gemini-text
Generate text content using Google Gemini models via scripts/. Use for text generation, multimodal prompts with images, thinking mode for complex reasoning, JSON-formatted outputs, and Google Search grounding for real-time information. Triggers on "generate with gemini", "use gemini for text", "AI text generation", "multimodal prompt", "gemini thinking mode", "grounded response".
npx skills add majiayu000/claude-skill-registry --skill gemini-text --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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**: | Parameter | Description | Example | |-----------|-------------|---------| | `prompt` | Text prompt (required) | `"Explain quantum computing"` | | `-
- When to Use This Skill
- Available Scripts
- scripts/generate.py
- Workflows
- Workflow 1: Basic Text Generation
- Workflow 2: With System Instruction (Persona)
- Workflow 3: Complex Reasoning (Thinking Mode)
- Workflow 4: Structured JSON Output
- Workflow 5: Real-Time Information (Grounding)
- Workflow 6: Multimodal (Image Analysis)
- Workflow 7: Content Creation Pipeline (Batch + Text + TTS)
- Parameters Reference
- Model Selection
- Temperature Settings
python scripts/generate.py "Explain quantum computing in simple terms" python scripts/generate.py "How do I read a file in Python?" --system "You are a helpful coding assistant" python scripts/generate.py "Analyze the ethical implications of AI in healthcare" --thinking python scripts/generate.py "Generate a user profile object with name, email, and preferences" --json python scripts/generate.py "Who won the latest Super Bowl?" --grounding python scripts/generate.py "Describe what's in this image in detail" --image photo.png python scripts/generate.py "Create a 500-word blog post about sustainable energy" pip install google-genai export GOOGLE_API_KEY="your-key-here" or
What does the gemini-text skill do?
Generate text content using Google Gemini models via scripts/. Use for text generation, multimodal prompts with images, thinking mode for complex reasoning, JSON-formatted outputs, and Google Search grounding for real-time information. Triggers on "generate with gemini", "use gemini for text", "AI text generation", "multimodal prompt", "gemini thinking mode", "grounded response".
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill gemini-text --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.
