「architectural」の検索結果

Applies the mental models and frameworks of Andrej Karpathy (deep learning, former Director of AI at Tesla, founding member of OpenAI, Eureka Labs). Use this skill whenever you are helping the user build neural networks from scratch, debug deep learning pipelines, evaluate AI agent workflows, design LLM apps, or navigate the transition to Software 3.0 (vibe coding). It is highly relevant for pedagogy (untangling complex knowledge), assessing AI capabilities vs. limitations (jagged intelligence, tokenization limits), and architectural decisions (end-to-end optimization vs. complex pipelines). Reach for this whenever discussing LLM training, autonomous systems, or AI-assisted coding.

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Applies the engineering and research philosophies of Jeff Dean, Chief Scientist at Google DeepMind and Google Research. Reach for this skill whenever you are designing large-scale distributed systems, optimizing latency and energy efficiency, or making architectural decisions about machine learning infrastructure. It should trigger automatically for topics involving hardware-ML co-design, model distillation, sparse activation, massively multi-task models, or scaling systems by 5x to 10x. Use this skill to evaluate system bottlenecks, transition from specialized to unified models, and optimize experimental velocity. Apply his mental models to avoid premature 100x scaling and to treat AI models as reasoning engines rather than memorization databases.

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Applies the reasoning, architectural principles, and AI philosophy of Christopher Manning (natural language processing expert, Stanford University, director of Stanford AI Lab). Use this skill whenever you are discussing natural language processing, LLM architecture, AI research strategy, cognitive science, or the evolution of machine learning. Trigger this skill for questions about AGI timelines, academic vs. industry research trade-offs, linguistic structure in neural networks, modularity in AI design, or evaluating true intelligence versus mere memorization. Channel his pragmatic focus on domain science, adaptability, and competing on ideas rather than raw compute.

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Detect and analyze areas with high maintenance cost, poor design, or accumulated technical debt. Use this skill when reviewing codebases for quality issues, planning refactoring efforts, conducting code audits, assessing project health, identifying maintenance hotspots, or prioritizing technical improvements. Analyzes code smells, architectural issues, dependency problems, test quality, documentation gaps, and provides actionable recommendations with priority rankings.

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Recommends appropriate software design patterns based on problem descriptions, requirements, or code scenarios. Use when designing software architecture, refactoring code, solving common design problems, or choosing between design approaches. Analyzes the problem context and suggests suitable creational, structural, behavioral, architectural, or concurrency patterns with implementation guidance and trade-off analysis.

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Plan, build, place, and validate physically meaningful Blender object assemblies, including manufactured part boundaries, mechanical seams, mounting interfaces, pivots, hierarchy, constraints, instancing, modular grids, connector planes, and precision snapping. Use when several objects must form one product, machine, architectural module, vehicle, furniture item, mechanism, or environment structure without incorrectly welding independent parts, eyeballing alignment, or leaving continuous shells as overlapping primitives.

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Build and refine production Blender geometry through semantic vertex/edge/face editing, BMesh, subdivision control cages, Boolean construction, Displace relief, remesh, sculpt preparation, retopology, and deliberate surface connections. Use after blockout for hard-surface, product, organic, architectural openings, terrain/relief, or reference-driven assets when shape quality, topology, continuity, or realistic part integration matters.

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Consult external AIs (Gemini 2.5 Pro, OpenAI Codex, Claude) for second opinions. Use for debugging failures, architectural decisions, security validation, or need fresh perspective with synthesis.

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Google Gemini CLI for second opinions, architectural advice, code reviews, security audits. Leverage 1M+ context for comprehensive codebase analysis via command-line tool.

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Automate 7-phase feature development with specialized agents (code-explorer, code-architect, code-reviewer). Use for multi-file features, architectural decisions, or encountering ambiguous requirements, integration patterns, design approach errors.

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Answer quick architectural questions, debug mini-issues, explore the codebase, and help improve skills and workflows. Use this skill when the user asks "how does X work", "where is Y", "why does Z happen", wants to understand code flow, trace a bug through the system, or asks about the relationship between components. Also use when the user wants to improve an existing skill or refine development workflows. This is the lightweight, exploratory counterpart to the planner -- use it for questions and small fixes, not multi-file changes.

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Build a precise, step-by-step action plan before making any code changes. Use this skill whenever the user describes a feature, bug fix, refactor, or any multi-file change. Also use when the user says "plan", "think through", "what would it take", or describes a problem without jumping to code. TRIGGER BEFORE writing any code for non-trivial changes. Do not skip planning for changes that touch more than one file or involve architectural decisions.

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Explore a codebase to find opportunities for architectural improvement, focusing on making the codebase more testable by deepening shallow modules. Use when user wants to improve architecture, find refactoring opportunities, consolidate tightly-coupled modules, or make a codebase more AI-navigable.

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Manages the Morphir knowledge base under kb/ — OKF bundles and concept documents. Use when adding content to a bundle, creating a new bundle, checking the knowledge base for conformance or provenance drift, building or querying its SQLite index, managing intent through its lifecycle, recording or reading architectural decisions as Decision Records, or navigating, searching and listing its bundles, concepts and links.

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Protocol for handling ambiguous decisions and missing/conflicting knowledge during code generation, design, and review. Ensures AI surfaces genuine judgment calls with structured options and stops on hallucination risk instead of silently assuming. Use when a decision has multiple valid approaches, when facts are missing or contradictory, when the user asks 'what should we do here?', 'is this a judgment call?', 'should I ask about this?', 'am I guessing here?', 'what are the tradeoffs?', or when deciding between two reasonable architectural or design options. Also composed by molecules to define how judgment calls and clarification requests are surfaced and resolved.

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Enforce architectural rules when generating or modifying code, and validate proposed designs before approval (design mode). Defaults to clean architecture; supports any architecture style via the architecture-refiner. Validates layer responsibilities, dependency direction, and structural constraints using the loaded architecture rules. Use when generating code, reviewing architecture, creating new files, or when the user mentions 'architecture', 'layers', 'structure', 'dependency rules', 'hexagonal architecture', 'ports and adapters', 'modular monolith', or 'onion architecture'. Also use when reviewing generated code for structural compliance.

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Production-grade TypeScript reference for React & Next.js frontend development. Covers type narrowing, component Props, generic hooks, discriminated unions, as const, satisfies, Zod validation, TanStack Query, server/client boundaries, forms, state management, performance, accessibility, debugging, and code review. Use when the user works with TypeScript in React or Next.js: type errors, Props design, generics, API typing, SSR/CSR boundaries, hydration issues, form validation, state management, performance, or code review. Also use for "how should I type this?", "why does this type error happen?", or any architectural decision involving TypeScript in a frontend context.

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AI code reviewer grounded in classic software engineering books for catching design smells, coupling issues, and architectural risks.

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Conducts deep technical research for Aha Loop stories. Use before implementing stories involving unfamiliar libraries or architectural decisions. Triggers on: research this, investigate, explore options, compare alternatives.

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Apply when facing a novel UI interaction or architectural decision with no precedent in the codebase. Build 2-3 competing prototypes and compare side by side before committing.

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Read and apply a project-specific BUILDING_SPEC.md or DIMENSIONS.md file before generating any architectural elements. Use when: generating floor plans, 3D buildings, or any spatial design where the project has defined custom dimensions, room requirements, or design standards.

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The ultimate brand asset: a consistent, explorable universe generated by AI. This skill covers creating coherent brand worlds, consistent characters, architectural spaces, and visual universes that serve as the foundation for all marketing content. Traditional brand building creates guidelines. AI world building creates explorable realities. Characters with consistent personalities across thousands of images. Environments that feel like real places. Visual languages so coherent that any new asset feels like it belongs. This is where AI generation meets universe design. The practitioners of this skill don't just create images—they create the DNA that generates infinite consistent images. They build the source code for visual worlds. Use when "world building, brand world, universe, consistent character, character design, visual language, brand universe, style bible, world bible, coherent assets, character sheet, environment design, world-building, universe, character-design, consistency, visual-language, brand-world, style-guide" mentioned.

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Extracts and records architectural decisions from diffs, conversation context, and explicit choices. Supports automatic extraction from staged changes, deduplication against prior decisions, and persistent ADR-format logging with spec traceability. Use after a significant design or architecture choice, before committing a diff that embeds implicit decisions, or when the same question keeps resurfacing.

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Deep Context Builder Skill (Ultra-Granular Pure Context Mode) workflow skill. Use this skill when the user needs Enables ultra-granular, line-by-line code analysis to build deep architectural context before vulnerability or bug finding and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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。