Node Templates

// Synthesizes an answer from multiple document chunks using an LLM. Connect one or more Document Chunk nodes to the context port.

// Run one of gstack's plan reviews — spec (office-hours), ceo, design, eng or devex — over a plan, using the passes that review actually runs. Returns findings, the tasks they imply, what is out of scope, what already exists, and its open decisions already shaped as requestUserDecision questions. The workspace agent asks those in the chat, appends the answers to the Decisions node wired into this review, and reruns it to apply them.

// Read a GitHub repository at a branch and map it against a request or a plan: which conventions apply, what already exists for each part of it (with paths), what does not exist yet, and where it touches shared code — plus facts written by code: the branch's commits beyond its base, the pull requests open into it, and the checks the repository can run. Reads the convention files and the files a model picks; a small repository is read whole. Wire its map into every Plan review so their findings are grounded in real code.

// Review a pull request or a branch the way gstack's pre-landing /review does: read the change from GitHub, check it against what it was asked to do, and run the critical and informational passes over the diff. Returns a verdict, findings with file and line, and the question that asks which findings to fix, ready for requestUserDecision. Reads only; posts nothing to GitHub.

// Transform a text using a custom prompt via an LLM, or answer a question about an image (image Q&A) with a vision model: e.g. read the room labels on a floor plan or describe a photo. Input text and/or an image plus instructions; output the elaborated text, optionally parsed as JSON.

// Summarizes large text content using an LLM. Automatically splits long documents into chunks and summarizes them hierarchically to produce a final summary.

// Runs a RAG agent that retrieves and analyzes data from Drive. Select a folder or file as context, provide a prompt, and the agent will search the knowledge base to answer your question.