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IDEでRocketRideを使ったAIパイプラインの構築とデバッグ

RocketRideを使用して、IDEまたはCLIからモデルプロバイダー、ベクトルストア、エージェントノードを横断するポータブルなAIパイプラインを構成、実行、観測、デプロイします。

IDEでRocketRideを使ったAIパイプラインの構築とデバッグとは？

このスキルは、IDEやCLIから直接、ポータブルなAIパイプラインを構築、実行、観測、デプロイできるようにするRocketRideツールチェーンを説明します。JSONベースのパイプライン定義を生成するビジュアルパイプラインビルダーを提供し、バージョン管理やチームコラボレーションを容易にします。リアルタイムの観測機能により、トークン使用量、LLM呼び出し、レイテンシを追跡して、パフォーマンスの最適化とデバッグを支援します。このツールはDockerを使用してデプロイでき、PythonおよびTypeScript SDKを提供するため、アプリケーションへのシームレスな統合が可能です。VS CodeやCursorなどの開発環境で動作し、RocketRideエンジンがバックエンドを処理します。

対応~Claude Code~Codex CLI~Cursor

npx skills add https://github.com/agentskillexchange/skills/tree/main/skills/build-and-debug-ai-pipelines-in-an-ide-with-rocketride

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スキル README より

IDEでRocketRideを使ったAIパイプラインの構築とデバッグ preview

ドキュメント

IDEでRocketRideを使ったAIパイプラインの構築とデバッグは何をしますか？

RocketRideを使用して、IDEまたはCLIからモデルプロバイダー、ベクトルストア、エージェントノードを横断するポータブルなAIパイプラインを構成、実行、観測、デプロイします。

前提条件

RocketRide IDE拡張機能またはRocketRideサーバー、コンテナデプロイ用のDocker、オプションのPythonまたはTypeScript SDK

インストール

環境に合わせたアップストリームのインストールまたはセットアップパスを使用します：

docker pull ghcr.io/rocketride-org/rocketride-engine:latest
docker create --name rocketride-engine -p 5565:5565 ghcr.io/rocketride-org/rocketride-engine:latest

アップストリームからの要件と注意事項：

Python SDK |
数行のコードで任意のPythonまたはTypeScriptアプリにパイプラインをドロップイン。インフラストラクチャのグルーコードは不要。

基本的な使用方法またはスタートガイド：

| ビジュアルパイプラインビルダー | VS Codeでノードをドラッグ、接続、設定。ボイラープレート不要。リアルタイムの観測機能でトークン使用量、LLM呼び出し、レイテンシ、実行を追跡。パイプラインはポータブルなJSONで、バージョン管理可能...
IDEの拡張機能をインストールします。拡張機能マーケットプレイスで「RocketRide」を検索：
ソース：https://github.com/rocketride-org/rocketride-server
アップストリームのドキュメントから抽出：https://raw.githubusercontent.com/rocketride-org/rocketride-server/HEAD/README.md

ドキュメント

https://docs.rocketride.org/

ソース

Agent Skill Exchange

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This repo contains 20 individual skills — each has its own dedicated page.

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ACF Extended for Advanced Custom Fields Workflows

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Use UX/UI Agent Skills when Claude should generate tokens, component specs, accessibility audits, and framework-specific UI code from a repeatable design workflow.

Add executable smoke tests for shell scripts and CLIs before ref

Use Bats-core when an agent needs to turn fragile shell scripts or command-line workflows into something it can verify repeatedly after edits. The agent writes focused Bash tests for success paths, failure paths, and output contracts, then runs them locally or in CI before a refactor, release, or incident fix goes out.

Add graph-backed memory and context retrieval to agent workflows

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Add Postgres-native vector retrieval to agent and RAG workflows

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Add runtime guardrails to TypeScript agents with VoltAgent

Use VoltAgent to intercept, validate, and enforce input/output policies in TypeScript agent workflows.

Add temporal context memory to agent workflows with Zep

Use Zep as an external context layer for agents that need to store events, assemble temporal graph context, and retrieve relevant memory before model calls.

Adobe Firefly API Batch Image Generator

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