Loading...
Loading...
Found 378 Skills
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.
Enables Claude to use Google AI Studio for testing prompts, exploring models, and prototyping AI applications
Generate Dreamina-compatible prompts from music analysis — per-section visual descriptions
将原始研究问题细化为结构化的深度研究任务。通过提问澄清需求，生成符合 OpenAI/Google Deep Research 标准的结构化提示词，完全替代 ChatGPT 的问题细化功能。当用户提出研究问题、需要帮助定义研究范围、或想要生成结构化研究提示词时使用此技能。
You are an expert prompt engineer specializing in crafting effective prompts for LLMs through advanced techniques including constitutional AI, chain-of-thought reasoning, and model-specific optimizati
Produce an LLM Build Pack (prompt+tool contract, data/eval plan, architecture+safety, launch checklist). Use for building with LLMs, GPT/Claude apps, prompt engineering, RAG, and tool-using agents.
Effective communication strategies for AI-assisted development. Learn context-first prompting, phased interactions, iterative refinement, and validation techniques to get better results from Claude and other AI coding assistants.
Generate hand-drawn style diagrams and infographics for recovery education articles. Creates anatomist's notebook aesthetic visuals - brain diagrams, timelines, social comparisons, and process flows using continuous line art, semantic color coding, and margin annotations.
Analyze and improve existing prompts for better performance
Provides comprehensive guidance for Stable Diffusion AI image generation including model usage, prompt engineering, parameters, and image generation. Use when the user asks about Stable Diffusion, needs to generate AI images, configure models, or work with Stable Diffusion.
Skill for image generation. Uses Google Nano Banana Pro (Gemini 3 Pro Image) API to generate high-quality images. Supports logos, infographics, illustrations, photorealistic images, and more.
Guide for defining and using Claude subagents effectively. Use when (1) creating new subagent types, (2) learning how to delegate work to specialized subagents, (3) improving subagent delegation prompts, (4) understanding subagent orchestration patterns, or (5) debugging ineffective subagent usage.