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Found 658 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.
Manages custom Agent resources on Gemini Enterprise Agent Platform. Use when the user wants to programmatically create, configure, list, update, or delete stateful, server-managed Agent resources (including mounting files, skills, and tools) before executing conversations.
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).
Enables Claude to conduct comprehensive research using Gemini Deep Research for in-depth analysis and reports
Enables Claude to interact with Gemini AI chat for quick queries, brainstorming, and alternative AI perspectives
Use Google Gemini API for text generation, multimodal analysis, image generation (Nano Banana), function calling, and search grounding. Invoke when user wants to use Gemini, ask Gemini, generate images with Gemini, or analyze content with Gemini.
Run Gemini CLI to take a screenshot for a specific page.
Google Gemini embeddings API (gemini-embedding-001) for RAG and semantic search. Use for vector search, Vectorize integration, or encountering dimension mismatches, rate limits, text truncation.
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).
Master of LLM Economic Orchestration, specialized in Google GenAI (Gemini 3), Context Caching, and High-Fidelity Token Engineering.
Upload and manage files using Google Gemini File API via scripts/. Use for uploading images, audio, video, PDFs, and other files for use with Gemini models. Supports file upload, status checking, and file management. Triggers on "upload file", "file API", "upload image", "upload PDF", "upload video", "file management".
Guides the usage of Gemini API on Google Cloud Vertex AI with the Gen AI SDK. Use when the user asks about using Gemini in an enterprise environment or explicitly mentions Vertex AI. Covers SDK usage (Python, JS/TS, Go, Java, C#), capabilities like Live API, tools, multimedia generation, caching, and batch prediction.