Scenario hosts dedicated skybox models that produce seamless equirectangular 360 panoramas, plus a seam-preserving skybox upscaler. Always generate with a skybox-specific model rather than a generic image model: these enforce the seam continuity and pole geometry that ordinary text-to-image output lacks.
\nConnection and the core generation loop: see the scenario skill in this repo. 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.
| Step | \nTool | \nPurpose | \n
|---|---|---|
| 1 | \nsearch (target=\"models\", query=\"skybox\", public=true) | \nDiscover current skybox models | \n
| 2 | \nmodel_schema_get | \nExact parameter contract for the chosen model | \n
| 3 | \nmodel_run | \ndry_run=true to price; wait=false to batch | \n
| 4 | \njobs_wait | \nOn timeout re-call with pending_job_ids | \n
| 5 | \nasset_display / asset_download | \nReview inline, then save the file | \n
Model IDs and the parameter facts below were live search hits at authoring time. Re-discover them each time: availability differs per team and evolves.
model_scenario-skybox-flux: text to 360 panorama, a style preset enum (count drifts; read it from the schema), automatic seam and pole correction, optional reference image with a strength slider.model_scenario-skybox-gpt: text to 360 panorama guided by up to 10 reference images, quality presets, width and height up to 3840 px.model_hunyuan-world-image-to-skybox: one photo of a place to a seamless 360 skybox.model_sc-upscale-flux-skybox: 2x to 8x skybox upscale that preserves the seamless wrap.Example: a stylized forest skybox for a game scene.
\nsearch with target=\"models\", query=\"skybox\", public=true. Pick a text-to-skybox model, for example model_scenario-skybox-flux.model_schema_get for that model. Expect fields like prompt, style, negativePrompt, image, strength, numOutputs, geometryEnforcement, seed. negativePrompt is inert unless negativePromptStrength is above 0.model_run with parameters={\"prompt\": \"ancient pine forest at dawn, mist between trunks, god rays\", \"style\": \"cinematic\", \"numOutputs\": 2}. Take style preset names from the schema response. dry_run=true first prices the run; launch with wait=false.jobs_wait with job_ids=[the returned job_id]. Its ~180s timeout is not an error: re-call with the returned pending_job_ids as job_ids, never a second model_run. Then asset_display each output.model_scenario-skybox-gpt and pass referenceImages.model_sc-upscale-flux-skybox with image=asset_id and the smallest upscaleFactor that reaches target (the upscale can cost several times the generation, and its dry_run can only run once the input asset exists, so the chain cannot be priced up front). baseModel defaults to FLUX.1-dev (stylized); a Krea-based realism option exists, and strength defaults to 0.6, which invents detail: lower it when the goal is the same panorama at higher resolution. Read the exact allowed values from model_schema_get before switching. Then asset_download the final asset.Engine format notes: Skybox Flux outputs equirectangular panoramas; keep the default sizing (1536x768 at authoring time) and read the real dimensions off the returned asset rather than assuming them. Skybox GPT's catalog lists equirectangular 2:1 plus cubemap strip 6:1 and cubemap cross 4:3 layouts, but its schema exposes only width and height, so confirm the layout contract with model_schema_get before relying on a cubemap layout. Beyond flat backdrops, the same search surfaces model_hunyuan-world-skybox-to-splat, and a separate search (query=\"world\") finds the Marble world models; both turn a finished panorama into a navigable 3D Gaussian splat scene, the pipeline the scenario-3d-worlds skill teaches end to end.
search; the catalog changes.model_schema_get: skybox models carry model-specific fields (style, geometryEnforcement, quality) that generic assumptions miss.