Claude-skill-registry genai-text
Generate text using Google GenAI Gemini models via CLI. Use when the user asks to generate text, get AI responses, create content, write with AI, or use Gemini for text completion.
install
source · Clone the upstream repo
git clone https://github.com/majiayu000/claude-skill-registry
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/majiayu000/claude-skill-registry "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/data/genai-text" ~/.claude/skills/majiayu000-claude-skill-registry-genai-text && rm -rf "$T"
manifest:
skills/data/genai-text/SKILL.mdsource content
GenAI Text Generation Skill
Generate text using the
genai-cli text command with Gemini models.
Quick Start
# Basic text generation uv run genai-cli text "What is the capital of France?" # With custom model uv run genai-cli text "Explain quantum computing" --model gemini-3-pro-preview # Creative writing with temperature uv run genai-cli text "Write a poem about the ocean" --temperature 0.9 # With system instruction uv run genai-cli text "Explain this code" --system "You are a senior software engineer" # Streaming output uv run genai-cli text "Tell me a long story" --stream # Save to file uv run genai-cli text "Generate documentation" --output ./docs.md
CLI Reference
uv run genai-cli text [OPTIONS] PROMPT Options: --model, -m Model: gemini-2.5-flash (default), gemini-2.5-flash-lite, gemini-3-pro-preview, gemini-2.0-flash --temperature, -t Randomness (0.0-2.0, default: 0.7) --max-tokens, -M Maximum output tokens --system, -s System instruction/persona --top-p Nucleus sampling threshold (0.0-1.0) --top-k Top-k token sampling (1-100) --stop Stop sequences (can be specified multiple times) --seed Random seed for reproducibility --stream Stream output in real-time --output, -o Save response to file --json Output as JSON
Available Models
| Model | Use Case | Notes |
|---|---|---|
| General text/multimodal | Default - balanced speed/quality |
| Low latency, high volume | Faster, cheaper |
| Complex reasoning/coding | Most capable |
| Alternative general use | Stable |
Parameters
Temperature
Controls randomness. Lower = more deterministic, higher = more creative.
# Factual, deterministic response uv run genai-cli text "What is 2+2?" --temperature 0.0 # Creative writing uv run genai-cli text "Write a creative story" --temperature 1.5
System Instruction
Set a persona or context for the model:
# Technical expert uv run genai-cli text "Review this code" --system "You are a senior security engineer" # Creative writer uv run genai-cli text "Write about sunset" --system "You are a poet who loves nature" # Specific format uv run genai-cli text "Explain REST APIs" --system "Explain like I'm 5 years old"
Token Control
Limit or control output length:
# Short response uv run genai-cli text "Summarize this topic" --max-tokens 100 # Long form content uv run genai-cli text "Write an essay" --max-tokens 2000
Sampling Parameters
Fine-tune generation with advanced sampling:
# Nucleus sampling (top-p) uv run genai-cli text "Generate ideas" --top-p 0.9 # Top-k sampling uv run genai-cli text "Complete this sentence" --top-k 40 # Combined for fine control uv run genai-cli text "Creative brainstorm" --temperature 0.8 --top-p 0.95 --top-k 50
Stop Sequences
Stop generation at specific text:
# Stop at markers uv run genai-cli text "Write a story" --stop "THE END" --stop "---"
Reproducibility
Use seed for consistent outputs:
# Same seed = same output uv run genai-cli text "Generate a name" --seed 42 uv run genai-cli text "Generate a name" --seed 42 # Same result
Output Modes
Standard Output
uv run genai-cli text "Hello" # Output: Hello! How can I assist you today?
Streaming
Real-time output as tokens are generated:
uv run genai-cli text "Tell me a story" --stream # Output appears word by word
JSON Output
Structured output for automation:
uv run genai-cli text "Hello" --json
{ "success": true, "command": "text", "data": { "response": "Hello! How can I assist you today?", "model": "gemini-2.5-flash" }, "metadata": { "temperature": null, "max_tokens": null, "stream": false, "output_file": null } }
File Output
Save directly to file:
uv run genai-cli text "Generate documentation" --output ./docs.md # With streaming uv run genai-cli text "Write a report" --stream --output ./report.txt
Use Cases
Code Generation
uv run genai-cli text "Write a Python function to calculate fibonacci" \ --model gemini-3-pro-preview \ --system "You are an expert Python developer"
Documentation
uv run genai-cli text "Document this API endpoint" \ --system "You are a technical writer" \ --output ./api-docs.md
Creative Writing
uv run genai-cli text "Write a short story about space exploration" \ --temperature 1.2 \ --max-tokens 1000 \ --stream
Data Analysis
uv run genai-cli text "Analyze this dataset and provide insights" \ --model gemini-3-pro-preview \ --system "You are a data scientist"
Translation
uv run genai-cli text "Translate to Spanish: Hello, how are you?" \ --temperature 0.3
Prerequisites
API key must be configured:
uv run genai-cli auth set-key