Execute Gemini CLI for AI-powered code analysis and generation. Use when you need to leverage Google's Gemini models for complex reasoning tasks.
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安装前可以查看交给 AI 代理的指令内容。
Execute Gemini CLI commands with support for multiple models and flexible prompt input. Integrates Google's Gemini AI models into Claude Code workflows.
Mandatory: Run via uv with fixed timeout 7200000ms (foreground):
uv run ~/.claude/skills/gemini/scripts/gemini.py "<prompt>" [working_dir]
Optional (direct execution or using Python):
~/.claude/skills/gemini/scripts/gemini.py "<prompt>" [working_dir]
# or
python3 ~/.claude/skills/gemini/scripts/gemini.py "<prompt>" [working_dir]
gemini-3-pro-preview)
export GEMINI_MODEL=gemini-3timeout: 7200000 for double protectionprompt (required): Task prompt or questionworking_dir (optional): Working directory (default: current directory)Plain text output from Gemini:
Model response text here...
Error format (stderr):
ERROR: Error message
When calling via Bash tool, always include the timeout parameter:
Bash tool parameters:
- command: uv run ~/.claude/skills/gemini/scripts/gemini.py "<prompt>"
- timeout: 7200000
- description: <brief description of the task>
Alternatives:
# Direct execution (simplest)
- command: ~/.claude/skills/gemini/scripts/gemini.py "<prompt>"
# Using python3
- command: python3 ~/.claude/skills/gemini/scripts/gemini.py "<prompt>"
Basic query:
uv run ~/.claude/skills/gemini/scripts/gemini.py "explain quantum computing"
# timeout: 7200000
Code analysis:
uv run ~/.claude/skills/gemini/scripts/gemini.py "review this code for security issues: $(cat app.py)"
# timeout: 7200000
With specific working directory:
uv run ~/.claude/skills/gemini/scripts/gemini.py "analyze project structure" "/path/to/project"
# timeout: 7200000
Using python3 directly (alternative):
python3 ~/.claude/skills/gemini/scripts/gemini.py "your prompt here"
uv run for automatic Python environment management (requires uv installed)./gemini.py (uses system Python via shebang)GEMINI_MODEL environment variable)还没有评价。欢迎分享你的使用感受。
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Testing patterns for PHPUnit and Playwright E2E tests. Use when writing tests, debugging test failures, setting up test coverage, or implementing test patterns for ActivityPub features.
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Cloud laboratory platform for automated protein testing and validation. Use when designing proteins and needing experimental validation including binding assays, expression testing, thermostability measurements, enzyme activity assays, or protein sequence optimization. Also use for submitting experiments via API, tracking experiment status, downloading results, optimizing protein sequences for better expression using computational tools (NetSolP, SoluProt, SolubleMPNN, ESM), or managing protein design workflows with wet-lab validation.
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Add unsigned integer (uint) type support to PyTorch operators by updating AT_DISPATCH macros. Use when adding support for uint16, uint32, uint64 types to operators, kernels, or when user mentions enabling unsigned types, barebones unsigned types, or uint support.
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This skill should be used when the user asks to "create an agent", "add an agent", "write a subagent", "agent frontmatter", "when to use description", "agent examples", "agent tools", "agent colors", "autonomous agent", or needs guidance on agent structure, system prompts, triggering conditions, or agent development best practices for Claude Code plugins.
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Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.
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Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.
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