Scenario Blocked Generations

Overview

Three gates can stop a generation, and only two of them read the prompt. Content filters run on the model provider's side, not on Scenario, so a provider block is a property of the model that was picked and the same prompt usually passes elsewhere in the catalog. IP Detection is the team's own gate: it screens the prompt and the input images before the run, an organization admin switches it on, and no model switch clears it. Plan and blocklist restrictions remove models before any prompt is judged. Treat a block as a routing problem first and a wording problem second. This skill is about false positives on content a team is entitled to make; it is not a way to produce content a provider prohibits. Core loop: see the scenario skill. If a sibling skill named here is missing from your available skills, ask the user to install it (npx skills add scenario-labs/skills --skill <name>); unattended, proceed from tool schemas and flag the gap.

Quick reference

Step	Call
Read the actual error	job_get with job_id: the row carries error and hint, plus modelId (the model to exclude) and cuCost (what the failed run charged); verbose=true adds metadata.input with the exact prompt the job ran. Or read error and hint off the jobs_wait row
Confirm the charge	job_get's cuCost first; a policy block is typically refunded and failed jobs are reimbursed except xAI generations stopped by moderation (see scenario), but the reservation is not always released at once, so confirm later with usage bounded by start_date and end_date to the job's day and include=["usages"], reading the per-type totals (the headline is project-lifetime; a team-gate screen shows as its own IP Detection line), and file the job_id with support if a charge stands
Read the team's gate	teams_list: the team row carries ipDetectionEnforcement; anything but disabled means a refusal naming intellectual-property risk is the team's own policy, screened before the run
Find alternative models	recommend with the failed job's capability (txt2img for a text-to-image block, img2img with a reference in play, img2video for a clip animated from a still) plus the user's own words; set max_cost_cu a little above the failed row's cuCost per asset to stay in the cost band, and drop the failed modelId from the ranking yourself, since recommend has no exclusion argument
Price an alternative	model_run with dry_run=true
Re-test the same intent	One model_run per candidate, prompt unchanged, so the model stays the only variable

Four failures look alike and only the first is about wording: a provider moderation block, an IP Detection block from the team's own settings, a 403 Forbidden error (the plan does not include that model), and a model a team has put on its own blocklist. Read the error before rewriting anything.

Why legitimate prompts get blocked

Three triggers stack, and each alone often sits under the threshold:

• Recognizable characters. Automated filters react to character names and signature traits they recognize. They cannot know who owns the IP, so a team's own characters are flagged like anyone else's.
• Intensity-coded language. Words chosen to convey scale or drama ("oversized", "huge", "massive") read as violence in aggregate, even when the subject is a prop.
• Real-person likeness. Filters watch for resemblance to public figures in the output as well as names in the prompt, so a character sheet the platform itself generated can be refused as a reference on the next run, and a celebrity comparison in the prompt ("built like a heavyweight champion") is a name by another route.

With two present the prompt sits near the threshold, which is why the same intent passes one run and fails the next: a small rewording tips it over. An upstream LLM step that rewrites prompts is a frequent cause, because it leans harder on intensifiers to solve an unrelated problem and re-introduces the block on every run.

Filters differ by provider, not by price

Scenario's public content policy guide ranks the providers: Google's models (Gemini, Veo) block the most brand and character references, ByteDance, OpenAI, Ideogram and Hunyuan sit in the middle, and FLUX and Recraft are the most permissive. recommend ranks by measured performance, not by filter strictness, so an alternative from the same provider inherits the same filter: take the next candidate from a different provider, and read modelId on the failed row to know which one that was: the provider is usually legible in the id, and when it is not, model_get (catalog-only, read lane) returns the record with complianceMetadata.modelProvider.

Video and audio blocks

Video changes the economics of a block. Several providers moderate the finished render, so a rejected clip has already been rendered in full, and a retry of the identical payload renders it again. Read cuCost on the failed row before anything else and confirm the charge per the quick reference, then switch provider as above, prompt unchanged.

The audio track is judged on its own. OutputAudioSensitiveContentDetected on an audio-enabled video run means the soundtrack tripped the filter, and the usual cause is a named instrument or genre, even inside an exclusion: "no music" and "a distant guitar" have both failed where "room tone, footsteps, a single voice" passed. Describe diegetic sound positively and name nothing to exclude; when the clip needs no sound, turn the audio field off where the schema exposes one (generateAudio on many families) rather than prompting silence.

IP Detection is the team's own gate

Teams on the Enterprise plan can switch on IP Detection, Scenario's own gate, independent of any provider's filter: it reads the prompt and the input images before the run against the filters an organization admin enabled (fictional characters, brands and trademarks, celebrity likeness, artist styles, and custom filters), and a match fails the job at once with a message naming intellectual-property risk, nothing generated and no generation cost charged. The screen itself bills 1 CU per screened generation plus 1 CU per input image, charged even when the result is a block and reported as its own IP Detection line in usage; video, audio and 3D inputs are neither analyzed nor charged. The setting rides the teams_list row as ipDetectionEnforcement: while it reads disabled, every IP or copyright refusal is a provider's; the active levels differ in one thing only, whether a job is let through or blocked when the check itself cannot run, so report the literal value and that distinction. When it is active, read which gate the error names: the team's gate names intellectual-property risk, a provider's names a content policy violation, and when the text says neither, run the unchanged prompt once on one alternative provider: a provider filter clears with the switch, the team's gate repeats on any model. A team-gate block earns one retry with the design described visually and no protected mark in the prompt or in any input image (the brands filter reads a logo shown in a reference like a name, while generic product descriptions are not flagged). If that repeats, it is a conversation, not a call: tell the user which gate fired; an organization admin owns the filters and can opt a single project out of screening for licensed work, and verified IP holders who keep hitting false positives on their own licensed property have an escalation path through their Scenario account manager. A copyright warning attached to a completed output is the provider's flag and informational: the asset was delivered, and reviewing it before commercial use is the team's call.

Recovery, cheapest first

1. Switch provider. Usually needs no prompt edit at all. Run the unchanged prompt against two or three alternatives from other providers at comparable cost; if they pass, the filter was that provider's, not the content's.
2. Describe scale by proportion, not intensity. "The staff reaches shoulder height" or "the blade is about as long as the forearm" carries the same art direction as "oversized" without the violence coding.
3. Soften recognizable names in the direct model input. Describe the design in the prompt and carry identity with a reference image instead, which holds the look better anyway (see scenario-consistency). Drop real-person comparisons the same way.
4. Constrain any upstream rewriter. When an LLM node writes the final prompt, put steps 2 and 3 in its instructions, or the block returns on the next run.

Then stop. If every provider refuses and one honest rewrite has not cleared it, the filter is reading something real: say so and hand it back to the user. Grinding out variants until one slips through is evasion, not art direction.

Worked example: a flagged weapon prop

The studio's own character is named Onyx, and "Onyx's oversized war hammer, huge spiked head" comes back flagged.

1. Read the error: job_get with job_id (verbose=true for the exact prompt the job ran), or the error and hint fields on the jobs_wait row. It names moderation, so this is a provider filter, not a plan restriction, a team blocklist, or IP Detection (teams_list shows ipDetectionEnforcement: "disabled").
2. recommend with the failed job's capability (txt2img here) and the user's own words, take two alternatives from providers other than the failed modelId's, price each with model_run and dry_run=true, then run the unchanged prompt on each. One passes: done, the filter belonged to the first provider.
3. Suppose all of them refuse. Rewrite once, by proportion and without the name: "a war hammer as tall as its wielder's shoulder, spiked head two hand-spans across", and carry Onyx's look with a reference image (see scenario-consistency).
4. Still refused everywhere: stop and tell the user what the filter appears to be reacting to.

Common mistakes

• Retrying the identical prompt: near-threshold prompts pass intermittently, so a retry that happens to work has fixed nothing, and on video it renders the whole clip again first.
• Rewriting wording before trying another provider: wording changes are slow and lose art direction, switching models is one call.
• Switching within the same provider: sibling members share the filter; the next candidate comes from another provider.
• Naming a genre or instrument to exclude it in an audio-enabled prompt: the exclusion is what the audio filter reads.
• Assuming IP ownership exempts a prompt: the filter is automated and sees only the text and images sent to it.
• Treating every IP-worded refusal as one gate: with enforcement on, the error wording tells them apart first and one provider switch second, and only the provider's clears that way; the team's needs its admin.
• Reporting a block as a Scenario fault: confirm other providers refuse it too, then file it with scenario-report.
• Escalating to a pricier model expecting a laxer filter: cost and moderation strictness are unrelated.
• Reading an empty model list as moderation: a team blocklist or a plan restriction removes models before any prompt is judged.