Gemini Text Generation

Gemini Text Generation

Generate content using Google's Gemini API through executable scripts with advanced capabilities including system instructions, thinking mode, JSON output, and Google Search grounding.

When to Use This Skill

Use this skill when you need to:

• Generate any type of text content (blogs, emails, code, stories)
• Process images with text descriptions or analysis
• Perform complex reasoning requiring step-by-step thinking
• Get structured JSON outputs for data processing
• Access real-time information via Google Search
• Apply specific personas or behavior patterns
• Combine text generation with other Gemini skills (images, TTS, embeddings)

Available Scripts

scripts/​generate.py

Purpose: Full-featured text generation with all Gemini capabilities

When to use:

• Any text generation task
• Multimodal prompts (text + image)
• Complex reasoning requiring thinking mode
• Structured JSON output requirements
• Real-time information needs (grounding)
• Custom system instructions/​personas

Key parameters:

Output: Generated text string, optionally with grounding sources

Workflows

Workflow 1: Basic Text Generation

python scripts/​generate.py "Explain quantum computing in simple terms"

• Best for: Simple content creation, explanations, summaries
• Model: gemini-3-flash-preview (default, fast)

Workflow 2: With System Instruction (Persona)

python scripts/​generate.py "How do I read a file in Python?" --system "You are a helpful coding assistant"

• Best for: Domain-specific tasks, expert personas, consistent tone
• Use when: You need specific behavioral constraints

Workflow 3: Complex Reasoning (Thinking Mode)

python scripts/​generate.py "Analyze the ethical implications of AI in healthcare" --thinking

• Best for: Complex analysis, step-by-step reasoning, multi-step problems
• Use when: Task requires careful consideration and logical progression

Workflow 4: Structured JSON Output

python scripts/​generate.py "Generate a user profile object with name, email, and preferences" --json

• Best for: Data extraction, structured data generation, API responses
• Output: Valid JSON ready for parsing
• Note: Prompt must clearly request JSON structure

Workflow 5: Real-Time Information (Grounding)

python scripts/​generate.py "Who won the latest Super Bowl?" --grounding

• Best for: Current events, news, factual information after training cutoff
• Output: Response + grounding sources with citations
• Use when: Accuracy of current information is critical

Workflow 6: Multimodal (Image Analysis)

python scripts/​generate.py "Describe what's in this image in detail" --image photo.png

• Best for: Image captioning, visual analysis, image-based Q&A
• Requires: Image file in PNG or JPEG format
• Combines well with: gemini-files for file upload

Workflow 7: Content Creation Pipeline (Batch + Text + TTS)

# 1. Create batch requests (gemini-batch skill)
# 2. Generate content
python scripts/​generate.py "Create a 500-word blog post about sustainable energy"
# 3. Convert to audio (gemini-tts skill)

• Best for: High-volume content production, podcasts, audiobooks

Parameters Reference

Model Selection

Temperature Settings

Thinking Budget

Output Interpretation

Standard Text Output

• Plain text response ready for use
• Check for truncation if max-tokens was set
• May include markdown formatting

JSON Output

• Valid JSON object (use --json flag)
• Parse with: import json; data = json.loads(output)
• Verify structure matches your requirements
• Handle potential parsing errors

Grounded Response

When --grounding is used, the script prints:

1. Main response text
2. "--- Grounding Sources ---" section
3. List of sources with titles and URLs

Thinking Mode Output

• May include reasoning steps before final answer
• Longer response times due to thinking process
• Better for tasks requiring careful analysis

Common Issues

"google-genai not installed"

pip install google-genai

"API key not set"

Set environment variable:

export GOOGLE_API_KEY="your-key-here"
# or
export GEMINI_API_KEY="your-key-here"

"Model not available"

• Check model name spelling
• Verify API access for selected model
• Try gemini-3-flash-preview (most available)

JSON parse errors

• Ensure prompt explicitly requests JSON structure
• Check output for JSON formatting
• Consider using system instruction: "You always respond with valid JSON"

Image file not found

• Verify image path is correct
• Use absolute paths if relative paths fail
• Supported formats: PNG, JPEG

Response truncated

• Increase --max-tokens value
• Break task into smaller requests
• Use pro models with higher token limits

Best Practices

Performance Optimization

• Use flash models for speed, pro for quality
• Lower temperature (0.0-0.3) for deterministic outputs
• Set appropriate max-tokens to control costs
• Use thinking mode only for complex tasks

Prompt Engineering

• Be specific and clear in your prompts
• Use system instructions for consistent behavior
• Include examples in prompts for better results
• For JSON: specify exact structure in prompt

Error Handling

• Wrap script calls in try-except blocks
• Validate JSON output before parsing
• Handle network timeouts with retries
• Check API quota limits for batch operations

Cost Management

• Use flash models when possible (lower cost)
• Limit max-tokens for simple queries
• Cache results for repeated queries
• Use batch API for high-volume tasks

Related Skills

• gemini-image: Generate images from text
• gemini-tts: Convert text to speech
• gemini-embeddings: Create vector embeddings for semantic search
• gemini-files: Upload files for multimodal processing
• gemini-batch: Process multiple requests efficiently

Quick Reference

# Basic
python scripts/​generate.py "Your prompt"

# Persona
python scripts/​generate.py "Prompt" --system "You are X"

# Thinking
python scripts/​generate.py "Complex task" --thinking

# JSON
python scripts/​generate.py "Generate JSON" --json

# Search
python scripts/​generate.py "Current event" --grounding

# Multimodal
python scripts/​generate.py "Describe this" --image photo.png

Reference

• See references/​models.md for detailed model information
• Get API key: https://aistudio.google.com/apikey
• Documentation: https://ai.google.dev/gemini-api