What problem does it solve?
Migrating an existing Python agent to a deployable Agent Stack service normally requires learning the A2A protocol, extension system, and platform APIs from scratch, risking accidental business-logic rewrites and security mistakes like leaking secrets into shared environments.
Core Features & Use Cases
- Guided Wrapping Workflow: An 11-step checklist covering dependency pinning, server entrypoint creation, LLM wiring, forms, file uploads, secrets, and output handling.
- Extension Integration: Maps agent needs to platform extensions (LLM Proxy, Forms, Trajectory, Files, Secrets, OAuth, MCP, RAG) with mandatory documentation-first implementation rules.
- Safety Constraints: Enforces no business-logic changes, no secret exposure, no CLI arguments, and no local filesystem assumptions, with verification checklists and a finalization report.
- Use Case: You have a LangChain-based research agent that reads API keys from env vars and takes CLI arguments. This Skill walks you through converting it into an Agent Stack server with form-based inputs, Secrets extension credentials, and trajectory output—without touching its core logic.
Quick Start
Ask the AI to wrap your existing Python agent project as an Agent Stack service using the agentstack-wrapper skill, pointing it at your agent's source directory.