Skip to content
agentscamp

Scaffold Commands

A curated collection of 8 scaffold commands for building with AI coding agents.

Command

Add Human Approval Step

Scaffold a human-in-the-loop approval gate into an agent so it pauses before a consequential action and resumes after approval.

/add-human-approval<the action/tool to gate, or the agent file>
Command

Add a Streaming LLM Endpoint

Scaffold a token-streaming LLM endpoint — server-side streaming plus the client handler — so responses render incrementally instead of after a long wait.

/add-streaming-endpoint<the route/feature to stream, or the framework>
Command

New Component

Scaffold a new UI component matching the project conventions.

/new-component<ComponentName> [props]
Command

Scaffold CLI Command

Scaffold a new subcommand for an existing CLI (or a minimal new CLI) in the project's language — flag/argument parsing, --help text, input validation, and correct exit codes — matching the framework and conventions already in use.

/scaffold-cli<the command to add — e.g. 'a `sync` subcommand that pulls remote config'>
Command

Scaffold Dockerfile

Scaffold a production-grade multi-stage Dockerfile and .dockerignore for the current project.

/scaffold-dockerfile<optional: stack/runtime hint>
Command

Scaffold GitHub Action

Scaffold a hardened GitHub Actions workflow for a stated goal, wired to the project's real test/lint/build commands.

/scaffold-github-action<what the workflow should do — e.g. CI test on PR, lint, release/publish, nightly cron>
Command

Scaffold RAG Pipeline

Scaffold a Retrieval-Augmented Generation pipeline — ingestion (load, chunk, embed, upsert) and retrieval (search, rerank, grounded prompt with citations) — fitted to the project's stack.

/scaffold-rag-pipeline<data source and use case>
Command

Scaffold a vLLM Serving Config

Scaffold a vLLM serving config for a model on a target GPU — pick precision/quantization and parallelism to fit, set batching and context length, and expose an OpenAI-compatible server.

/scaffold-vllm-config<model + target GPU(s) and VRAM, or a description of the serving workload>