jae c42cd9a062 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 18:47:33 +01:00

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Venice Video Queue

Queue videos for generation on Venice.ai. Supports text-to-video, image-to-video, and video-to-video. Returns a queue_id for later retrieval with venice-video-retrieve.

For a simpler all-in-one workflow, use venice-video-generate instead.

Features

Text-to-video, image-to-video, and video-to-video support
Automatic file-to-base64 encoding for local image/video/audio inputs
CLI with full argparse support
JSON output mode for scripting
Convenience functions for common workflows

Prerequisites

pip install requests pydantic
export VENICE_API_KEY="your_venice_api_key"

Usage

Text-to-video

python scripts/queue_video.py "A cat playing piano in a jazz club" \
  --duration 5s --resolution 720p --aspect-ratio 16:9

Image-to-video

python scripts/queue_video.py "Animate this scene with gentle motion" \
  --image /path/to/image.png \
  --model wan-2.5-preview-image-to-video

JSON output (for scripting)

python scripts/queue_video.py "Cinematic sunset" --json
# Output: {"model": "wan-2.5-preview-text-to-video", "queue_id": "abc123..."}

Options

Option	Short	Default	Description
`prompt`	--	(required)	Text description
`--model`	`-m`	`wan-2.5-preview-text-to-video`	Model ID
`--duration`	`-d`	`5s`	Duration (`5s`, `10s`)
`--resolution`	`-r`	`720p`	Resolution
`--aspect-ratio`	`-a`	None	Aspect ratio (omit if model doesn't support)
`--negative-prompt`	`-n`	None	What to avoid
`--image`	`-i`	None	Input image path
`--video`	`-v`	None	Input video path
`--audio`	--	None	Audio file path
`--with-audio`	--	off	Enable audio generation
`--json`	--	off	JSON output

After Queuing

Use the returned queue_id with venice-video-retrieve:

python ../venice-video-retrieve/scripts/retrieve_video.py MODEL QUEUE_ID

Python Import

from queue_video import queue_video, queue_text_to_video, queue_image_to_video

# Text-to-video
result = queue_text_to_video(prompt="A cat playing piano", duration="5s")
print(f"Queue ID: {result.queue_id}")

# Image-to-video
result = queue_image_to_video(prompt="Animate this", image_path="/path/to/img.png")
print(f"Queue ID: {result.queue_id}")

Environment Variables

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

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