A specialized automation tool for rendering and retrieving 2D and 3D assets for Aavegotchi NFTs on the Base network.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install aavegotchi-3d-renderer
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install aavegotchi-3d-renderer using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Aavegotchi 3D Renderer?
The Aavegotchi 3D Renderer skill provides a streamlined workflow for developers and collectors to programmatically generate high-fidelity assets for Aavegotchi NFTs. By leveraging Goldsky subgraphs and the official Aavegotchi batch renderer API, this tool converts on-chain metadata into visual artifacts such as full-body PNGs, headshots, and 3D GLB models.
This utility is a critical part of the Openclaw Skills ecosystem for those building community tools, dashboards, or gaming integrations. It abstracts the complexity of deriving renderer hashes—which must account for specific collateral types, eye shapes, and wearable indices—and manages the asynchronous polling process required for asset generation on the Aavegotchi production servers.
Aavegotchi 3D Renderer Use Cases
- Generating high-resolution character headshots for community leaderboards and social sharing.
- Automating the retrieval of 3D GLB models for integration into metaverse platforms.
- Synchronizing local asset caches with on-chain metadata changes during gaming sessions.
- Creating dynamic visual previews within NFT marketplaces or inventory management tools using Openclaw Skills.
How Aavegotchi 3D Renderer Works
- Extract the unique tokenId from direct user input or an Aavegotchi inventory URL.
- Query the Goldsky Base core subgraph to retrieve current gotchi metadata, including wearables and collateral.
- Derive a deterministic renderer hash based on a specific sequence of attributes (Collateral-EyeShape-EyeColor-Body-Face-Eyes-Head-RightHand-LeftHand-Pet).
- Initiate a render request via a POST call to the Aavegotchi batch API with the forced rendering flag enabled.
- Poll the verification endpoint at regular intervals until the assets are marked as available or the operation times out.
- Download the generated PNG artifacts to a local directory and return the file paths along with GLB metadata.
Aavegotchi 3D Renderer Setup
To integrate this component into your Openclaw Skills workflow, you can execute the bundled script using Node.js. Use the following command to render a specific gotchi:
node scripts/render-gotchi-bypass.mjs --token-id 6741
You can also provide a direct inventory URL as an input source:
node scripts/render-gotchi-bypass.mjs --inventory-url "https://www.aavegotchi.com/u/0x.../inventory?itemType=aavegotchis&chainId=8453&id=6741"
Optional flags allow you to control the output directory and polling behavior:
node scripts/render-gotchi-bypass.mjs --out-dir /tmp --poll-attempts 18 --poll-interval-ms 10000
Aavegotchi 3D Renderer Data Schema & Taxonomy
The skill organizes rendering data and local file artifacts into a structured response format:
| Field | Description |
|---|---|
tokenId |
The unique identifier of the rendered Aavegotchi. |
hash |
The derived string used to represent the gotchi's visual state. |
PNG_Full |
Local filesystem path to the full-body image artifact. |
PNG_Headshot |
Local filesystem path to the headshot image artifact. |
GLB_3DModel |
The availability status and remote URL for the 3D model file. |
pollSummary |
Technical metrics including total attempts and intervals used. |
Aavegotchi 3D Renderer Advanced Features
- Deterministic hash derivation logic that accounts for complex wearable indexing and eye shape mappings.
- Automated asynchronous polling with customizable retry logic and intervals for robust asset delivery.
- Support for multiple render types including PNG_Full, PNG_Headshot, and GLB_3DModel in a single batch request.
- Direct integration with Goldsky Base core subgraphs for real-time blockchain data synchronization.
- Extensible output management for seamless integration into larger Openclaw Skills automation pipelines.
SKILL.md
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