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Compute + Storage Integration

Orchestrate 0G Compute inference and Storage uploads with Merkle root recording.

1|Updated Apr 1, 2026
Search Tags:#ai-inference#wallet-ops#0g-compute#processresponse#decentralized-storage#cross-layer-pipeline#merkle-upload
One-click install
npx skills add https://github.com/Vocaid/vocaid-hub --skill compute-storage-integration
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: Compute + Storage Integration
Source: https://github.com/Vocaid/vocaid-hub/tree/main/agents/.agents/shield/skills/cross-layer/compute-plus-storage
Command: npx skills add https://github.com/Vocaid/vocaid-hub --skill compute-storage-integration

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill bridges 0G Compute inference with decentralized storage so AI outputs remain auditable, retrievable, and ready for downstream automation instead of fading into temporary responses.

Core Features & Use Cases

  • Start-to-finish pipelines: run an inference request, call processResponse, upload results to 0G Storage, and record the resulting Merkle root for later reference.
  • Hybrid retrieval flows: download stored audio, text, or images, feed them back into compute for transcription, chat analysis, or further image generation, and ensure cleanup of temp files.
  • Use Case: A creator generates an AI image, uploads it to storage, registers the root hash on-chain, and later asks the agent to analyze that stored artifact for compliance.

Quick Start

Use the compute-plus-storage skill to run an AI inference, upload the resulting payload to 0G Storage, and return the storage root hash so it can be referenced downstream.

Frequently Asked Questions about Compute + Storage Integration

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I store AI inference outputs to decentralized storage?▼

To store AI inference outputs to decentralized storage, you can run a 0G Compute inference request, call processResponse, and upload the resulting payload to 0G Storage to receive a Merkle root hash for persistent retrieval.

What environment setup is needed to run 0G Compute and Storage pipelines?▼

Running 0G Compute and Storage pipelines requires Node.js 18+, the @0glabs/0g-serving-broker, @0glabs/0g-ts-sdk, ethers 6, funded compute and storage wallets, and environment variables PRIVATE_KEY, RPC_URL, STORAGE_INDEXER, and PROVIDER_ADDRESS.

Can I analyze stored data by feeding it back into 0G Compute?▼

Yes, you can download stored audio, text, or images from 0G Storage and feed them back into 0G Compute for transcription, chat analysis, or further image generation, ensuring cleanup of temporary files afterward.

How do I register a Merkle root hash on-chain after an AI image upload?▼

After generating an AI image and uploading the payload to 0G Storage, the pipeline returns the resulting storage root hash, which can be recorded on-chain to reference the artifact for later compliance analysis.

Why use a cross-layer pipeline for AI generation and decentralized storage?▼

A cross-layer pipeline bridges 0G Compute inference with decentralized storage so AI outputs remain auditable, retrievable, and ready for downstream automation instead of fading into temporary responses.

Does the 0G Storage upload process support retrieving previously generated artifacts?▼

Yes, the process uploads results to 0G Storage and records the resulting Merkle root, allowing a creator or agent to later download and analyze the stored artifact for compliance or further processing.

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