muapi-interior-design-visualizer

Design Community v1.0.0

Repo stars 3,374
Author repo Generative-Media-Skills

Interpretation is structured for decision-making; original keeps the upstream SKILL.md unchanged.

Decide Fit First

Core job: Visualize interior design by generating an empty room and filling it with stylish furniture and decor, or by redesigning an exis…
Best fit: Use it when the task has reusable inputs, steps, and validation criteria rather than a one-off answer.
Avoid forcing it: If the source lacks commands, platform support, or external-service evidence, keep those fields unknown instead of guessing.

Design Intent

Structure: The skill is organized around “Inputs”, “Steps”, “Phase A — Room Setup”, “Phase B — Furnishing & Styling”, showing how the author expects the agent to judge fit, collect context, and produce verifiable output.
Trigger evidence: Prioritize the author’s wording around when to use it, what context to collect, and what output shape to produce.
Evidence boundary: Author text states facts, repository files prove commands and paths, and Fluxly only adds fit, limits, and usage judgment.

How To Use It

Inputs: Provide target material, scope, expected result, forbidden changes, and validation method.
Invocation: Name muapi-interior-design-visualizer directly; if the source includes slash commands, start with the command and then add task context.
Validation: Start small and check whether the result follows “Inputs / Steps / Phase A — Room Setup” before expanding.

Boundaries And Review

Dependencies: Prepare Vendor-specific API keys before running a full task.
Permissions: Declared permissions include read / write / shell-exec; ask the agent to state file, command, and rollback boundaries before acting.
Quality bar: A useful result names the deliverable, evidence, and next action. Generic prose means the task needs tighter context.

Interior Design Visualizer

Visualize interior design by generating an empty room and filling it with stylish furniture and decor, or by redesigning an existing room.

Inputs

Name	Type	Required	Default	Description
`room_type`	text	no	modern living room	The type of room to design (e.g. living room, bedroom, office).
`design_style`	text	no	contemporary minimalist	The aesthetic style (e.g. Scandinavian, Industrial, Bohemian).
`empty_room_image`	image_url	no	—	Optional image of an empty room to be used as a base.

Steps

Phase A — Room Setup

If {{empty_room_image}} is not provided, submit the plan with ONE step to generate an empty base room:

Empty Room Generation — muapi image generate (model=gpt-image-2-text-to-image):
- Prompt: A professional wide-angle photograph of a completely empty {{room_type}} with {{design_style}} architecture. Large windows with natural light, clean wooden or tiled flooring, white or neutral-colored walls, no furniture, high ceiling. High-quality architectural photography, cinematic look.
- Aspect ratio: 4:3 or 1:1

Present the empty room to the user.

Phase B — Furnishing & Styling

Once the base room is ready, submit the plan to fill it with furniture:

Room Furnishing — muapi image edit (model=nano-banana-2-edit):
- Reference Image: The empty room image from Phase A (or the user-provided {{empty_room_image}}).
- Prompt: A stunningly designed {{room_type}} filled with high-end furniture in a {{design_style}} style. Includes a comfortable sofa, stylish coffee table, elegant rug, indoor plants, decorative wall art, and ambient lighting. The furniture placement is natural and architecturally sound. Photorealistic textures, soft lighting, cozy and inviting atmosphere, 8k resolution.
- Aspect ratio: Same as the base image.

After generation, present the final interior design visualization to the user. You can offer to generate variations with different styles (e.g., "Change style to Industrial").

Trigger Keywords

interior design, room visualizer, furniture design, home decor, redesign room

Notes for the Executing Agent

This recipe is LLM-orchestrated: read each phase, gather any missing inputs from the user, then call muapi CLI commands. Use muapi auth configure first if MUAPI_API_KEY is unset.
For model IDs without a CLI alias yet, fall back to the raw endpoint via curl -X POST https://api.muapi.ai/api/v1/<endpoint> -H "x-api-key: $MUAPI_API_KEY" -H 'content-type: application/json' -d '{...}' and poll with muapi predict wait <request_id>.
Substitute {{input_name}} placeholders with the user's actual inputs before issuing each call.

Fluxly profile Author and license come from source; runtime, permissions, and network are Fluxly detections or estimates

Fluxly category: Design
Author-declared agents: No explicit declaration found; this is not inferred or tested compatibility
Static check: 92 / 100 · heuristic scan, not runtime safety proof
Author / version / license: @SamurAIGPT · v1.0.0 · no license declared
Fluxly token estimate: Lean
Fluxly setup estimate: Guided setup
External API key: Required · Vendor-specific
Detected OS requirements: Windows
Runtime requirements: Unspecified
Detected file/system behavior: Read-only
Write / modify
Shell exec
Detected network behavior: External requests
Install commands: None (reference only)

Profile is derived at build time from SKILL.md and install vectors. Subject to drift from author intent.

Heads up: 未限定 allowed-tools，默认拥有全部工具权限。

Output preview muapi-interior-design-visualizer.preview

The current SKILL.md does not define a fixed output example.

SKILL.md SKILL.md

# Interior Design Visualizer

**Visualize interior design by generating an empty room and filling it with stylish furniture and decor, or by redesigning an existing room.**

## Inputs

| Name | Type | Required | Default | Description |
|:---|:---|:---|:---|:---|
| `room_type` | text | no | modern living room | The type of room to design (e.g. living room, bedroom, office). |
| `design_style` | text | no | contemporary minimalist | The aesthetic style (e.g. Scandinavian, Industrial, Bohemian). |
| `empty_room_image` | image_url | no | — | Optional image of an empty room to be used as a base. |


## Steps

### Phase A — Room Setup

If `{{empty_room_image}}` is not provided, submit the plan with ONE step to generate an empty base room:

1. **Empty Room Generation** — `muapi image generate` (model=`gpt-image-2-text-to-image`):
   - Prompt: `A professional wide-angle photograph of a completely empty {{room_type}} with {{design_style}} architecture. Large windows with natural light, clean wooden or tiled flooring, white or neutral-colored walls, no furniture, high ceiling. High-quality architectural photography, cinematic look.`
   - Aspect ratio: 4:3 or 1:1

Present the empty room to the user.

### Phase B — Furnishing & Styling

Once the base room is ready, submit the plan to fill it with furniture:

1. **Room Furnishing** — `muapi image edit` (model=`nano-banana-2-edit`):
   - Reference Image: The empty room image from Phase A (or the user-provided `{{empty_room_image}}`).

…

Evidence boundary and execution chain

Author text anchors workflow facts; Fluxly only indexes current sections, terms, files, and commands.

sections -> Inputs → Steps → Phase A — Room Setup → Phase B — Furnishing & Styling → Trigger Keywords → Notes for the Executing Agent
terms    -> Empty Room Generation · Room Furnishing · Visualize interior design by generating an empty room and filling it with stylish furniture and decor, or by redesigning an existing room. · Present the empty room to the user. · After generation, present the final interior design visualization to the user. · - This recipe is LLM-orchestrated: read each phase, gather any missing inputs from the user, then call muapi CLI commands.
files/cmd -> roomtype · designstyle · emptyroomimage · {{emptyroomimage}} · muapi image generate · gpt-image-2-text-to-image · muapi image edit · nano-banana-2-edit
body sha256 -> 016a714f73a6