Ensure accessibility in UI components including semantic HTML, ARIA attributes, keyboard navigation, and WCAG 2.2 AA compliance.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when the user wants to use Google Gemini for analysis, large files or codebases, sandbox execution, or brainstorming. Uses headless Gemini CLI scripts (no MCP). Triggers on "use Gemini", "analyze with Gemini", "large file", "sandbox", "brainstorm with Gemini".
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Headless wrapper for Google Gemini CLI. Sends prompts via stdin to gemini -p "" --yolo.
Free tier available — no API key required (Google OAuth only).
Gemini CLI runs as a subprocess and includes model startup time:
Set expectations with the user before running long tasks.
node .claude/skills/omega-gemini-cli/scripts/ask-gemini.mjs "What is the best caching strategy for a Node.js API?"
node .claude/skills/omega-gemini-cli/scripts/ask-gemini.mjs "Explain async/await" --model gemini-2.5-flash
node .claude/skills/omega-gemini-cli/scripts/ask-gemini.mjs "Explain async/await" -m gemini-2.5-flash
node .claude/skills/omega-gemini-cli/scripts/ask-gemini.mjs "List 5 design patterns" --json
# Returns: {"response":"..."} on success, {"error":"...","raw":"..."} on parse failure
node .claude/skills/omega-gemini-cli/scripts/ask-gemini.mjs "Write and run a fibonacci function" --sandbox
node .claude/skills/omega-gemini-cli/scripts/ask-gemini.mjs "Write and run a fibonacci function" -s
echo "Explain recursion" | node .claude/skills/omega-gemini-cli/scripts/ask-gemini.mjs
node .claude/skills/omega-gemini-cli/scripts/ask-gemini.mjs "Review this code: $(cat src/auth.ts)"
node .claude/skills/omega-gemini-cli/scripts/verify-setup.mjs
# Exit 0 = available (Node 18+ and Gemini CLI found)
# Exit 1 = not installed or too old
| Script | Purpose |
|---|---|
ask-gemini.mjs | Core headless wrapper — sends prompt via stdin |
parse-args.mjs | Argument parser (--model/-m, --json, --sandbox/-s) |
verify-setup.mjs | Availability check (Node 18+, Gemini CLI via PATH or npx) |
format-output.mjs | Output normalization (JSON envelope handling) |
| Flag | Short | Description |
|---|---|---|
--model MODEL | -m | Gemini model (e.g., gemini-2.5-flash, gemini-2.5-pro) |
--json | Machine-readable JSON output: {"response":"..."} envelope | |
--sandbox | -s | Code execution sandbox mode |
| Model ID | Notes |
|---|---|
gemini-3-pro-preview | Latest (2026), highest capability |
gemini-3-flash-preview | Latest (2026), faster |
gemini-2.5-pro | Stable, high capability |
gemini-2.5-flash | Recommended: lower quota/latency |
gemini-2.5-flash-lite | Lightest, fastest, lowest quota usage |
| Code | Meaning |
|---|---|
| 0 | Success |
| 1 | Error (CLI failure, auth issue, JSON parse failure with --json) |
| 9009 | Windows: command not found (falls back to npx automatically) |
| Command | Purpose |
|---|---|
/analyze | Run headless script with user's prompt (and any @ file refs) |
/sandbox | Run with --sandbox; execute or test code |
/omega-gemini | Alias: run headless for analysis, sandbox, or brainstorm as appropriate |
/brainstorm | Brainstorm mode (build prompt with challenge + optional methodology) |
/omega-gemini-setup | Verify Node and Gemini CLI; guide user to install and auth. No MCP. |
^[a-zA-Z0-9._-]+$ on Windows)@google/gemini-cli fallback automaticallygemini interactive session (run gemini once to sign in)shell: true with model validation; non-Windows uses array args (no shell)npx -y @google/gemini-cli--json wraps output as {"response":"..."} on success or {"error":"...","raw":"..."} on failurenpm install -g @google/gemini-cli) with one-time Google sign-inBefore work: Read .claude/context/memory/learnings.md
After work: Append findings to learnings or issues as needed.
Note: Use pnpm search:code to discover references to this skill codebase-wide.
아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.
로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.
아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.
Ensure accessibility in UI components including semantic HTML, ARIA attributes, keyboard navigation, and WCAG 2.2 AA compliance.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when you want to improve response quality through meta-cognitive reasoning. Applies 15+ reasoning methods to reconsider and refine initial outputs.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
N-round opposing-stance debates for trade-off analysis. Assigns pro/con roles to agents, runs structured debate rounds with quality scoring, and produces a moderator synthesis with confidence-rated recommendation. Generalizable to architecture, technology, security, and design decisions.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Force adversarial code review stance that eliminates confirmation bias — reviewer must find issues or re-analyze
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Creates specialized AI agents on-demand when no existing agent matches a request. Use when the Router cannot find a suitable agent for a task. Enables self-evolution by generating persistent agents.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
LLM-as-judge evaluation framework with 5-dimension rubric (accuracy, groundedness, coherence, completeness, helpfulness) for scoring AI-generated content quality with weighted composite scores and evidence citations
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.