gemini
Execute Gemini CLI for AI-powered code analysis and generation. Use when you need to leverage Google's Gemini models for complex reasoning tasks.
Best use case
gemini is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Execute Gemini CLI for AI-powered code analysis and generation. Use when you need to leverage Google's Gemini models for complex reasoning tasks.
Teams using gemini should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/gemini/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How gemini Compares
| Feature / Agent | gemini | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Execute Gemini CLI for AI-powered code analysis and generation. Use when you need to leverage Google's Gemini models for complex reasoning tasks.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Gemini CLI Integration ## Overview Execute Gemini CLI commands with support for multiple models and flexible prompt input. Integrates Google's Gemini AI models into Claude Code workflows. ## When to Use - Complex reasoning tasks requiring advanced AI capabilities - Code generation and analysis with Gemini models - Tasks requiring Google's latest AI technology - Alternative perspective on code problems ## Usage **Mandatory**: Run via uv with fixed timeout 7200000ms (foreground): ```bash uv run ~/.claude/skills/gemini/scripts/gemini.py "<prompt>" [working_dir] ``` **Optional** (direct execution or using Python): ```bash ~/.claude/skills/gemini/scripts/gemini.py "<prompt>" [working_dir] # or python3 ~/.claude/skills/gemini/scripts/gemini.py "<prompt>" [working_dir] ``` ## Environment Variables - **GEMINI_MODEL**: Configure model (default: `gemini-3-pro-preview`) - Example: `export GEMINI_MODEL=gemini-3` ## Timeout Control - **Fixed**: 7200000 milliseconds (2 hours), immutable - **Bash tool**: Always set `timeout: 7200000` for double protection ### Parameters - `prompt` (required): Task prompt or question - `working_dir` (optional): Working directory (default: current directory) ### Return Format Plain text output from Gemini: ```text Model response text here... ``` Error format (stderr): ```text ERROR: Error message ``` ### Invocation Pattern When calling via Bash tool, always include the timeout parameter: ```yaml Bash tool parameters: - command: uv run ~/.claude/skills/gemini/scripts/gemini.py "<prompt>" - timeout: 7200000 - description: <brief description of the task> ``` Alternatives: ```yaml # Direct execution (simplest) - command: ~/.claude/skills/gemini/scripts/gemini.py "<prompt>" # Using python3 - command: python3 ~/.claude/skills/gemini/scripts/gemini.py "<prompt>" ``` ### Examples **Basic query:** ```bash uv run ~/.claude/skills/gemini/scripts/gemini.py "explain quantum computing" # timeout: 7200000 ``` **Code analysis:** ```bash uv run ~/.claude/skills/gemini/scripts/gemini.py "review this code for security issues: $(cat app.py)" # timeout: 7200000 ``` **With specific working directory:** ```bash uv run ~/.claude/skills/gemini/scripts/gemini.py "analyze project structure" "/path/to/project" # timeout: 7200000 ``` **Using python3 directly (alternative):** ```bash python3 ~/.claude/skills/gemini/scripts/gemini.py "your prompt here" ``` ## Notes - **Recommended**: Use `uv run` for automatic Python environment management (requires uv installed) - **Alternative**: Direct execution `./gemini.py` (uses system Python via shebang) - Python implementation using standard library (zero dependencies) - Cross-platform compatible (Windows/macOS/Linux) - PEP 723 compliant (inline script metadata) - Requires Gemini CLI installed and authenticated - Supports all Gemini model variants (configure via `GEMINI_MODEL` environment variable) - Output is streamed directly from Gemini CLI
Related Skills
gemini-imagegen
Generate and edit images using the Gemini API (Nano Banana Pro). Use this skill when creating images from text prompts, editing existing images, applying style transfers, generating logos with text, creating stickers, product mockups, or any image generation/manipulation task. Supports text-to-image, image editing, multi-turn refinement, and composition from multiple reference images.
zapier-workflows
Manage and trigger pre-built Zapier workflows and MCP tool orchestration. Use when user mentions workflows, Zaps, automations, daily digest, research, search, lead tracking, expenses, or asks to "run" any process. Also handles Perplexity-based research and Google Sheets data tracking.
writing-skills
Create and manage Claude Code skills in HASH repository following Anthropic best practices. Use when creating new skills, modifying skill-rules.json, understanding trigger patterns, working with hooks, debugging skill activation, or implementing progressive disclosure. Covers skill structure, YAML frontmatter, trigger types (keywords, intent patterns), UserPromptSubmit hook, and the 500-line rule. Includes validation and debugging with SKILL_DEBUG. Examples include rust-error-stack, cargo-dependencies, and rust-documentation skills.
writing-plans
Use when design is complete and you need detailed implementation tasks for engineers with zero codebase context - creates comprehensive implementation plans with exact file paths, complete code examples, and verification steps assuming engineer has minimal domain knowledge
workflow-orchestration-patterns
Design durable workflows with Temporal for distributed systems. Covers workflow vs activity separation, saga patterns, state management, and determinism constraints. Use when building long-running processes, distributed transactions, or microservice orchestration.
workflow-management
Create, debug, or modify QStash workflows for data updates and social media posting in the API service. Use when adding new automated jobs, fixing workflow errors, or updating scheduling logic.
workflow-interactive-dev
用于开发 FastGPT 工作流中的交互响应。详细说明了交互节点的架构、开发流程和需要修改的文件。
woocommerce-dev-cycle
Run tests, linting, and quality checks for WooCommerce development. Use when running tests, fixing code style, or following the development workflow.
woocommerce-code-review
Review WooCommerce code changes for coding standards compliance. Use when reviewing code locally, performing automated PR reviews, or checking code quality.
Wheels Migration Generator
Generate database-agnostic Wheels migrations for creating tables, altering schemas, and managing database changes. Use when creating or modifying database schema, adding tables, columns, indexes, or foreign keys. Prevents database-specific SQL and ensures cross-database compatibility.
webapp-testing
Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.
web3-testing
Test smart contracts comprehensively using Hardhat and Foundry with unit tests, integration tests, and mainnet forking. Use when testing Solidity contracts, setting up blockchain test suites, or validating DeFi protocols.