u/Conscious-Drawer-364

I keep seeing vibe coding framed as a way to ship another SaaS product as quickly as possible.

Wrap an API, add a dashboard, create three pricing tiers, and start charging a monthly subscription.

There is nothing wrong with making money from software. Developers need to eat, infrastructure costs money, and sustainable projects need funding. But I think we are overlooking a much bigger opportunity.

Vibe coding has made software development accessible to far more people. We could use that accessibility to build open-source tools that people can inspect, modify, learn from, self-host, and continue using without another monthly payment.

With subscriptions creeping into nearly every part of technology, I want to focus more of my own work on software that people can actually own.

I kept building one-off agents with LangChain's `deepagents`, and every time I redid the same setup: the model wiring, the project layout, the human-in-the-loop guardrails. By the third one it was just copy-paste from my own old repos and so I turned the setup into a Claude Code skill.

You run it in any project and it scaffolds a working agent for you. MIT, posting for feedback and to see if it is useful to anyone else.

Repo: https://github.com/EliaAlberti/dcode-agent-kit

The core idea

One command. You type `/new-dcode-agent` in any project, it interviews you (purpose, tools, model, whether it mutates anything), shows you a plan, then writes a self-contained agent folder right where you are. No blank file, no copy-pasting from docs.

What it writes

A standalone folder: `agent.py` + a sibling `model.py` + a README. `agent.py` does `from model import chat_model` (same-directory import, no sys.path shim). The agent is built with `create_deep_agent(model, tools, system_prompt, ...)` and tools are plain Python functions.

You pick one of three forms:

- an SDK program (a standalone Python agent you run or deploy)

- a dcode agent (an AGENTS.md identity for the deepagents-code CLI)

- or both (one you run headless and also chat with)

The deepagents gotcha it handles for you

Mutating tools are gated with `interrupt_on`, but that silently no-ops unless you also pass a `checkpointer` (`InMemorySaver`). That one cost me real time. So the skill always pairs them, and it drops in a small verification snippet that asserts the agent actually pauses before you ship. Read-only by default, anything that changes state asks first.

Any Providers

The connector is env-driven `ChatOpenAI`: `LLM_API_KEY`, optional `LLM_BASE_URL`, `LLM_MODEL`. Runs on any OpenAI-compatible endpoint including local (llama.cpp, Ollama, vLLM, LM Studio). Nothing baked into the generated code.

What's still rough

- The wizard is Claude Code specific (the agents it writes are plain Python and run anywhere).

- Installed as a plugin, the command is namespaced (`/dcode-agent-kit:new-dcode-agent`); the manual skills-folder install gives the clean `/new-dcode-agent`.

- It is v0.1.0. It scaffolds the skeleton and the safety, you still write the real tools.

Would love feedback on the approach, especially the three-forms split and whether you would structure the scaffold differently.