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Found 62 Skills
Optimize and restructure user prompts for better AI responses. Use when user writes in non-English (Chinese, Japanese, Korean, etc.), request is vague/unclear, or user asks to improve their prompt. Triggers on: '帮我', '请帮忙', 'お願い', any non-English complex request. Translates, restructures, and shows optimized prompt before proceeding.
Get a second opinion from leading AI models on code, architecture, strategy, prompting, or anything. Queries models via OpenRouter, Gemini, or OpenAI APIs. Supports single opinion, multi-model consensus, and devil's advocate patterns. Trigger with 'brains trust', 'second opinion', 'ask gemini', 'ask gpt', 'peer review', 'consult', 'challenge this', or 'devil's advocate'.
Systematic LLM prompt engineering: analyzes existing prompts for failure modes, generates structured variants (direct, few-shot, chain-of-thought), designs evaluation rubrics with weighted criteria, and produces test case suites for comparing prompt performance. Triggers on: "prompt engineering", "prompt lab", "generate prompt variants", "A/B test prompts", "evaluate prompt", "optimize prompt", "write a better prompt", "prompt design", "prompt iteration", "few-shot examples", "chain-of-thought prompt", "prompt failure modes", "improve this prompt". Use this skill when designing, improving, or evaluating LLM prompts specifically. NOT for evaluating Claude Code skills or SKILL.md files — use skill-evaluator instead.
Generate AI images from text prompts. Triggers on: "生成图片", "画一张", "AI图", "generate image", "配图", "create picture", "draw", "visualize", "generate an image".
Use when users provide vague, underspecified, or unclear requests where they need help defining WHAT they actually want - across ANY domain (writing, analysis, code, documentation, proposals, reports, presentations, creative work). Trigger aggressively when users express VAGUE GOALS ("make this better", "improve our X", "figure out what to include", "I don't know where to start", "kinda lost on what to do", "not sure what this means"), UNDEFINED SUCCESS ("should look professional", "explain this clearly", "make it convincing", "whatever works best", missing constraints/audience/format), COMMUNICATION UNCLEAR ("how do I explain/communicate this", "my team gets confused when I describe it", "help me figure out what to ask about X"), AMBIGUOUS REQUIREMENTS ("analyze the data" without saying what to look for, "improve documentation" without saying how, "make it more robust" without defining robustness, any request with multiple valid interpretations), or META-PROMPTING ("optimize this prompt", "improve my prompt", "make this clearer", "review my instructions", learning about prompt frameworks like CO-STAR/RISEN/RODES, understanding what makes prompts effective). Trigger for non-technical users and ANY situation where the request needs refinement, structure, or clarification before execution can begin. When in doubt about whether a request is clear enough - trigger.
Transforms vague prompts into optimized Claude Code prompts. Adds verification, specific context, constraints, and proper phasing. Invoke with /best-practices.
Automatically intercepts and optimizes prompts using the prompt-learning MCP server. Learns from performance over time via embedding-indexed history. Uses APE, OPRO, DSPy patterns. Activate on "optimize prompt", "improve this prompt", "prompt engineering", or ANY complex task request. Requires prompt-learning MCP server. NOT for simple questions (just answer them), NOT for direct commands (just execute them), NOT for conversational responses (no optimization needed).
This skill should be used when the user asks to "create a replit prompt", "write a prompt for replit", "optimize for replit agent", "prepare instructions for replit", or mentions building something with Replit Agent. Transforms user requirements into optimized, structured prompts that Replit Agent understands and executes accurately with minimal iterations.
Creates professional AI image/video prompts with photographer's and cinematographer's eye. Specializes in composition, lighting, color grading, and storytelling. Use when generating AI images/videos with artistic vision, working with models like Nano Banana Pro, Qwen, Sora2, Wan 2.2. For graphic design work (thumbnails, banners, layouts), use /graphic-designer instead.
AI video pipeline validator for Veo 3 feasibility, 8-second scene chunking, and shot continuity. USE WHEN: Validating screenplays for AI video generation, chunking scenes into 8-second segments, generating continuation prompts, scoring feasibility risk, or adding editing metadata. PIPELINE POSITION: screenwriter → **production-validator** → imagine/arch-v INPUT: XML from screenwriter skill (scene tags with duration, action, key_visuals) OUTPUT: Enhanced XML with validation, chunks, continuity tags, and Veo 3 prompts KEY FUNCTIONS: - Veo 3 feasibility validation with risk scoring (LOW/MEDIUM/HIGH/CRITICAL) - 8-second scene chunking with continuation prompts - Shot continuity tagging for editors - Technical optimization for AI-friendly alternatives
Gas Town × DOK Framework - A two-dimensional model for analyzing AI collaboration maturity and cognitive complexity to reveal growth opportunities.
Transforms vague or simple user prompts into high-quality, structured, and high-performance AI instructions using systematic optimization techniques like XML tagging, few-shot examples, and Chain-of-Thought. Use this skill when you need to improve the reliability, accuracy, or formatting of an AI's output.