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Found 484 Skills
Generate infographic images from user descriptions using Gemini API (Nano Banana Pro). Converts natural language descriptions into structured infographic prompts, then calls Gemini image generation to produce PNG images. Supports 11 visual styles (sketchnote, kawaii, professional, scientific, anime, claymation, editorial, storyboard, bento grid, bricks), 3 orientations (landscape/portrait/square), 3 detail levels (brief/standard/detailed), and multiple languages. Use when user asks to create infographics, generate visual summaries, make data visualizations, or produce illustrated explanations. Trigger words include 信息图, infographic, 生成图, 可视化, visual summary, data visualization.
Watch and analyze YouTube videos using Gemini's video understanding API. Pass any YouTube URL to get summaries, timestamps, Q&A, or detailed analysis of video content — audio and visual.
Exports the project's Claude configuration (CLAUDE.md + ai-context/) to tool-specific instruction files for GitHub Copilot, Google Gemini, and Cursor. Trigger: /config-export, export config, copilot instructions, gemini config, cursor rules.
Google Gemini integration. Manage Users, Conversations. Use when the user wants to interact with Google Gemini data.
Cross-model second opinion from Google Gemini — a different AI reviewing the same changes, with deep Google ecosystem knowledge. Three modes: review (pass/fail gate for Google Ads campaigns, SEO metadata, or code), challenge (adversarial stress-test that tries to break your changes), and consult (open Q&A with Gemini on Google Ads strategy, SEO best practices, or implementation questions). Use when the user says "gemini review", "ask gemini", "gemini challenge", "second opinion from gemini", "consult gemini", "stress test with gemini", "what would gemini say", "cross-model review", or "get another opinion". Voice aliases: "gem", "gemini check". Especially useful for Google Ads changes, SEO metadata updates, campaign structure decisions, keyword strategies, and bid/budget changes — Gemini has native Google ecosystem knowledge that complements Claude's analysis.
Generate images using Google Gemini's image generation API for UI mockups, icons, illustrations, and visual assets.
Google Gemini API with @google/genai SDK. Use for multimodal AI, thinking mode, function calling, or encountering SDK deprecation warnings, context errors, multimodal format errors.
Build RAG systems and semantic search with Gemini embeddings (gemini-embedding-001). 768-3072 dimension vectors, 8 task types, Cloudflare Vectorize integration. Prevents 13 documented errors. Use when: vector search, RAG systems, semantic search, document clustering. Troubleshoot: dimension mismatch, normalization required, batch ordering bug, memory limits, wrong task type, rate limits (100 RPM).
Build document Q&A with Gemini File Search - fully managed RAG with automatic chunking, embeddings, and citations. Upload 100+ file formats, query with natural language. Use when: document Q&A, searchable knowledge bases, semantic search. Troubleshoot: document immutability, storage quota (3x), chunking config, metadata limits (20 max), polling timeouts, displayName dropped (Blob uploads), grounding lost (JSON mode), tool conflicts (googleSearch + fileSearch).
Generate, edit, and compose images using Gemini Nano Banana models via portable Python scripts. Handles authentication via API Key or Vertex AI environment variables. Available parameters: prompt, model, aspect-ratio, safety-filter-level. Always confirm parameters with the user or explicitly state defaults before running.
Invokes Gemini CLI as a second opinion. Use for reviewing plans, code, architectural decisions, AND for analyzing large volumes of content that benefit from Gemini's 1M+ token context window.
Review web animations by recording the browser and sending video to Gemini for frame-level analysis