CI / build-check-test (push) Waiting to run

Details

feat: add 11 Venice AI skills as bundled defaults

Skills included:
- venice-chat: Chat with Venice LLM models, vision, reasoning
- venice-chat-benchmark: Benchmark chat models with infographics
- venice-image-gen: Generate images via Venice API
- venice-list-image-models: List available image models
- venice-list-text-models: List available text models
- venice-list-video-models: List available video models
- venice-tts: Text-to-speech via Venice API
- venice-video-generate: Generate videos from text/images
- venice-video-queue: Queue video generation jobs
- venice-video-quote: Get video generation cost quotes
- venice-video-retrieve: Retrieve completed videos

All rebranded from Agent Zero paths to Agent JAE (~/.jae/agent/skills/).
Requires VENICE_API_KEY environment variable.

2026-03-23 18:46:23 +01:00

3.1 KiB

Raw Blame History

Venice Video Generate

Full-lifecycle video generation using Venice.ai. Combines queue, poll, retrieve, and save into a single operation with progress logging. This is the recommended approach for video generation.

Features

Single-call generation -- queue + poll + retrieve + save in one operation
Progress logging every 20 seconds with progress bars and ETAs
Text-to-video and image-to-video support
Auto-save to disk with configurable output paths
Timeout protection -- configurable max wait (default: 15 minutes)
Handles both JSON status responses and binary video data

Prerequisites

pip install requests
export VENICE_API_KEY="your_venice_api_key"

Usage

Basic text-to-video

python scripts/generate_video.py "A cat playing piano"

With options

python scripts/generate_video.py "Ocean waves at sunset" \
  --model kling-2.6-pro-text-to-video \
  --duration 10s \
  --resolution 1080p \
  --aspect-ratio 16:9

Image-to-video

python scripts/generate_video.py "Make this image come alive" \
  --image /path/to/image.png \
  --model wan-2.5-preview-image-to-video

Custom output

python scripts/generate_video.py "Dancing robot" --output ./my_video.mp4

Options

Option	Short	Default	Description
`prompt`	--	(required)	Text description of the video
`--model`	`-m`	`wan-2.5-preview-text-to-video`	Model ID
`--duration`	`-d`	`5s`	Duration (e.g., `5s`, `10s`)
`--resolution`	`-r`	`720p`	Resolution (e.g., `720p`, `1080p`)
`--aspect-ratio`	`-a`	`16:9`	Aspect ratio (e.g., `16:9`, `9:16`, `1:1`)
`--audio`	--	off	Enable audio generation
`--no-audio`	--	--	Explicitly disable audio
`--negative-prompt`	`-n`	None	What to avoid
`--image`	`-i`	None	Input image for image-to-video
`--output`	`-o`	auto	Output file path
`--output-dir`	--	`/root/venice_videos`	Output directory
`--max-wait`	--	`900`	Max wait seconds (15 min)
`--quiet`	`-q`	off	Suppress progress output

Common Models

Text-to-Video

Model	Quality	Speed
`wan-2.5-preview-text-to-video`	Good	Fast (30-60s for 5s video)
`kling-2.6-pro-text-to-video`	Higher	Slower (60-120s)
`veo3.1-full-text-to-video`	Excellent	Slowest (90-180s)

Image-to-Video

Model	Notes
`wan-2.5-preview-image-to-video`	Fast, reliable
`veo3.1-full-image-to-video`	Premium quality

Python Import

from generate_video import generate_video

result = generate_video(
    prompt="A beautiful sunset over mountains",
    model="wan-2.5-preview-text-to-video",
    duration="5s",
    verbose=True
)

if result.success:
    print(f"Video saved: {result.video_path}")
    print(f"Took: {result.elapsed_seconds:.1f}s")
else:
    print(f"Failed: {result.error}")

Environment Variables

Variable	Required	Description
`VENICE_API_KEY`	Yes	Venice.ai API key

3.1 KiB Raw Blame History