veo-3.2-prompter

Overview of veo-3.2-prompter skill

What veo-3.2-prompter actually does

veo-3.2-prompter is a prompt-design skill for Google Veo 3.2-style video generation workflows. Its real job is not just “write a better prompt,” but to turn messy user intent plus optional assets into a structured, executable output: a final prompt and recommended generation parameters, shaped for the Veo reference-image system and Gemini API conventions.

Who should install this skill

This skill is best for people who:

• need to create Veo prompts from mixed inputs like images, video clips, and audio direction
• want more reliable prompt construction than an ordinary freeform chat prompt
• care about cinematic prompt quality, asset handling, and reference selection
• are using or preparing for Google Veo 3.x workflows, especially Veo 3.2 / Artemis-style prompting

It is less useful if you only need a one-line creative idea with no assets or technical constraints.

The real job-to-be-done

Most users do not struggle with “having an idea.” They struggle with converting an idea into a Veo-ready instruction set that:

• uses the right reference method
• separates subject, face, style, composition, and audio intent
• avoids unsupported syntax from other video models
• outputs something close to API-ready instead of a vague paragraph

That is the core value of the veo-3.2-prompter skill.

What makes it different from a generic prompt helper

The strongest differentiator is the skill’s internal mapping logic. It uses an atomic-element approach to classify uploaded assets into roles such as:

• subject identity
• face identity
• scene environment
• aesthetic style
• composition or first-frame structure
• video extension source
• audio direction

That matters because Veo does not treat all references the same way. The skill helps decide whether an input should become a STYLE, SUBJECT, or SUBJECT_FACE reference, or whether it should be described in text instead.

Important constraints to know before adopting

This repository is strong on prompting logic, but it is not a full SDK wrapper or end-to-end automation tool. Key constraints surfaced by the references:

• Veo 3.2 syntax is tied to Gemini-style RawReferenceImage usage, not @asset_name syntax
• reference images are capped at up to 3 in the syntax guide
• audio is not directly attached as a reference image; it should be described in the prompt and paired with generate_audio=True
• the referenced Veo 3.2 model ID is marked provisional, with veo-3.1-generate-preview noted as current stable in the guide

If you need production-safe API code more than prompt design, this skill is only part of the solution.

How to Use veo-3.2-prompter skill

Install veo-3.2-prompter skill

Install from the pexoai/pexo-skills repository:

npx skills add pexoai/pexo-skills --skill veo-3.2-prompter

If your environment uses a different skill loader, use the same repo and skill slug: veo-3.2-prompter.

Read these files first

For the fastest understanding, start here:

1. skills/veo-3.2-prompter/SKILL.md
2. skills/veo-3.2-prompter/references/atomic_element_mapping.md
3. skills/veo-3.2-prompter/references/veo_syntax_guide.md

This reading order works because SKILL.md explains the workflow, while the two reference files explain the decision logic and Veo syntax constraints that actually affect output quality.

What input the skill needs from you

The veo-3.2-prompter usage pattern works best when you provide:

• the goal of the video
• the main subject
• desired visual style
• scene or environment
• shot type or camera behavior
• duration or pacing expectations
• any uploaded assets and what each asset is supposed to control
• whether audio should be generated, implied, or ignored

Even a short brief is usable, but the skill performs better when you label what each asset means.

How to turn a rough request into a strong request

Weak input:

• “Make a cool ad from these images.”

Strong input:

• “Create a 10-second premium product ad for this watch. Use watch_front.jpg to preserve the product appearance, moodboard.jpg for color palette and lighting style, and make the setting feel like a dark luxury studio. Slow push-in camera move, shallow depth of field, high contrast reflections, no human hands, polished cinematic look, generated audio with subtle mechanical ticks.”

Why this is better:

• it separates subject reference from style reference
• it gives the skill a camera and scene target
• it clarifies what should remain consistent
• it reduces the chance that the model treats every image as a generic style cue

How the skill thinks about your assets

The veo-3.2-prompter for Prompt Writing workflow is built around atomic element mapping. In practice, you should tell the skill whether each file is primarily:

• a face identity reference
• an object or character subject reference
• a style or mood reference
• a layout / first-frame reference
• a source clip to extend
• an audio inspiration source to describe in text

This is a major adoption point: the same image can imply different roles, and bad role assignment leads to weaker prompts.

How reference selection affects output quality

From the included syntax guide, Veo-style reference handling is not generic. Typical choices are:

• SUBJECT for product, object, or non-face subject fidelity
• SUBJECT_FACE for facial identity preservation
• STYLE for mood boards, art direction, palette, or look

A practical rule: do not waste a reference slot on an image unless you know what behavior you want from it. If a file only suggests atmosphere, it may be better as a style reference or even as textual description rather than a primary subject anchor.

Suggested workflow in real use

A good veo-3.2-prompter guide workflow looks like this:

1. gather the user brief and all assets
2. classify each asset by atomic role
3. choose the minimal set of references that actually controls the generation
4. state what must stay consistent versus what can vary
5. specify motion, framing, and environment in text
6. describe audio direction in text if needed
7. generate the final JSON output with prompt plus recommended parameters
8. revise after first output based on drift, style mismatch, or subject inconsistency

This is better than prompting Veo directly with a mixed paragraph because it separates control decisions before wording decisions.

What the final output should look like

The skill is designed to produce a single optimized JSON object rather than a loose prose answer. That output should typically include:

• the final prompt text
• recommended parameters
• reference decisions implied by the attached assets
• any audio-generation intent

That structure is useful if you plan to hand the result into another tool, SDK call, or internal automation layer.

Practical prompt-writing tips that matter here

When using veo-3.2-prompter, the biggest quality gains usually come from:

• naming the primary subject unambiguously
• telling the skill which asset has authority over appearance
• separating style from identity
• describing camera motion explicitly
• stating whether the clip is net-new generation or extension of an existing video
• describing sound in words instead of assuming audio files will be used directly as references

These are not generic prompt tips; they directly match the skill’s Veo-oriented mapping logic.

Misuse patterns to avoid

Avoid these common mistakes:

• uploading multiple images without saying what each one should control
• asking for both strict identity preservation and a radically conflicting style reference
• using syntax habits from other video models, especially @asset_name
• assuming audio uploads will behave like visual references
• overloading the request with too many equally important goals

If your prompt feels conflicted, the model usually reflects that conflict rather than resolving it for you.

veo-3.2-prompter skill FAQ

Is veo-3.2-prompter better than a normal chat prompt?

Usually yes, if your task involves assets or fidelity constraints. A normal chat prompt can produce a nice paragraph, but veo-3.2-prompter is more useful when you need asset-role decisions, Veo-specific reference logic, and a final output that is closer to implementation-ready.

Is this skill only for Veo 3.2?

No. The repository explicitly says it should be used for Google Veo 3.x prompting in general, but its guidance is framed around Veo 3.2 conventions and Artemis-style prompt engineering. You should still verify model IDs and current API details before production use.

Can beginners use the veo-3.2-prompter skill?

Yes, but beginners will get much better results if they provide structured inputs instead of “make it cinematic.” The skill helps with prompt construction, but it still depends on clear source intent and asset labeling.

When should I not use veo-3.2-prompter?

Skip it if:

• you do not have a Veo-oriented workflow
• you only want a quick creative concept, not a structured output
• you need fully maintained API code rather than prompt engineering logic
• your generation stack uses another model with very different reference semantics

Does it help with audio prompts?

Yes, within limits. The repo references audio direction as something that should be described in prompt text rather than uploaded as a Veo reference image. That makes it useful for soundtrack, dialogue, or sound-effect intent, but not as direct audio-conditioning infrastructure.

Does the skill include runnable code?

Not really. The strongest supporting content is reference documentation, especially around RawReferenceImage usage and reference types. Think of this as a high-value prompt design layer, not a packaged SDK integration.

How to Improve veo-3.2-prompter skill

Give better asset labels up front

The easiest way to improve veo-3.2-prompter results is to annotate assets before invocation. For example:

• portrait.jpg = preserve this exact face
• shoe.png = preserve product appearance
• moodboard.jpg = color palette and lighting only
• layout_frame.jpg = opening composition reference

That single change reduces ambiguity more than adding adjectives.

Prioritize what must stay fixed

Users often ask for too many “must-have” traits. Decide what is truly non-negotiable:

• identity
• product shape
• face fidelity
• style
• environment
• camera motion

If everything is fixed, nothing is prioritized. The skill works better when it knows the control hierarchy.

Strengthen your first request with cinematic specifics

For better veo-3.2-prompter usage, add details like:

• lens feel or framing
• camera movement
• lighting direction
• pace and shot energy
• scene texture
• whether realism or stylization matters more

“Cinematic” alone is weak. “Handheld medium close-up, golden-hour backlight, subtle lens breathing, grounded realism” gives the skill something it can operationalize.

Watch for reference-role mistakes

One of the main failure modes is assigning the wrong function to an asset. Examples:

• using a portrait as STYLE when face preservation is the goal
• using a mood board as SUBJECT and accidentally distorting identity control
• attaching too many competing references instead of selecting the strongest 1 to 3

If first outputs drift, revisit role assignment before rewriting the whole prompt.

Improve the prompt after first generation

After the first result, revise based on the actual failure:

• subject drift: strengthen subject reference and reduce conflicting style cues
• face mismatch: use SUBJECT_FACE intent more clearly
• weak atmosphere: expand style and lighting language
• composition problems: specify opening frame or layout more directly
• bad audio fit: rewrite audio direction in plain descriptive text

This is a better iteration loop than just saying “make it better.”

Validate against the reference docs

To improve the veo-3.2-prompter skill, compare your requests against:

• references/atomic_element_mapping.md
• references/veo_syntax_guide.md

Those files contain the practical logic that many users would otherwise reinvent badly: what each asset type is good for, when to use STYLE vs SUBJECT vs SUBJECT_FACE, and what Veo syntax assumptions are actually supported.

Adapt for current API reality

Because the syntax guide marks some Veo 3.2 details as provisional, improve your workflow by treating the skill as a prompt-and-structure layer while checking the latest Google model names and SDK signatures separately. That protects you from a common adoption mistake: assuming prompt logic and API stability are the same thing.