Distribute AI assistant skills and commands across workspaces with Lola

Use Lola to distribute team-authored skills, commands, and agents from a central Git repository into Che workspaces. Lola supports multiple AI assistants, including Claude Code, Cursor, Copilot, Gemini CLI, and OpenCode. It translates module content into each assistant’s native file structure, so developers receive a consistent set of standards and tools without manual setup.

Lola manages skills, commands, and agents — not the full AI assistant configuration. For example, to persist Claude Code settings, MCP servers, and plugins, see Persist Claude Code configuration in Che workspaces.

How it works

A team maintains a Lola module in a Git repository containing skills (coding standards, guardrails), commands (on-demand audit macros), and agent definitions. Developers add a .lola-req file to their project referencing the module. Running lola sync fetches the module and installs it into the target assistant’s native directory structure.

Prerequisites

  • An AI assistant (Claude Code, Cursor, Copilot, Gemini CLI, or OpenCode) installed in the workspace container image.

  • Lola installed in the workspace container image or available on PATH. To install Lola:

    $ uv tool install lola-ai

  • A Git repository containing a Lola module. At minimum, the module should contain:

    module/
  skills/
    <skill-name>/
      SKILL.md
  commands/
    <command-name>.md
  AGENTS.md

  • Git credentials configured in the workspace if the module repository is private. See How Git authentication works in cloud development environments.

Procedure

  1. Create a .lola-req file at the root of your project repository with a reference to the module:

    <git_repository_url>@main#subdirectory=module&assistant=<assistant> (1)
    1 Replace <git_repository_url> with the URL of your Lola module repository. Replace <assistant> with the target assistant: claude-code, cursor, copilot, gemini-cli, or opencode. The subdirectory fragment points to the module root inside the repository.

  2. Run lola sync to fetch and install the module:

    $ lola sync

    Lola clones the module, extracts skills and commands, and writes them into the target assistant’s native directory structure in the project directory.

  3. Optional: To automate the sync at workspace startup, add a postStart command to your devfile:

    commands:
  - id: lola-sync
    exec:
      commandLine: lola sync
      workingDir: /projects/<project_name> (1)
      component: <editor_container> (2)
events:
  postStart:
    - lola-sync
    1 Replace with the name of your project directory.
    2 Replace with the name of a container component from the components section of your devfile.
Verification

  1. Start a workspace with the .lola-req file in the project root.

  2. Run lola sync (or verify it ran via postStart).

  3. Launch the AI assistant and verify that the skills and commands from the module are available.

Advantages

  • Skills and commands are maintained centrally in Git.

  • Multiple modules can be combined in a single .lola-req file.

  • Supports multiple AI assistants from a single module.

  • Lola translates modules into each assistant’s native file structure with no runtime overhead.

  • Works across clusters and Che instances.

Limitations

  • Lola must be installed in the workspace container image or installed at startup.

  • Manages skills, commands, and agents only. Does not persist assistant-specific settings, MCP server configurations, or plugin caches.

  • Changes to skills and commands are not synced back to the source repository. The module repository is the single source of truth.

Additional resources