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Found 13 Skills
Operational prompt engineering for production LLM apps: structured outputs (JSON/schema), deterministic extractors, RAG grounding/citations, tool/agent workflows, prompt safety (injection/exfiltration), and prompt evaluation/regression testing. Use when designing, debugging, or standardizing prompts for Codex CLI, Claude Code, and OpenAI/Anthropic/Gemini APIs.
Use when writing, refining, or structuring prompts for AI-powered app features — system prompts, user prompt templates, few-shot examples, chain-of-thought, prompt versioning, and defensive prompting
Comprehensive AI prompt engineering safety review and improvement prompt. Analyzes prompts for safety, bias, security vulnerabilities, and effectiveness while providing detailed improvement recommendations with extensive frameworks, testing methodologies, and educational content.
Transform vague prompts into precise, well-structured specifications using EARS (Easy Approach to Requirements Syntax) methodology. This skill should be used when users provide loose requirements, ambiguous feature descriptions, or need to enhance prompts for AI-generated code, products, or documents. Triggers include requests to "optimize my prompt", "improve this requirement", "make this more specific", or when raw requirements lack detail and structure.
Generates blog post thumbnail images for Orbitant following the brand's visual identity, using Google's Imagen API (Nano Banana 2). Activates when creating blog images, generating thumbnails, designing featured images for articles, or when someone needs a visual for an Orbitant insight/blog post. Use this skill even if the user just says "I need an image for this article", "create a thumbnail", "generate a hero image", or "make a featured image". Also triggers when the user mentions "Nano Banana 2", "image generation", or asks for a prompt for an AI image tool.
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.
Create effective AI image generation prompts for DALL-E, Midjourney, and Stable Diffusion. Generate prompts for various styles and use cases.
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).
Create, optimize, and iteratively refine agent prompts and system prompts. Use when asked to "improve a prompt", "optimize a system prompt", "rewrite an agent prompt", "tune prompt wording", "make this prompt more reliable", or "adapt a prompt for OpenAI, Claude, or Gemini". Handles model-specific prompt guidance, prompt markers/tags, eval design, and meta optimization loops for new and existing prompts.
Generates Nano Banana Pro prompts for 4-panel engineer humor comics. Use when user mentions "漫画作成", "エンジニア漫画", "4コマ", or "あるある".
March Madness, playoff brackets, tournament picks. Upset potential, chalk vs contrarian strategies, historical trends, confidence levels.
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.