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Found 432 Skills
Analyzes and improves LLM prompts and agent instructions for token efficiency, determinism, and clarity. Use when (1) writing a new system prompt, skill, or CLAUDE.md file, (2) reviewing or improving an existing prompt for clarity and efficiency, (3) diagnosing why a prompt produces inconsistent or unexpected results, (4) converting natural language instructions into imperative LLM directives, or (5) evaluating prompt anti-patterns and suggesting fixes. Applies to all LLM platforms (Claude, GPT, Gemini, Llama).
Create and configure custom OpenCode agents (primary and subagents) with specialized prompts, tools, permissions, and models. Use when the user wants to create, modify, or configure OpenCode agents, or mentions agent modes, tool permissions, or task delegation.
Transforms vague UI/feature requests into structured, optimized prompts with design system awareness. Use when generating prompts for UI implementation, feature specification, or design-to-code translation. Triggers on tasks requiring prompt refinement, UI specification, or design system integration.
An expert prompt engineering skill that turns Claude into "Alpha-Prompt" — a master prompt engineer who collaboratively creates high-quality prompts through flexible dialogue. This skill activates when users request to "optimize prompt", "improve system instruction", "enhance AI instruction", or mention prompt engineering tasks.
Triggered when users provide dream text materials, diary fragments, or oral dream descriptions and wish to generate videos. Trigger phrases include: "dreamt of", "had a dream", "dream material", "help me generate a video", "convert to video", "dream to video". It also applies to scenarios where users directly paste a dream description and expect to receive a video file. This skill converts text into video prompts, automatically submits them to the Jiemeng Platform for generation, and downloads the video files.
Write, review, and improve prompts for any LLM — Claude, GPT, Gemini, Llama, DeepSeek, Mistral, Cohere, Qwen, Grok, Nova, and more. Use when the user asks to "write a system prompt", "improve this prompt", "review my prompt", "make a prompt for", "optimize my prompt", "fix my prompt", "why isn't my prompt working", or wants help writing better prompts for any AI model. Also use when building agents, chatbots, or AI assistants that need system-level instructions, or when the user has a bad prompt they want rewritten. Covers system prompts, task prompts, tool descriptions, and general prompt improvement across all major model families.
Analyze requirements during the functional design and problem diagnosis stages to develop executable solutions, and output user-facing and AI-facing action documents separately.
Uncle Huang's Private Advisory Board — a business decision think tank composed of 12 top thinkers. With a structured private advisory board process, different cognitive frameworks collide to produce optimal decisions. Trigger scenarios: - Users say "start a private advisory board", "invite the think tank", "help me make decisions" - Users say "Private Advisory Board: [topic]", "Let the experts discuss this matter" - Users use the /私董会 or /advisory-board command - Users face major business decisions requiring multi-perspective analysis Non-trigger scenarios: - Simple operational issues (how to publish, how to typeset) - Pure technical issues (code debugging) - Daily content creation (use article-writer / topic-partner) - Personal emotional counseling (use life-coach)
Generate anime-style video prompts for Seedance 2.0 (Higgsfield). Use this when users want anime, Japanese animation style, shonen manga action, seinen manga drama, magical girl, mecha, isekai, slice-of-life anime, or any Japanese animation aesthetics. Trigger conditions: anime, Japanese animation, shonen manga, seinen manga, manga-style video, anime fight, anime opening, anime ending, cherry blossoms, chibi, cute style, mecha, isekai, or any anime-style request. Even phrases like "make it look like anime" or "Japanese cartoon style".
Use when designing prompts for LLMs, optimizing model performance, building evaluation frameworks, or implementing advanced prompting techniques like chain-of-thought, few-shot learning, or structured outputs.
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).
Master Anthropic's prompt engineering techniques to generate new prompts or improve existing ones using best practices for Claude AI models.