Dcode Agent Kit

Install one skill. Type /new-dcode-agent in any project. Get a working agent.

A Claude Code skill that scaffolds a Deep Agents (LangChain) agent for you: it interviews you, then drops a ready-to-run agent into whatever project you are in. Bring your own model provider: the agents it scaffolds run on any OpenAI-compatible API, with nothing hard-coded and no vendor lock-in.

Quick Start • What You Can Build • The Skill • Providers • FAQ

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Quick Start

Install the skill once, then run /new-dcode-agent in any project. Pick either install method.

Option 1: install the skill (simplest)

git clone https://github.com/EliaAlberti/dcode-agent-kit.git
mkdir -p ~/.claude/skills
cp -r dcode-agent-kit/skills/new-dcode-agent ~/.claude/skills/

/new-dcode-agent is now available in every project.

Option 2: install as a plugin (managed updates)

In Claude Code:

/plugin marketplace add EliaAlberti/dcode-agent-kit
/plugin install dcode-agent-kit@dcode-agent-kit

Installed as a plugin, the skill is namespaced: invoke it as /dcode-agent-kit:new-dcode-agent (the manual install in Option 1 keeps the plain /new-dcode-agent).

Then, in any project, run it

/new-dcode-agent

Answer a few questions. The skill shows you a plan, then writes a ready-to-run agent folder into your current project.

You: /new-dcode-agent

Claude: What form should this agent take?
        1. SDK program   2. dcode agent   3. both
You: 1

Claude: Name it (kebab-case):
You: release-notes

Claude: In a sentence or two, what is it for?
You: Summarise merged pull requests into release notes.

Claude: Closest flavour? custom / project / work-jira / vps-ops / personal
You: project

Claude: Can it change things, or is it read-only?
You: read-only

Claude: Here is the spec [what it will create in ./release-notes/]. Scaffold it?
You: yes

Claude: Created ./release-notes/ (agent.py + model.py + README.md), wired to your
        provider, and ran a smoke test. Run it with: cd release-notes && python agent.py

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What You Get

• The /new-dcode-agent skill: install it once, run it in any project. It interviews you and writes a complete, working agent where you are.
• Provider-agnostic agents: each agent it builds works with any OpenAI-compatible API (OpenAI itself, or a compatible gateway), set in one environment variable.
• Self-contained output: each agent is a small folder (agent.py + model.py + README.md) with no shared dependencies, so you can move it, commit it, or deploy it anywhere.
• Safe by default: an agent that can change things gets an approval gate that pauses and asks before it acts.

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What Are Deep Agents and dcode?

Deep Agents is a LangChain framework for building capable AI agents in Python. You call create_deep_agent(...) with a system prompt and some tools (plain Python functions), and you get an agent that can plan, use those tools, run sub-agents, and pause for your approval before risky actions.

dcode (the deepagents-code CLI) is a terminal coding agent built on that same framework. You can add agents to it: named identities you chat with, each with its own knowledge and rules. The built-ins are coder and researcher, and you switch with /agents.