.md

Setup

Get an AI agent building dcupl projects in three steps: install the CLI, install the dcupl skill in Claude Code, then confirm the agent can drive both. The whole setup takes a couple of minutes.

  1. Install the CLI

    Install the CLI globally so the dcupl binary is available everywhere, then verify the resolved engine versions. 

    # install the CLI globally
npm install -g @dcupl/cli

# verify + check resolved @dcupl/* versions
dcupl version

    If it prints versions, you're ready. If you hit command not found, see CLI Installation.

  2. Install the dcupl skill

    The dcupl skill is a Claude Code plugin. Add the marketplace and install it from within Claude Code: 

    /plugin marketplace add dcupl/dcupl-plugins
/plugin install dcupl@dcupl-plugins

    Once installed, the agent has the dcupl domain knowledge it needs to use the CLI correctly — no extra prompting required. The plugin works in Claude Code and in the Claude desktop app. Every change to the plugin is an update: pull it with /plugin marketplace update dcupl-plugins.

  3. Verify the agent can drive it

    Confirm everything is wired up by asking the agent to do something concrete:

    You
    Scaffold a new dcupl project and show me the structure.

    The agent should reach for the skill, run dcupl init with a --starter (its shell can't answer the interactive starter prompt), and walk you through the generated files. If it scaffolds a project and explains the layout, your setup works — you're ready to build.

Note

Local-only by default. Scaffolding and querying need no account. To let the agent deploy or sync to the cloud, create a project in the dcupl Console and have the agent run dcupl config set with the project ID and API key (see Cloud Sync).

Tip

Every command accepts --json for machine-readable output. The skill already knows this, but it's the single most useful flag if you wire dcupl into your own agent — see Why it works.

Docs for agents

The skill carries the command patterns; for anything else, point the agent at these docs in a form it reads directly:

  • Every page as markdown. Append .md to any docs URL, for example /sdk/quick-start.md.
  • An index./llms.txt lists every page with its description and links the full-text files.
  • Full text per section. One file per section, each sized to fit one agent context: llms-getting-started.txt, llms-sdk.txt, llms-sdk-api.txt, llms-sdk-advanced.txt, llms-sdk-guides.txt, llms-cli.txt, llms-agentic.txt, llms-console.txt, llms-learn.txt and llms-playground.txt; /llms-full.txt holds all of them in one file.
  • A deployed API. A REST API (RAPI) deployed from the Console exposes an MCP endpoint where it is turned on, so an agent can query the data without the CLI: see MCP endpoint.