| name | gemma-local-diagnostic |
| description | Diagnose if local LLM API servers (Ollama, LM Studio, llama.cpp) and python environments are running and configured properly. |
Gemma Local Diagnostic Skill
This skill diagnoses the user's local development environment to ensure that local LLM API servers are running and accessible, and that python dependencies are correctly installed.
When to use this skill
- Use this when the user reports connection errors, API timeouts, or model loading issues.
- Use this to verify the initial environment setup of the hands-on participant before running the scripts.
Diagnostics Checklist
1. Local LLM Server Diagnostics
The agent should perform HTTP tests to check the status of local servers:
- Ollama: Query
GET http://localhost:11434/api/tags to check if running and find downloaded models (e.g., gemma4:e4b).
- LM Studio: Query
GET http://localhost:1234/v1/models to verify the local server is running.
- llama.cpp: Query
GET http://localhost:8080/health or GET http://localhost:8080/v1/models to verify the local server is running.
2. Python Environment & Dependency Check
- Inspect the root and
hands-on/ directories for virtual environments (e.g., .venv).
- Check if critical packages are installed:
- If packages are missing, suggest the installation command (e.g., using
uv pip install httpx python-dotenv or pip install httpx python-dotenv).
3. Reporting Results
Provide a structured report in the chat:
- LLM Server Status: Which server is online, and which models are loaded.
- Python Dependencies: Current status of packages.
- Actionable Steps: What the user needs to do next if anything is offline or missing.
