Build and debug AI pipelines in an IDE with RocketRide
Use RocketRide to compose, run, observe, and deploy portable AI pipelines from an IDE or CLI across model providers, vector stores, and agent nodes.
npx skills add agentskillexchange/skills --skill build-and-debug-ai-pipelines-in-an-ide-with-rocketride
Use RocketRide when an operator needs a repeatable environment for designing and running production AI pipelines instead of hand-wiring provider SDKs, vector stores, observability, and deployment scripts. The workflow is to install the IDE extension or server, create a portable pipeline, connect model, data, vector, and agent nodes, run and debug the pipeline locally, then deploy it through the RocketRide runtime or SDKs. Invoke this when the task is building and operating an AI workflow pipeline, not when you only need a hosted product page, a generic SDK, or an MCP server listing. The scope boundary is IDE-centered AI pipeline composition, debugging, and deployment.
What this skill actually does
Inputs and prerequisites: RocketRide IDE extension or RocketRide server, Docker for container deployment, optional Python or TypeScript SDKs.
Setup notes: Install the RocketRide IDE extension or run the server locally, create a `.pipe` pipeline, connect the required model, data, vector, and agent nodes, then run the pipeline from the canvas, CLI, SDK, or Docker runtime.
Source and verification boundary: use https://docs.rocketride.org/ as the canonical reference before running the workflow; keep commands, API calls, CLI usage, and generated outputs reviewable against that upstream source.
Framework fit: publish this as a Multi-Framework workflow only when the operator can invoke the documented toolchain directly, rather than treating the upstream project as a generic product listing.