Ensure accessibility in UI components including semantic HTML, ARIA attributes, keyboard navigation, and WCAG 2.2 AA compliance.
日本語の概要は準備中です。原文の説明を表示しています。
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".
インストールする前に、エージェントに与えられる指示の中身を確認できます。
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.
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Ensure accessibility in UI components including semantic HTML, ARIA attributes, keyboard navigation, and WCAG 2.2 AA compliance.
日本語の概要は準備中です。原文の説明を表示しています。
Use when you want to improve response quality through meta-cognitive reasoning. Applies 15+ reasoning methods to reconsider and refine initial outputs.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
Force adversarial code review stance that eliminates confirmation bias — reviewer must find issues or re-analyze
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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
日本語の概要は準備中です。原文の説明を表示しています。