scenario-skyboxes

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Translated

Use when a task involves generating or iterating on skyboxes, 360 panoramas, equirectangular images, environment maps, HDRI-style backdrops, or VR backdrops through the Scenario MCP. Triggers include text-to-skybox, turning a photo into a 360 environment, restyling a panorama's mood, upscaling a skybox without breaking the seam wrap, or exporting equirectangular or cubemap layouts for game engines such as Unity, Unreal, or Godot.

13installs

Sourcescenario-labs/skills

Added on2026-09-26

NPX Install

npx skill4agent add scenario-labs/skills scenario-skyboxes

SKILL.md Content

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Scenario Skyboxes and 360 Panoramas

Overview

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.

Connection 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.

Quick reference


search
model_schema_get
dry_run=true
pending_job_ids
asset_display

Step	Tool	Purpose
1	`search` (target="models", query="skybox", public=true)	Discover current skybox models
2	`model_schema_get`	Exact parameter contract for the chosen model
3	`model_run`	`dry_run=true` to price; `wait=false` to batch
4	`jobs_wait`	On timeout re-call with `pending_job_ids`
5	`asset_display` / `asset_download`	Review inline, then save the file

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.

Worked example: generate, iterate, export

Example: a stylized forest skybox for a game scene.

```
search
```
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.
Iterate on mood: copy the seed from the best result and change only style (cinematic, oil-painting, cyberpunk, and more). To keep composition while shifting look, pass the favorite as image with low strength (0.2 to 0.4). To steer mood from concept art instead, switch to
```
model_scenario-skybox-gpt
```
and pass referenceImages.
Export: run
```
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.
Verify the seam at no cost: compare the saved PNG's leftmost and rightmost pixel columns; on a seamless panorama they differ by near zero while columns a quarter-turn apart differ by an order of magnitude more.

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.

Common mistakes

Prompting a generic image model for a "360 panorama": edges will not wrap and poles smear. Use a dedicated skybox model.
Upscaling with a generic upscaler: it breaks continuity at the wrap seam. Use the skybox upscaler.
Hardcoding model IDs in scripts or docs: re-discover with
```
search
```
; the catalog changes.
Fighting seam or pole distortion through prompt wording on Skybox Flux: raise geometryEnforcement above 0 instead, and only when distortion is actually visible.
Requesting a non 2:1 width to height ratio on Skybox GPT while expecting equirectangular output: keep 2:1 (for example 2048x1024) for correct 360 viewing.
Skipping
```
model_schema_get
```
: skybox models carry model-specific fields (style, geometryEnforcement, quality) that generic assumptions miss.