Common Skill Utilities

This directory contains shared utilities used across multiple skills.

API Key Helper

api_key_helper.py provides standardized configuration for all Gemini-based skills, supporting both Google AI Studio and Vertex AI endpoints.

Usage in Skills

import sys
from pathlib import Path

# Add common directory to path
common_dir = Path(__file__).parent.parent.parent / 'common'
sys.path.insert(0, str(common_dir))

from api_key_helper import get_api_key_or_exit

# Get API key with automatic error handling
api_key = get_api_key_or_exit()

API Key Lookup Order

The helper checks for GEMINI_API_KEY in this order:

Process environment variable (recommended for development)
```
export GEMINI_API_KEY='your-api-key'
```

Project root .env file

echo 'GEMINI_API_KEY=your-api-key' > .env

.claude/.env file

echo 'GEMINI_API_KEY=your-api-key' > .claude/.env

.claude/skills/.env file (shared across all Gemini skills)

echo 'GEMINI_API_KEY=your-api-key' > .claude/skills/.env

Skill directory .env file

echo 'GEMINI_API_KEY=your-api-key' > .claude/skills/your-skill/.env

Vertex AI Support

To use Vertex AI instead of Google AI Studio:

# Enable Vertex AI
export GEMINI_USE_VERTEX=true
export VERTEX_PROJECT_ID=your-gcp-project-id
export VERTEX_LOCATION=us-central1  # Optional, defaults to us-central1

Or in .env file:

GEMINI_USE_VERTEX=true
VERTEX_PROJECT_ID=your-gcp-project-id
VERTEX_LOCATION=us-central1

Using get_client() Helper

For automatic client selection (AI Studio or Vertex AI):

from api_key_helper import get_client

# Get appropriate client based on configuration
client_info = get_client()

if client_info['type'] == 'vertex':
    # Using Vertex AI
    from vertexai.generative_models import GenerativeModel
    model = GenerativeModel('gemini-2.5-flash')
    response = model.generate_content("Hello")
else:
    # Using AI Studio
    client = client_info['client']
    response = client.models.generate_content(
        model='gemini-2.5-flash',
        contents="Hello"
    )

Using get_vertex_config() Helper

For checking Vertex AI configuration:

from api_key_helper import get_vertex_config

vertex_config = get_vertex_config()
if vertex_config['use_vertex']:
    print(f"Using Vertex AI")
    print(f"Project: {vertex_config['project_id']}")
    print(f"Location: {vertex_config['location']}")

Error Handling

If the API key is not found, the helper will:

Print a clear error message
Show all available methods to set the API key
Provide the URL to obtain an API key
Exit with status code 1

For Vertex AI, if VERTEX_PROJECT_ID is missing when GEMINI_USE_VERTEX=true, the helper will provide clear instructions.

This ensures users get immediate, actionable feedback when configuration is missing.

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