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Use this skill for guided MBSE work in MATLAB — starting a new project, resuming work mid-workflow on an existing project, or answering orientation questions about how the MBSE skills fit together. Trigger when the user says they want to create, start, or set up a new MBSE project; work on a model-based systems engineering / RFLPV project; or asks which skill covers which phase. Walks through phases one at a time — propose → approve → generate → run → confirm. Use proactively whenever someone mentions starting or continuing an MBSE project.

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Deploy AI models to embedded hardware using MathWorks tools (MATLAB, Simulink, Embedded Coder). Covers two workflow patterns: (1) MathWorks-native or 3P-imported models rebuilt as dlnetwork for lean hardware (Cortex-M, DSP), (2) direct C/C++ code generation from PyTorch and LiteRT models for high-performance hardware (Cortex-A, x86, GPU). Trigger when: user wants to deploy AI to embedded targets; generate C/CUDA from neural networks; compress AI models for MCU/DSP; integrate AI in Simulink for system-level simulation; import PyTorch/ONNX/TensorFlow models for embedded deployment; optimize AI for resource-constrained hardware; or use loadPyTorchExportedProgram, importNetworkFromPyTorch, dlquantizer, exportNetworkToSimulink, or Embedded Coder with AI models.

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Generate beautiful, distinctive HTML/CSS/JS control panels for MATLAB uihtml components. 8 built-in styles (Clean, Material, Cosmic Dark, Neumorphic Dark, Dashboard Light, Midnight Gradient, Minimal Mono, Warm Dark) plus custom aesthetics. Produces production-grade UI with sliders, buttons, toggles, and panels. Use when building visually polished MATLAB app UIs with uihtml.

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Generate an interactive bash wizard that walks a human through steps only they can perform. Use when provisioning infrastructure, setting up credentials or CI secrets, walking an unfamiliar third-party dashboard, or running a one-off migration or cutover. Don't invoke this for steps the agent can perform itself.

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Generates MLS-ready property listing descriptions with attention-grabbing headlines, feature highlights, neighborhood context, and SEO keywords across multiple buyer-persona styles

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Generate a structured implementation plan from an evidence draft. Validate paths, obtain configured independent Codex and Qoder reviews, synthesize available advice against repository evidence, preserve the draft, and produce testable acceptance criteria and validation steps.

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Generates Product Requirements Documents (PRD) for new features. Use when planning features or starting projects. Triggers on: create prd, write prd, plan feature, requirements, spec out.

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Generate "X vs Y" and "Alternative to X" SEO/CRO pages targeting competitive search intent during PRD v0.9 Go-to-Market. Triggers on requests to build comparison pages, target competitive search traffic, or when user asks "build alternatives pages", "X vs Y page", "competitor comparison page", "we're better than X page", "capture branded search", "competitive SEO". Outputs SCR-ALT-* page specs and GTM-* channel entries.

日本語の概要は準備中です。原文の説明を表示しています。

Create mathematical animations with Manim Community Edition(manimce), with optional TTS voiceover and synced subtitles. Generates distinctive, production-grade animations that avoid generic "AI slop" aesthetics. Use when user wants to animate concepts, equations, illustrate proofs, visualize algorithms, create math explainers, or produce 3Blue1Brown-style videos.

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Greenfield project architecture + harness scaffolding for AI Agent productivity. Interview-driven decisions → requirements.md → execute. Produces: Code Structure (vertical slice exemplar), Test Infrastructure, Guard Rails, conditional extensions, AND Harness (CLAUDE.md with domain/team context, rules, skills, hooks). L2: architecture decisions, L3: harness setup, L4: requirements + harness decisions (tasks generated later by /execute into plan.json). Use when: "/scaffold", "scaffold", "new project", "set up project", "프로젝트 세팅", "초기 구조"

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One-shot perspective reset that surfaces blind spots mid-work. Scans what the user has been doing, generates one abstract reframing question, and runs 3 quick checks (scope drift, side effects, better approach) in under 10 lines. No dialogue, no code. Trigger phrases: "/stepback", "step back", "한발 물러서", "넓은 관점", "놓치는 거 없나", "방향 맞나", "zoom out", "큰 그림", "방향이 맞는 거야", "잠깐 멈춰", "지금 뭘 하고 있는 거야", "blind spot", "재확인"

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Independently review a CUMCM computation package before its claims enter validation or paper writing. Use only inside a generated independent-review package.

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Use when the user has an AI-generated Xiaohongshu draft and wants it to sound more personal, concrete, platform-appropriate, and stronger in hook, retention, and interaction value. Trigger phrases: "去 AI 味", "不像我", "合规检查", "改得像真人", "开头不抓人", "没停留", "humanize draft", "platform check". Do not use for initial positioning or topic generation.

日本語の概要は準備中です。原文の説明を表示しています。

Generate technical architecture diagrams, pipeline flows, layer/stack diagrams, and system illustrations as standalone HTML files. Uses a dark-mode design system with embedded CSS and inline SVG. Use when the user needs architecture diagrams, system illustrations, flow charts, or technical visualizations.

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Transform audit data, performance reports, and structured analyses into polished visual deliverables. Generates presentation decks (PPTX), interactive HTML dashboards, or styled markdown reports with charts. Includes a customizable design system with forest green accent and warm cream backgrounds. Use when the user has analysis data and needs a visual deliverable.

日本語の概要は準備中です。原文の説明を表示しています。

Create distinctive, production-grade frontend interfaces with high design quality. Covers design thinking, typography pairing, color theory, motion design, spatial composition, and code quality. Generates real working code — HTML/CSS/JS or React — with intentional aesthetic direction, not generic AI output.

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Unified multi-platform paid media audit with parallel execution. Spawns scored audits for Google Ads, Meta Ads, and Microsoft Ads simultaneously, merges results into a budget-weighted aggregate health score (A-F), adds cross-platform analysis (budget allocation, tracking consistency, creative alignment, attribution overlap), and produces a prioritized action plan. Optionally generates a client-ready deck via pro-deck-builder. Use when the user asks for a full paid media audit, cross-platform review, or multi-channel ad assessment.

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Answer Engine Optimization (AEO) and Generative Engine Optimization (GEO) specialist. Optimize content and websites to appear in AI-generated answers from ChatGPT, Perplexity, Claude, Google AI Overviews, and other LLM-powered search experiences. Use when the user asks about AI search optimization, AEO, GEO, AI Overviews, appearing in AI answers, LLM citations, or optimizing for generative search.

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Generate changelog entries and GitHub releases from git history. Categorizes commits into features, fixes, breaking changes, and docs. Supports conventional commits, PR-based grouping, and semantic versioning. Creates formatted CHANGELOG.md entries and GitHub releases.

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Initialize GitHub repositories with standard files and configuration. Generates LICENSE, CONTRIBUTING.md, SECURITY.md, issue/PR templates, CI config, and .gitignore. Detects project type and adapts templates accordingly.

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Generate structured research briefs from RSS feeds and web sources on any topic. Synthesizes multiple sources into actionable analysis with key findings, data points, expert perspectives, and content angles.

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Generate structured data markup (JSON-LD) for rich results, AI search visibility, and enhanced SERP features. Supports Article, FAQ, HowTo, Product, Review, LocalBusiness, Event, BreadcrumbList, Person, Organization, and more. Use when the user needs schema markup, structured data, rich snippets, JSON-LD, or wants to improve search appearance.

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Generate Open Graph social preview images (1280x640) for GitHub repositories. Creates branded OG images that display when repos are shared on Twitter, LinkedIn, and Slack. Supports custom templates, dark/light themes, and automated generation.

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Generate and maintain llms.txt files for AI discoverability. Scans repos to create curated content maps that help AI answer engines surface your project accurately. Implements the llms.txt specification from Answer.AI.

日本語の概要は準備中です。原文の説明を表示しています。