Augment Code via Auggie CLI
Use Augment Code's powerful context engine and agentic coding capabilities through OpenClaw. This skill wraps the auggie CLI to let users generate code, analyze projects, debug issues, and build prototypes — hands-free through Edith glasses or any OpenClaw channel.
Prerequisites
- Auggie CLI installed:
npm install -g @augmentcode/auggie - Logged in:
auggie login
When to use
Trigger this skill when the user asks to:
- Build, create, or scaffold something ("build me a landing page", "create a REST API")
- Analyze or review code ("analyze this codebase", "review this function")
- Debug or fix issues ("why is this failing", "fix the auth bug")
- Prototype or generate code ("prototype a todo app", "generate a React component")
- Get code suggestions or insights ("how should I structure this", "what's wrong with this approach")
How to use
Generate code or build something
Run auggie in print mode for one-shot tasks:
auggie --print "Build a simple Express API with /health and /users endpoints" 2>&1
Analyze the current project
auggie --print "Analyze this codebase and give me a summary of the architecture" 2>&1
Debug an issue
auggie --print "Look at the recent error in src/ws-relay.ts and suggest a fix" 2>&1
Get quiet/structured output
For clean responses without step details:
auggie --print --quiet "What dependencies does this project use and are any outdated?" 2>&1
Response formatting
When returning results to the user (especially through Edith glasses voice output):
- Summarize the key output concisely
- For code generation: describe what was created and where
- For analysis: give the top 3-5 findings
- For debugging: state the root cause and fix
- Keep it conversational and brief — this may be spoken aloud through glasses
Error handling
- If
auggieis not installed: tell the user to runnpm install -g @augmentcode/auggie - If not logged in: tell the user to run
auggie login - If the command times out: suggest breaking the task into smaller pieces
- Auggie may take 10-30 seconds for complex tasks — warn the user if it's a big request