UiPath CLI user guide

AI coding agents — Claude Code, Cursor, GitHub Copilot, Gemini CLI, Codex, OpenCode — can build and operate UiPath automations using uip the same way a developer does. You teach them by installing skills: task-oriented instruction bundles published by UiPath. Once a skill is installed, the agent knows when to pack a Solution, how to chain publish with deploy run, when to wait for a job, how to inspect an Orchestrator folder — the whole surface, not just command names.

This page walks through set-up per agent: install the agent's own CLI, log in to UiPath, install skills, and verify the install.

All supported agents share the same prerequisites:

You do not need to install UiPath tools (uip tools install …) before installing skills — the agent can auto-install them on first use.

See Skills for the model and uip skills reference for the full flag surface.

See claude.com/claude-code for installation. On macOS/Linux, the common path is curl -fsSL https://claude.ai/install.sh | bash.

Claude Code is global-only for skills. Passing --local errors out with a ValidationError.

Open Claude Code. Ask: "Pack this Solution and deploy it to the Shared folder." The agent should propose uip solution pack → uip solution publish → uip solution deploy run — the canonical flow.

If the agent does not recognize UiPath-specific tasks, restart Claude Code so it reloads plugins, then retry.

See cursor.com for installation.

Global (available in every workspace):

Per-project (tracked alongside your repo):

Local installs add the skills store directory to .gitignore automatically — cached skill bundles stay out of commits.

Open Cursor in the repo. In the Composer, ask: "Create a new UiPath Agent project and pack it." The agent should propose uip agent init → uip agent validate → uip agent pack.

GitHub Copilot is a VS Code / JetBrains / Visual Studio plugin. See github.com/features/copilot.

(Add --local for per-repo rules.)

Open a project in VS Code (or your supported editor) and open the Copilot Chat panel. Ask a UiPath-specific task. Copilot should cite the installed skill rules when composing its response.

See github.com/google-gemini/gemini-cli.

(Add --local for project-scoped skills.)

Run gemini in the repo. Ask for a UiPath task and check that it proposes uip commands from the installed skills.

See OpenAI's Codex installation instructions.

Start Codex. Ask for a UiPath task; the skills should guide it to the correct uip command chain.

See opencode.ai.

Launch OpenCode in the project directory. Check ~/.config/opencode/skills/ (global) or ./.opencode/skills/ (local) for the installed skill files.

Skills encode the sequence of commands to use. Ask for outcomes ("deploy this Solution to production"), not specific commands. The agent will chain pack → publish → deploy run (or ask you to confirm each step, depending on the agent's mode).

Agents run uip on your machine, using the session uip login set up. If you are logged in as a personal account, the agent acts as you. Use an External App session (Flow 2 in Authentication) if you want the agent bound to a service identity instead.

After a CLI upgrade, also run uip skills update --agent <name> to pick up any newly-published skills and uip tools update to bring tools to the matching MAJOR.MINOR line. See Managing tools and skills.

Ask the agent to run uip <command> --help and feed the output back into the conversation. The built-in help is authoritative and covers flags that skills may not yet describe. Agents typically recover on their own once given the help output.

For MCP-aware clients that are not on the supported-agents list, UiPath CLI ships an MCP server via uip mcp. It exposes one tool, run_command, that lets the client execute any uip command. This is not the primary AI path for UiPath — skills are — but it is available for integrations that already speak MCP. See uip mcp reference.