Generative-Media-Skills is a toolkit that lets AI agents generate, edit, and display images, videos, and audio through the muapi command-line interface. It is for users of Claude Code, Cursor, Gemini CLI, and OpenCode who need multimodal media-generation workflows. The catalogue entries are the skills that expose these media capabilities to coding agents.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add SamurAIGPT/Generative-Media-Skills --skill floor-plan-renderinggit clone --depth 1 https://github.com/SamurAIGPT/Generative-Media-SkillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/samuraigpt/generative-media-skills/floor-plan-rendering)<a href="https://agentmods.dev/skills/samuraigpt/generative-media-skills/floor-plan-rendering"><img src="https://agentmods.dev/badge/skills/samuraigpt/generative-media-skills/floor-plan-rendering/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/samuraigpt/generative-media-skills/floor-plan-rendering"><img src="https://agentmods.dev/badge/skills/samuraigpt/generative-media-skills/floor-plan-rendering.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Data Exfiltration · line 55 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
- medium Data Exfiltration · line 55 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00029 | $0.00683 |
| Opus 5 | $0.00015 | $0.00342 |
| Sonnet 5 | $0.00006 | $0.00137 |
| Haiku 4.5 | $0.00003 | $0.00068 |
Grade B, and why
muapi-floor-plan-rendering scanned grade B with 2 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Sends data to an external URLmediumData exfiltration
A POST to an outside endpoint may be telemetry or may be exfiltration; either way the mod talks to somewhere, and you should know where.
- 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 Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
- 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 How it starts
The opening of the file, as written. The whole thing — 57 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Floor Plan Rendering
Design a 2D floor plan and convert it into a realistic, high-quality 3D architectural rendering.
Inputs
| Name | Type | Required | Default | Description |
|---|---|---|---|---|
floor_plan_description |
text | yes | — | Description of the floor plan (e.g. "a modern 2-bedroom apartment with a balcony and open kitchen"). |
base_plan_image |
image_url | no | — | Optional 2D floor plan image to use as a starting point. |
Steps
Phase A — 2D Floor Plan Design
If {{base_plan_image}} is not provided, submit the plan with ONE step to create the 2D blueprint:
- Floor Plan Generation —
muapi image generate(model=nano-banana-2):- Prompt:
A professional, clean 2D architectural floor plan of {{floor_plan_description}}. Top-down view, technical drawing style, white background, black lines, labeled rooms (Living Room, Kitchen, Bedroom, etc.), high contrast, minimalist design. - Aspect ratio: 4:3 or 1:1
- Prompt:
Present the 2D plan to the user for approval.
Phase B — 3D Rendering
Once the 2D plan is ready, submit the plan to convert it into a realistic 3D visualization:
- 3D Conversion —
muapi image edit(model=nano-banana-2-edit):- Reference Image: The 2D plan from Phase A.
- Prompt:
A stunning, realistic 3D isometric cutaway rendering of the architectural floor plan. Photorealistic textures, warm wooden flooring, modern furniture, soft natural sunlight coming from windows, realistic shadows. High-end architectural visualization, cinematic look, 8k resolution, clean white studio background. - Aspect ratio: 4:3 or 1:1
After generation, present the final 3D rendering to the user.
Trigger Keywords
3d floor plan, architectural rendering, 2d to 3d plan, house design, interior plan
Notes for the Executing Agent
- This recipe is LLM-orchestrated: read each phase, gather any missing inputs from the user, then call
muapiCLI commands. Usemuapi auth configurefirst ifMUAPI_API_KEYis 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 withmuapi predict wait <request_id>. - Substitute
{{input_name}}placeholders with the user's actual inputs before issuing each call.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 57 lines · 29 tokens per session scan B 88ae8e711035
muapi-floor-plan-rendering is a skill published in the GitHub repository SamurAIGPT/Generative-Media-Skills (4,259 stars, last pushed yesterday), licensed MIT. It adds 29 tokens to every session and 683 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it B with 2 findings (sends data to an external url, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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