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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.
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Execute Gemini CLI for AI-powered code analysis and generation. Use when you need to leverage Google's Gemini models for complex reasoning tasks.
Execute spec tasks one at a time with tmux visibility. Python-based orchestrator with transparent serial execution. Triggers on: rate limit, overnight run, throttled execution, avoid quota exhaustion, sequential mode, slow execution, or user says 'Run sequential orchestration for <spec>'.
Create a Kiro spec for a feature in .kiro/specs create/update feature spec/PRD/RFC—requirements, design doc, and implementation tasks checklist.Trigger on spec/specification/PRD/RFC/tech spec, requirements/user story/acceptance criteria/EARS, design doc/architecture, task breakdown/implementation plan/checklist; 需求/验收/设计/任务.
Sync spec files with code changes. Triggers when modifying code that affects .kiro/specs/*/requirements.md or .kiro/specs/*/design.md. Use after implementing features, fixing bugs, or refactoring that changes behavior documented in specs.
Orchestrate multi-agent workflows from a Kiro spec using codex (code) + Gemini (UI), including dispatch/review/state sync via AGENT_STATE.json + PROJECT_PULSE.md; triggers on user says "Start orchestration from spec at <path>", "Run orchestration for <feature>", or mentions multi-agent execution.
Execute codeagent-wrapper for multi-backend AI code tasks. Supports Codex, Claude, and Gemini backends with file references (@syntax) and structured output.
Execute Codex CLI for code analysis, refactoring, and automated code changes. Use when you need to delegate complex code tasks to Codex AI with file references (@syntax) and structured output.
| name | gemini |
| description | Execute Gemini CLI for AI-powered code analysis and generation. Use when you need to leverage Google's Gemini models for complex reasoning tasks. |
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)