Why Your AI Video Prompt Was Rejected: The Content Filter, Explained

You wrote a prompt, hit generate, and got a rejection instead of a render: "Your content contains material that violates our Terms of Service," with a category name attached. No clip, no explanation of which word did it, and the generate button feels broken. This guide explains what actually ran when your prompt was rejected on Moosky, what it costs you (nothing), and the honest way to get a compliant version through the gate.

This is not a workaround guide. Every gate described here is public by design, because knowing what the filter enforces is what makes edit-and-retry work the first time.

First, the money question: a rejected prompt costs zero credits

The rejection fires before your run is queued and before any credits are deducted. A blocked prompt is not a partial charge, not a "well, we still tried" fee: the request never reaches a paid model, so there is nothing to refund and nothing to bill. Rejected requests are free, and you can edit and re-run as often as you want without watching a credit counter move.

Two side effects worth knowing:

• The rejection names the flagged categories (for example nudity or violence), not the specific words. That is deliberate: echoing back matched words would hand out an exact blocklist.
• Uploaded reference images are validated too, and if the request is rejected, your uploads are deleted from the request. Nothing sensitive lingers from a rejected attempt.

What actually checks a prompt

There are three gates between your text and a rendered clip, and a rejection at any of them looks similar from the outside.

Gate 1: Moosky's own pattern check. Your prompt (and negative prompt) is matched against a banned-pattern list covering four policy categories: nudity, hate, violence, and copyright. Patterns are words, phrases, or regexes, each with a risk score. A single high-risk match, or enough combined risk, blocks the request on the spot.

Gate 2: automated text and image moderation. Whatever clears the pattern list passes through a moderation model (OpenAI's moderation API) that reads meaning, not just vocabulary. The same pass also inspects uploaded reference images, so a clean prompt with a disallowed image still gets rejected. This is the gate that catches phrasings the pattern list would never contain, because it judges what the scene is, not which words you used.

Gate 3: the provider's own safety system. Video and image models run their provider-side safety checks on the final prompt (and on generated frames). A request can clear both Moosky gates and still come back rejected from the model provider. That rejection is not Moosky changing its mind mid-run: different engines enforce different tolerances, which is why the same prompt can succeed on one model and fail on another.

Because gates 2 and 3 read intent rather than vocabulary, a rejection usually means the scene you described is outside policy, not that you used a forbidden word.

Clicking generate again with the same prompt does nothing

Users burned the most patience here: the audit logs show people resubmitting an identical rejected prompt three to five times in a row, because a bare 403 feels like a transient error. It is not transient. The portal remembers a rejected submission for thirty minutes, and resending the exact same prompt and reference files returns the guidance message without even hitting the server.

The block lifts the moment the content actually changes. So the productive move after a rejection is never "try again." It is edit, then re-run: change what the request depicts, not how it is spelled. Cosmetic tricks (case changes, extra punctuation, reordering filler words) don't survive either moderation pass, and they don't change what the provider's safety system sees downstream.

What gets rejected, category by category

These are the actual policy buckets, plain language, no euphemisms.

Sexual content and nudity. The single most common rejection reason on image composers like Wan 2.7 Image. This covers explicit content and its gray zone: partially-clothed or suggestive framing, even when the prompt itself is tame, is judged from the image, so uploaded references get flagged on their own merits.

Hate content. Slurs, dehumanizing language, and content targeting protected characteristics. This category carries the highest automatic risk scores, so it blocks with the least accumulated context.

Violence. Graphic violence, gore, and real-world violence framed as entertainment. Fantasy action ("a knight duel at sunset") is fine; realistic depictions of people being hurt are not.

Copyright. Trademarked characters, brand logos, and "make me a scene from [franchise]" requests. Both Moosky's pattern list and the moderation model read these, and the provider gate enforces them independently anyway.

Self-harm and the hard lines. Content that promotes or depicts self-harm, and anything sexualizing minors, are rejected without nuance and are not negotiable in any rephrasing. A repeated pattern of rejections is logged as infractions; enough infractions in a short window (by default, five within 24 hours) auto-suspends the account, so systematic "let's see if I can get it past the filter" probing gets you banned, not generated.

The honest fix: reframe the shot, keep the story

A rejection means the described scene is out of policy. The legitimate edit is changing the scene, not hiding it, and the goal is to keep what you actually need (the story beat, the product, the mood) while dropping what trips the gate. Moosky's own image workflow does this automatically after a provider safety rejection: a recovery agent rewrites the prompt toward "fully clothed subjects, non-sexual, non-exploitative, public or performance-like framing" while preserving the creative intent. You can do the same edit by hand. Examples of edits that work because they change the depicted content:

Two drafting habits prevent most rejections before they happen. First, describe what should be in frame rather than what must not be: an elaborate "nothing revealing" negative prompt can itself read as the flag. Second, when a scene involves bodies, wardrobe and framing are prompt material, not afterthoughts. Saying "athletes in full kit, wide shot" is a sentence that never hits the gate.

On Moosky's image composers, you also get this reframing for free: after a provider safety rejection, the recovery service proposes a softened prompt before your retry runs, so the edit-and-retry loop is built into the product.

When the filter is wrong

Moderation models are statistical, so false positives happen, especially around medical, documentary, fashion-catalog, and historical-topic language that reads as something else. Three things to know:

• A "moderation unavailable" error is not a rejection. If the moderation service itself is down, Moosky deliberately fails safe (your request is held rather than waved through) but tells you it's a service outage. Nothing about your content was judged; try again shortly.
• A flagged category you can't explain is worth one neutral rewrite. Make the framing literal and clinical: setting, wardrobe, camera distance, who is present. If the same scene still fails, the gate is reading the scene itself, and the reframe table above is your next step.
• Repeated failures across models on the same idea usually mean the idea is the problem, not the wording. Keep the story beat, change the depiction.

Where this shows up most, and the right next click

The filter gate is one check among many, and the rest of the generation journey has its own expectation-setting worth reading before you spend: the Wan 3.0 workflow guide covers the video composer where rejections and credit surprises both cluster, how to write prompts that actually move the model covers the prompt craft this article assumes, and what "free" means in credit terms explains the billing model a rejection never touches. Ready to test an edit yourself? Open the Wan 2.7 Image editor or the video composer and watch the category name on the next rejection. It's the most specific feedback any of the three gates will ever give you, and the only kind worth editing against.