Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Swap faces between images using EachLabs AI. Use when the user wants to replace or swap faces in photos.
Swap faces between images using EachLabs AI. Use when the user wants to replace or swap faces in photos.
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.
Swap faces between images and videos using the EachLabs Predictions API.
Header: X-API-Key: <your-api-key> Set the EACHLABS_API_KEY environment variable. Get your key at eachlabs.ai.
ModelSlugBest ForAI Face Swap V1aifaceswap-face-swapImage face swapEachlabs Face Swapeach-faceswap-v1Image face swapFace Swap (legacy)face-swap-newImage face swapFaceswap Videofaceswap-videoVideo face swap
curl -X POST https://api.eachlabs.ai/v1/prediction \ -H "Content-Type: application/json" \ -H "X-API-Key: $EACHLABS_API_KEY" \ -d '{ "model": "aifaceswap-face-swap", "version": "0.0.1", "input": { "target_image": "https://example.com/target-photo.jpg", "swap_image": "https://example.com/source-face.jpg" } }'
curl -X POST https://api.eachlabs.ai/v1/prediction \ -H "Content-Type: application/json" \ -H "X-API-Key: $EACHLABS_API_KEY" \ -d '{ "model": "each-faceswap-v1", "version": "0.0.1", "input": { "target_image": "https://example.com/target-photo.jpg", "swap_image": "https://example.com/source-face.jpg" } }'
curl -X POST https://api.eachlabs.ai/v1/prediction \ -H "Content-Type: application/json" \ -H "X-API-Key: $EACHLABS_API_KEY" \ -d '{ "model": "faceswap-video", "version": "0.0.1", "input": { "target_video": "https://example.com/target-video.mp4", "swap_image": "https://example.com/source-face.jpg" } }'
For prompt-based face replacement: curl -X POST https://api.eachlabs.ai/v1/prediction \ -H "Content-Type: application/json" \ -H "X-API-Key: $EACHLABS_API_KEY" \ -d '{ "model": "gpt-image-v1-5-edit", "version": "0.0.1", "input": { "prompt": "Replace the face in image 1 with the face from image 2. Keep the same pose, lighting, and expression. Maintain natural skin tone and seamless blending.", "image_urls": [ "https://example.com/target-photo.jpg", "https://example.com/source-face.jpg" ], "quality": "high" } }'
Check model GET https://api.eachlabs.ai/v1/model?slug=<slug> β validates the model exists and returns the request_schema with exact input parameters. Always do this before creating a prediction to ensure correct inputs. POST https://api.eachlabs.ai/v1/prediction with model slug, version "0.0.1", and input matching the schema Poll GET https://api.eachlabs.ai/v1/prediction/{id} until status is "success" or "failed" Extract the output image URL from the response
Use high-quality source images with clear, well-lit faces The source face image should be a clear frontal or near-frontal portrait Matching lighting conditions between source and target produces more natural results Specify "seamless blending" and "natural skin tone" in prompts For the target image, faces should be clearly visible and not heavily occluded
Agent frameworks, memory systems, reasoning layers, and model-native orchestration.
Largest current source with strong distribution and engagement signals.