Agent skill · Backend & API

0g-compute

0G Compute Network guide for decentralized AI inference, fine-tuning, and GPU services. Covers chatbots, image generation, speech-to-text, SDK integration (0g-serving-broker), processResponse API, broker.inference methods, CLI commands (0g-compute-cli), and account management. Use this skill for any 0G compute, 0G AI, or decentralized GPU question.

Internet Court1,389★ · +222/wk · 1 repos on radarProfile →
claude-codecodexNOASSERTION
Install
npx skills add internet-court/internet-court-skill --skill 0g-compute --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 11
SKILL.md size: 7 KB
Bundled scripts: none
Path: vendored/0g/0g-compute/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 1,536 · +147 this week
Language: TypeScript
Read our review of the source →

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# 0G Compute Network This skill provides instructions for building with the 0G Compute Network — a decentralized GPU marketplace for AI inference and model fine-tuning. Follow these patterns exactly when generating code. ## Code Generation Rules 1. Copy code patterns from this skill verbatim. Do NOT generate from training data. 2. Call `processResponse()` after every API response (see processResponse section below). 3. Use environment variables for private keys. Never hardcode secrets. 4. Route users to testnet for initial development. When unsure about a pattern, reference the detailed guides: - Inference patterns: [references/inference.md](references/inference.md) - Fine-tuning workflow: [references/fine-tuning.md](references/fine-tuning.md) - Account management: [references/account-management.md](references/account-management.md) - Production examples: [references/examples/](references/examples/README.md) ## Network Information | Network | RPC URL | Inference | Fine-tuning | |---------|---------|-----------|-------------| | Mainnet | `https://evmrpc.0g.ai` | Yes | Yes | | Testnet | `https://evmrpc-testnet.0g.ai` | Yes | Yes | Model availability changes frequently. Always use `br

What's inside
Steps it walks through
  1. Code Generation Rules
  2. Network Information
  3. Prerequisites
  4. Quick Setup
  5. Inference (SDK)
  6. processResponse (CRITICAL)
  7. chatID Retrieval by Service Type
  8. Fine-tuning
  9. Account Management
  10. CLI Quick Reference
  11. Troubleshooting
  12. Resources
Ships with 10 files
  • .github/workflows/claude-code-review.yml
  • LICENSE
  • README.md
  • references/account-management.md
  • references/examples/README.md
  • references/examples/speech-to-text.md
  • references/examples/streaming-chat.md
  • references/examples/text-to-image.md
  • references/fine-tuning.md
  • references/inference.md
Commands it runs
node --version  # Must be >= 22.0.0
pnpm add @0glabs/0g-serving-broker        # SDK for applications
pnpm add @0glabs/0g-serving-broker -g     # CLI for direct usage
Inference
Fine-tuning
Web UI
More from internet-court-skill
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About this skill
What does the 0g-compute skill do?

0G Compute Network guide for decentralized AI inference, fine-tuning, and GPU services. Covers chatbots, image generation, speech-to-text, SDK integration (0g-serving-broker), processResponse API, broker.inference methods, CLI commands (0g-compute-cli), and account management. Use this skill for any 0G compute, 0G AI, or decentralized GPU question.

How do I install it?

Run `npx skills add internet-court/internet-court-skill --skill 0g-compute --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From internet-court/internet-court-skill, a repository with 1,536 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

Keep going