.png Create 10 angle variations uv run multishot-ugc... --image hero.png --output-dir ./shots Select best variations for VEED lip-sync # Review shots, then generate videos for chosen ones uv run veed-ugc... --image ./shots/3_00001_.png --brief "..."
Source image should be high quality (at least 1K resolution) Works best with images containing a clear subject/person Generation takes ~2-3 minutes for 10 variations All variations maintain the original aspect ratio unless specified
Agent frameworks, memory systems, reasoning layers, and model-native orchestration.
Largest current source with strong distribution and engagement signals.