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Found 378 Skills
Novel Cover Generation. Automatically analyze the genre style based on the book title and author's name, call GPT-Image-2 to directly generate a professional web novel cover with title and signature. Trigger methods: /story-cover, /封面, "Help me make a cover", "Generate cover image", "Make a novel cover", "Cover design"
Curated collection of high-quality prompts for various use cases. Includes role-based prompts, task-specific templates, and prompt refinement techniques. Use when user needs prompt templates, role-play prompts, or ready-to-use prompt examples for coding, writing, analysis, or creative tasks.
This skill should be used when the user asks to "optimize prompts", "design prompt templates", "evaluate LLM outputs", "build agentic systems", "implement RAG", "create few-shot examples", "analyze token usage", or "design AI workflows". Use for prompt engineering patterns, LLM evaluation frameworks, agent architectures, and structured output design.
Expert in designing effective prompts for LLM-powered applications. Masters prompt structure, context management, output formatting, and prompt evaluation. Use when "prompt engineering, system prompt, few-shot, chain of thought, prompt design, LLM prompt, instruction tuning, prompt template, output format, prompts, llm, gpt, claude, system-prompt, few-shot, chain-of-thought, evaluation" mentioned.
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).
Generate aesthetics-prioritized image generation prompts tailored for the Chinese manhua drama market. It supports various genres including xianxia, wuxia, ancient Chinese style, urban fantasy, urban romance, hot-blooded combat, post-apocalyptic wasteland, mystery & detective, and campus youth, paired with painting styles like ink wash painting, gongbi painting, cel-shading, impasto, watercolor, semi-realism, and flat illustration. Triggered when users need to create beautiful manhua drama image prompts, descriptions of Chinese manhua characters/scenes/objects, or mention keywords such as "manhua drama", "Chinese manhua", "generate character", "xianxia scene", "ancient-style character", "urban manhua", "draw one".
Write and optimize prompts for AI-generated outcomes across text and image models. Use when crafting prompts for LLMs (Claude, GPT, Gemini), image generators (Midjourney, DALL-E, Stable Diffusion, Imagen, Flux), or video generators (Veo, Runway). Covers prompt structure, style keywords, negative prompts, chain-of-thought, few-shot examples, iterative refinement, and domain-specific patterns for marketing, code, and creative writing.
Invokes Codex CLI as a second opinion. Use for reviewing plans, code, architectural decisions, or getting an independent perspective from OpenAI's reasoning models.
Generate images using FAL.ai nanobanana pro. Use when creating product shots, social graphics, brand assets, or any visual content. Integrates with automation system for direct asset generation in Claude Code.
Transform prompts into structured TCRO format with phase-specific clarification. Automatically invoked by /ai-eng/research, /ai-eng/plan, /ai-eng/work, and /ai-eng/specify commands. Use when refining vague prompts, structuring requirements, or enhancing user input quality before execution.
Measure and improve how well your AI works. Use when AI gives wrong answers, accuracy is bad, responses are unreliable, you need to test AI quality, evaluate your AI, write metrics, benchmark performance, optimize prompts, improve results, or systematically make your AI better. Covers DSPy evaluation, metrics, and optimization.
Analyze other agents' sessions and construct targeted corrective prompts to fix mistakes, correct context drift, or drive home task requirements