google-ai-mode-skill

name google-ai-mode-skill description Use this skill when the user requests current information, documentation, coding examples, or web research beyond the knowledge cutoff. Queries Google's AI Search mode to retrieve comprehensive AI-generated overviews with source citations from 100+ websites. Returns markdown with inline footnoted references [1][2][3]. You will receive a detailed Markdown file with references and information summarized directly from Google's AI search. Ideal for you to get new information and clues for further research. Google AI Mode Skill Query Google's AI Search mode to retrieve comprehensive, source-grounded answers from across the web. When to Use This Skill Trigger this skill when the user: Requests current information beyond the knowledge cutoff (post-January 2025) Needs documentation or API references for libraries and frameworks Asks for coding examples or implementation patterns Wants technical comparisons or best practices Requires research with citations and sources Mentions "Google AI search", "Google AI mode", or "web research" CLI Flags Essential Flags --debug - Enable comprehensive logging python scripts/run.py search.py --query "..." --debug Saves detailed logs to logs/search_YYYY-MM-DD_HH-MM-SS.log Logs every step: browser launch, CAPTCHA detection, AI content waiting, citation extraction Essential for troubleshooting CAPTCHA issues or failed searches Log file path printed at completion --save - Save results to skill folder python scripts/run.py search.py --query "..." --save Saves markdown to results/YYYY-MM-DD_HH-MM-SS_Query_Name.md Timestamped filename for organized storage Results preserved in skill directory for future reference Use instead of --output for automatic naming Combined usage (recommended for debugging): python scripts/run.py search.py --query "..." --debug --save Other Flags --show-browser - Show browser window (for CAPTCHA solving) python scripts/run.py search.py --query "..." --show-browser --output <path> - Custom output file path python scripts/run.py search.py --query "..." --output result.md --json - Include JSON metadata in output python scripts/run.py search.py --query "..." --output result.md --json Query Optimization Strategy CRITICAL : Always optimize user queries before execution. Google AI Mode's quality depends on query precision. Optimization Template [Technology/Topic] [Version] [Year] ([Specific Aspect 1], [Aspect 2], [Aspect 3]). [Output format request]. Optimization Rules Include Current Year (2026) for up-to-date results Use parentheses to list specific aspects needed Request structured output (tables, comparisons, categorized lists) Include version numbers for library/framework queries Examples User Query Optimized Query "React hooks" "React hooks best practices 2026 (useState, useEffect, custom hooks, common pitfalls). Provide code examples." "What's new in Rust?" "Rust 1.75 new features 2026 (async traits, impl Trait improvements, const generics, stabilized APIs). Include migration guide and code examples." "PostgreSQL vs MySQL performance?" "PostgreSQL vs MySQL performance comparison 2026 (query optimization, indexing strategies, concurrent writes, JSON handling, scaling patterns). Provide benchmark data and use case recommendations." "How to handle errors in Go?" "Go error handling patterns 2026 (error wrapping, custom errors, sentinel errors, panic vs error, testing error cases). Provide code examples and best practices comparison." "Learn FastAPI basics" "FastAPI tutorial 2026 (routing, dependency injection, async endpoints, request validation with Pydantic, OpenAPI documentation, testing). Include step-by-step implementation guide." Note: If user provides an already detailed query with version numbers and requirements, use it as-is. Workflow Receive user request Optimize query using template above Inform user: "Searching for: '[optimized query]'" Execute search with --save --debug flags Return results with inline citations [1][2][3] Script Execution CRITICAL : Always use the run.py wrapper. Direct script execution will fail. Basic Search python scripts/run.py search.py --query "Your search query" Recommended Usage python scripts/run.py search.py --query "..." --save --debug The run.py wrapper automatically: Creates .venv on first run Installs dependencies (patchright, beautifulsoup4, html-to-markdown) Activates virtual environment Executes search script Installs Google Chrome (not Chromium) for anti-detection How It Works Persistent Browser Context : Uses saved browser profile at ~/.cache/google-ai-mode-skill/chrome_profile to preserve cookies/session between searches Eliminates CAPTCHAs : Persistent context means Google recognizes the browser → rarely triggers CAPTCHA AI Content Detection : Waits for Google AI Overview to appear on page Citation Extraction : Injects JavaScript to extract source links from AI response Markdown Conversion : Converts HTML to markdown with inline citations [1][2][3] Fast Results : Typical search completes in 5-7 seconds (no CAPTCHA) CAPTCHA Handling With persistent context, CAPTCHAs are rare. If encountered: Detection : Multi-layer check (URL /sorry/index , page text, content length) Automatic Handling : If CAPTCHA detected in headless mode → script returns CAPTCHA_REQUIRED error Manual Solution : Re-run with --show-browser flag, solve CAPTCHA in browser, script continues automatically Note : After CAPTCHA is solved once, persistent context preserves the session → future searches won't require CAPTCHA. Output Format Returns markdown with inline citations and source list. Example: React 18 introduces concurrent features including Suspense for data fetching[1], automatic batching for state updates[2], and transitions for non-urgent updates[3]. --- ## Sources: [1] React 18 Release Notes https://react.dev/blog/2022/03/29/react-v18 [2] Automatic Batching Explained https://github.com/reactwg/react-18/discussions/21 [3] Transitions API Documentation https://react.dev/reference/react/useTransition Common Use Cases Finding Library Documentation python scripts/run.py search.py --query "Prisma ORM 2026 (schema definition, migrations, client API, relation queries, transactions). Include TypeScript examples." --save --debug Getting Coding Examples python scripts/run.py search.py --query "WebSocket implementation Node.js 2026 (server setup, client connection, message handling, authentication, reconnection logic). Production-ready code examples." --save Technical Comparisons python scripts/run.py search.py --query "GraphQL vs REST API 2026 (performance, caching, tooling, type safety, learning curve). Comparison table with use case recommendations." --save Best Practices Research python scripts/run.py search.py --query "Microservices security patterns 2026 (API gateway authentication, service mesh, mutual TLS, secret management, observability). Architecture diagrams and implementation guide." --save --debug Troubleshooting Issue Solution ModuleNotFoundError Use run.py wrapper, never execute scripts directly CAPTCHA every time First-time setup: solve CAPTCHA once with --show-browser , then persistent context preserves session No AI overview found Rephrase query with more specificity using optimization template Browser fails to start Verify internet connection and Chrome installation Need detailed logs Use --debug flag - log saved to logs/ folder AI Mode not available Your region/country doesn't support Google AI Mode. Use a proxy/VPN to access from supported regions (US, UK, Germany, etc.) Exit Codes: 0 - Success 1 - General error 2 - CAPTCHA required (retry with --show-browser ) 3 - Browser closed by user 4 - AI Mode not available in region (use proxy/VPN) 130 - User interrupted (Ctrl+C) Best Practices Always optimize queries - Specificity determines result quality Use --save --debug for important searches - Preserves results and provides audit trail Include version numbers for library/framework queries Request structured output - Tables and comparisons improve usability Solve CAPTCHA once - Persistent context eliminates future CAPTCHAs Verify citations - Check provided sources for accuracy