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3d Asset Generation

CommunityPopular
calesthio
3d-asset-generation

Generate, reconstruct, inspect, and route production 3D assets for OpenMontage worlds using Atlas Cloud, fal.ai, licensed catalogs, and Blender.

Overview

Publishercalesthio
RepositoryOpenMontage
Skill name3d-asset-generation
Stars
67K
Forks
8.5K
Bundled files
1
LicenseAGPL-3.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by calesthio on GitHub. Read the source before you install it.

Installation

Install the 3d Asset Generation AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/calesthio/OpenMontage/tree/main/.agents/skills/3d-asset-generation
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/calesthio/OpenMontage.git /tmp/OpenMontage
mkdir -p .claude/skills
cp -r /tmp/OpenMontage/.agents/skills/3d-asset-generation .claude/skills/3d-asset-generation
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable 3d Asset Generation in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads 3d Asset Generation on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so 3d Asset Generation is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

3D Asset Generation

Use this skill when a production needs real meshes rather than primitive stand-ins. It complements threejs-world-generation: that skill owns semantic world planning; this skill owns how unique and repeated meshes enter the world with provenance.

Route by asset role

NeedToolModel/pathWhy
Repeated vegetation, rocks, generic propsthreejs_asset_catalogCC0 Kenney catalogFree, coherent, instancing-friendly
Unique object described in wordsatlas_3dtripo-h3.1/text-to-3dDirect textured/PBR GLB with seeds and face limit
Object matching a concept imagefal_3dHunyuan 3D v3.1 Rapid image-to-3DBetter silhouette/style conditioning from one image
Several objects extracted from one regional compositionfal_3dSAM 3D ObjectsIndividual and combined GLBs plus placement metadata
Terrain, composition, lighting, camera, final framesblender_worldBlender 4.5 LTS / Eevee NextScene-level control; generated-asset APIs are not world renderers

Never ask a text-to-3D model to generate a whole cinematic world in one mesh. Generate hero objects, use licensed catalogs for high-volume scatter, and assemble everything in Blender from a semantic specification.

Paid-call discipline

Before every first provider call, state the exact provider, model, operation, estimated unit cost, and number of requested outputs. Generate one sample before a batch. As of 2026-08-13:

  • Atlas Tripo H3.1: $0.22 untextured; $0.33 standard textures; $0.44 HD textures; detailed geometry adds $0.22; quad mesh adds $0.055.
  • fal Hunyuan 3D v3.1 Rapid: $0.225 per generation; PBR adds $0.15.
  • fal SAM 3D Objects: $0.02 per reconstruction.

Pricing changes. Confirm the provider page before quoting or running a batch.

Asset prompt contract

Each request describes one isolated object, not a shot:

  1. Name the object and silhouette.
  2. Specify construction materials and visible wear.
  3. Specify the project's art style and color constraints.
  4. State scale and orientation.
  5. Exclude ground plane, backdrop, extra objects, labels, and lighting rigs.

For image-to-3D, use a simple background and make the object occupy more than half the frame. Request PBR only for assets close enough to benefit from it.

Mandatory mesh QA

Do not approve from the provider thumbnail alone. Import the downloaded artifact into Blender and inspect:

  • front, rear, and silhouette;
  • geometry holes and floating pieces;
  • ground contact, scale, and orientation;
  • UV seams and missing texture slots;
  • base-color, roughness, metallic, and normal response;
  • triangle count and whether the intended camera distance justifies it.

Record provider, model id, prompt, seeds, source page, cost, and output path in the asset provenance manifest. Failed samples remain failed; do not silently swap provider or spend on another model.

Assembly normalization is mandatory

Provider and catalog GLBs rarely share units, origins, or up-axis assumptions. Never compensate with arbitrary scene-level scale guesses. The Blender assembly spec declares target_height; the renderer measures the imported bounding box, normalizes to that target, and offsets the bounding-box floor to the sampled terrain height. Review the resulting real-world scale and ground contact.

Repeated scatter must declare semantic exclusion_zones around settlements, roads, rivers, landmark apertures, and hero camera sightlines. Density that occludes the subject is not production detail. Waterways and paths must use flat terrain-following ribbons; beveled 3D curves read as pipes from aerial cameras.

Landmarks whose reveal timing matters declare visibility windows in the scene spec. Camera occlusion remains preferred for natural reveals, but deterministic visibility keys are the hard guarantee for approved timing.

World fidelity budget

A reference-grade region needs three simultaneous density layers:

  • macro: authored terrain silhouettes, waterways, paths, settlements;
  • meso: hero buildings, bridges, cliffs, canopy clusters, props;
  • micro: ground cover, rocks, flowers, debris, material breakup.

The asset gate must show global, regional, and walk-height Blender stills. A wide aerial alone can hide broken contacts; a walk shot alone can hide an empty world. Primitive-only previews must be labeled blockout and cannot pass as a production-fidelity review.

For a final animation, render a small bounded frame range first and measure the per-frame time. Choose the full-render resolution from that measurement rather than intuition, render a numbered PNG sequence, and call blender_world with resume: true after interruption so it starts at the first missing frame.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the 3d Asset Generation AI skill do?

Generate, reconstruct, inspect, and route production 3D assets for OpenMontage worlds using Atlas Cloud, fal.ai, licensed catalogs, and Blender.

Why use 3d Asset Generation on TypingMind?

Because you install it once and use it with any model. 3d Asset Generation is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install 3d Asset Generation in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/calesthio/OpenMontage/tree/main/.agents/skills/3d-asset-generation. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use 3d Asset Generation?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with 3d Asset Generation?

As many as you like. As long as a model supports skills, you can use 3d Asset Generation with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the 3d Asset Generation AI skill free?

Yes. It is published on GitHub by calesthio under the AGPL-3.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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