Pre-action boundary checking — validates agent tool calls against declared capabilities and task contracts
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Execute Gemini CLI prompts and return results
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설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Execute Google Gemini CLI prompts in non-interactive mode and return structured results. Enables Claude + Gemini hybrid workflows.
<prompt> Required. The prompt to send to Gemini CLI
--json Return structured JSON output (-o json)
--stream-json Return streaming JSON events (-o stream-json)
--output <path> Save response to file
--model <name> Model override (default: Gemini CLI default)
--timeout <ms> Execution timeout (default: 120000, max: 600000)
--yolo Enable auto-approval mode (gemini -y)
--sandbox Run in sandbox mode (gemini -s)
--plan Use plan approval mode (--approval-mode plan)
--working-dir Working directory for Gemini execution
1. Pre-checks
- Verify `gemini` binary is installed (which gemini)
- Verify authentication (GOOGLE_API_KEY, GEMINI_API_KEY, or gcloud auth)
2. Build command
- Base: gemini -p "<prompt>"
- Apply options: -o json, -m <model>, -y, -s, --approval-mode plan
3. Execute
- Run via Bash tool with timeout (default 2min, max 10min)
- Or use helper script: node .claude/skills/gemini-exec/scripts/gemini-wrapper.cjs
4. Parse output
- Text mode: return raw stdout
- JSON mode: parse single JSON object, extract response field
- Stream-JSON mode: parse event stream, extract final assistant message
5. Report results
- Format output with execution metadata
-p flag: Non-interactive prompt mode (no session persistence)--yolo only when explicitly requested-s) available for isolated execution[Gemini Exec] Completed
Model: (default)
Duration: 23.4s
Working Dir: /path/to/project
--- Output ---
{gemini response text}
[Gemini Exec] Completed (JSON)
Model: (default)
Duration: 23.4s
--- Response ---
{extracted response from JSON}
--- Stats ---
{token usage and other stats}
[Gemini Exec] Completed (Stream-JSON)
Model: (default)
Duration: 23.4s
Events: 12
--- Final Message ---
{extracted final assistant message}
[Gemini Exec] Failed
Error: {error_message}
Exit Code: {code}
Suggested Fix: {suggestion}
For complex executions, use the wrapper script:
node .claude/skills/gemini-exec/scripts/gemini-wrapper.cjs --prompt "your prompt" [options]
The wrapper provides:
# Simple text prompt
gemini-exec "explain what this project does"
# JSON output with model override
gemini-exec "list all TODO items" --json --model gemini-2.5-pro
# Stream-JSON for detailed event tracking
gemini-exec "analyze the codebase" --stream-json
# Save output to file
gemini-exec "generate a README" --output ./README.md
# Sandbox mode with auto-approval
gemini-exec "fix the failing tests" --yolo --sandbox
# Plan mode for careful execution
gemini-exec "refactor the auth module" --plan
# Specify working directory
gemini-exec "analyze the codebase" --working-dir /path/to/project
Works with the orchestrator pattern:
gemini-exec requires the Gemini CLI binary to be installed and authenticated. The skill is only usable when:
gemini binary is found in PATH (which gemini succeeds)If either check fails, this skill cannot be used. Fall back to Claude agents for the task.
Note: This skill is invoked via
/gemini-execcommand, delegated by the orchestrator, or suggested by routing skills when gemini is available. The intent-detection system can trigger it for research and code generation hybrid workflows.
When used within Agent Teams (requires explicit invocation):
Orchestrator delegates generation task
→ /gemini-exec invoked explicitly
→ Output returned to orchestrator
→ Reviewer validates quality
→ Iterate if needed
When the orchestrator or intent-detection detects a research request:
gemini binary and auth/gemini-exec "Research and analyze: {topic}. Provide structured findings with sources." --json
When routing skills detect a code generation task and gemini is available:
/tmp/.claude-env-status-*/gemini-exec "Generate {description} following {framework} best practices" --yolo
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Pre-action boundary checking — validates agent tool calls against declared capabilities and task contracts
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Auto-detect project context and optimize harness — deactivate unused agents/skills, suggest missing experts, generate project profile
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Adversarial code review using attacker mindset — trust boundary, attack surface, business logic, and defense evaluation
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Apache Airflow best practices for DAG authoring, testing, and production deployment
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Alembic migration patterns for naming conventions, safety checks, expand-contract, env.py configuration, and CI integration
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Pre-routing ambiguity analysis — scores request clarity and asks clarifying questions when needed (inspired by ouroboros)
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