Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add pexoai/pexo-skills --skill veo-3.2-promptergit clone --depth 1 https://github.com/pexoai/pexo-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/pexoai/pexo-skills/veo-3.2-prompter)<a href="https://agentmods.dev/skills/pexoai/pexo-skills/veo-3.2-prompter"><img src="https://agentmods.dev/badge/skills/pexoai/pexo-skills/veo-3.2-prompter/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/pexoai/pexo-skills/veo-3.2-prompter"><img src="https://agentmods.dev/badge/skills/pexoai/pexo-skills/veo-3.2-prompter.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00063 | $0.00919 |
| Opus 5 | $0.00032 | $0.00460 |
| Sonnet 5 | $0.00013 | $0.00184 |
| Haiku 4.5 | $0.00006 | $0.00092 |
Grade A, and why
veo-3.2-prompter scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 77 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Veo 3.2 Prompt Designer Skill
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.
When to Use
- When the user provides assets (images, videos, audio) for video generation with Veo 3.2.
- When the user's request is complex and requires careful prompt construction for the Veo model.
- When using any Google Veo 3.x model for video generation.
Core Function
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.
Internal Workflow
- Phase 1: Recognition — Analyze uploaded assets and user intent. Use the
atomic_element_mapping.mdto classify each asset into its atomic element role(s). - Phase 2: Mapping — For each atomic element, determine the optimal reference method (reference image, text prompt, or hybrid). Use the mapping table to decide.
- 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
RawReferenceImagesystem.
Usage Example
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
}
}
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 77 lines · 63 tokens per session scan A 500c5ddd8565
veo-3.2-prompter is a skill published in the GitHub repository pexoai/pexo-skills (777 stars, last pushed 22d ago), licensed MIT. It adds 63 tokens to every session and 919 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
seedance-prompter
Expert prompt engineering for Seedance 2.0. Use when the user wants to generate a video with multimodal assets (images, videos, audio) and needs the best possible prompt.
multimodal-llm
Vision, audio, video generation, and multimodal LLM integration patterns. Use when processing images, transcribing audio, generating speech, generating AI video (Kling v3, Sora 2, Veo 3.1 std/lite/fast, Runway Gen-4.5 via gen4turbo), or building multimodal AI pipelines.
wegent-video-generation
Use this skill when the user asks to generate or create a video. Supports text, image, video, and audio reference materials according to the selected video model.
minimax-video-gen
A guide for generating videos with MiniMax's Hailuo model from text, images, or subject references. Video generation runs as a background task that must be checked until finished.
image-generation
Generate images with Gemini (default) or fal.ai FLUX.2 klein 4B (--cheap for fast/low-cost). Generate videos with Grok Imagine (default) or fal.ai LTX-2 (--cheap). Use for: create image, generate visual, AI image generation, poster, video generation.
minimax-video
A video-generation tool for creating short MP4 videos from text, images, video frames, or a subject reference. It uses the MiniMax Hailuo model and saves generated videos locally.