ai-video-generation
Generate AI video from any input — text, image, or script — with Pexo. Auto-routes each shot to the best of 10+ models (Seedance,…
Expert prompt engineering for Google Veo 3.2 (Artemis engine). Use when the user wants to generate a video with Veo 3.2, needs help crafting cinematic prompts, or mentions Veo, Google video generation, or Artemis engine.
Claude Code
$ npx -y skills add pexoai/pexo-skills --skill veo-3.2-prompter --agent claude-code
Codex
$ npx skills add pexoai/pexo-skills --skill veo-3.2-prompter -a codex -y
Cursor
$ npx skills add pexoai/pexo-skills --skill veo-3.2-prompter -a cursor -y
Gemini CLI
$ npx skills add pexoai/pexo-skills --skill veo-3.2-prompter -a gemini-cli -y
GitHub Copilot
$ npx skills add pexoai/pexo-skills --skill veo-3.2-prompter -a github-copilot -y
OpenCode
$ npx skills add pexoai/pexo-skills --skill veo-3.2-prompter -a opencode -y
Windsurf
$ npx skills add pexoai/pexo-skills --skill veo-3.2-prompter -a windsurf -y
name: veo-3.2-prompter description: > Expert prompt engineering for Google Veo 3.2 (Artemis engine). Use when the user wants to generate a video with Veo 3.2, needs help crafting cinematic prompts, or mentions Veo, Google video generation, or Artemis engine. version: 0.1.0 author: wells tags: [video, generation, prompt, veo, google, artemis, cinematic]
This skill transforms a user's scattered multimodal assets (images, videos, audio) and creative intent into a structured, executable prompt for the Google Veo 3.2 video generation model (Artemis engine). It acts as an expert prompt engineer, ensuring the highest quality output from the underlying model.
This skill analyzes all user inputs and generates a single, optimized JSON object containing the final prompt and recommended parameters. The internal workflow (Recognition, Mapping, Construction) is handled automatically and should not be exposed to the user.
1. **Phase 1: Recognition** — Analyze uploaded assets and user intent. Use the `atomic_element_mapping.md` to classify each asset into its atomic element role(s). 2. **Phase 2: Mapping** — For each atomic element, determine the optimal reference method (reference image, text prompt, or hybrid). Use the mapping table to decide. 3. **Phase 3: Construction** — Assemble the final prompt using the 5-Part Framework (Shot → Subject → Environment → Camera → Style) and attach reference images via the Gemini API's `RawReferenceImage` system.
**User Request:** "Make a cinematic shot of this perfume bottle rotating on a dark surface, like a luxury commercial." *User uploads `perfume.png`*
**Agent using `veo-3.2-prompter`:** *The agent internally processes the request and assets, then outputs the final JSON to the next skill in the chain.*
**Final Output (for internal use):**
{
"final_prompt": "Hero shot, a frosted glass perfume bottle with gold cap rotating slowly on a reflective dark surface, three-point studio lighting with soft key and rim light creating subtle caustics, smooth 180-degree arc, hyper-realistic luxury commercial style with shallow depth of field. Crystalline chime, soft ambient pad.",
"reference_images": [
{
"file": "perfume.png",
"reference_type": "SUBJECT"
}
],
"recommended_parameters": {
"model": "veo-3.2-generate",
"duration_seconds": 8,
"aspect_ratio": "16:9",
"resolution": "1080p",
"generate_audio": true
}
}| Feature | Capability | |---|---| | Engine | Artemis — world-model physics simulation (not pixel prediction) | | Max duration | ~30s native continuous generation | | Audio | Native dialogue + synchronized SFX | | Reference images | Up to 3 (`STYLE`, `SUBJECT`, `SUBJECT_FACE`) | | Video extension | Chain clips via previous video input | | First/last frame | Specify start and/or end keyframes | | Resolutions | 720p, 1080p, 4K (with upscaling) | | Aspect ratios | 16:9, 9:16 |
This skill relies on an internal knowledge base to make informed decisions. The agent MUST consult these files during execution.
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