「machine design」の検索結果

Design, critique, read, write, render, and implement UML and UML-like software diagrams. Use when the user mentions UML, sequence diagrams, class diagrams, activity diagrams, state machines, use case diagrams, component diagrams, deployment diagrams, object diagrams, package diagrams, profile diagrams, timing diagrams, communication diagrams, interaction overview diagrams, composite structure diagrams, ERDs, database schema diagrams, C4, BPMN, swimlanes, flowcharts, network diagrams, application architecture diagrams, software architecture diagrams, diagram-as-code, model-as-code, XMI, UMLDI, PlantUML, Mermaid, Graphviz DOT, D2, Structurizr, DBML, diagrams.net/draw.io, Kroki, or interactive diagram editors and explorers.

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Write an implementation plan as one interactive HTML page - a tree of claims (why › what › how › where), each proved by one exhibit (UI mockup, state machine, call stack, schema or code), with the decisions the user has to make placed on the claim they change. Use when the user types /html-plan, or asks for a plan, RFC or design before building something that touches more than a couple of files.

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 text-driven visuals in Markdown: charts, diagrams, cards, architecture and page layouts. Charts and analytical views (bar, line, pie, heatmap, correlation, regression); reliability and operations (latency, incident, throughput, cycle time, OKR, standup, on-call); product and finance (funnel, retention, revenue, budget); process and workflow (approval, BPMN); software design and behaviour (class, state machine, sequence, C4); dependencies and impact (dependency graph, ER, causality); system architecture as HTML (layer stack, wings, zones, topology, service catalogue, request paths); infrastructure (cloud, Kubernetes, ETL, network, security, IAM, compliance); governance and people (ArchiMate, org chart, hiring); knowledge and planning (mind map, roadmap, Gantt, migration); documents (comparison, SWOT, memo, policy, catalogue, case study). Use when a document needs a diagram, chart or card. Figures render live. Not for slide decks or math notation. Not recommended: mermaid / canvas / drawio / dot.

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Model component behaviour as explicit states, events, and transitions. Use when a component has many interacting states that must be exhaustive. For the feel and feedback of a single interaction, use `micro-interaction-spec`.

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Specify one micro-interaction completely — trigger, rules, feedback, loops, and modes. Use when handing a single interaction to engineering. For motion craft alone use `animation-principles`; for multi-state components use `state-machine`.

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。

Provisions, scales, and operates Amazon EC2 virtual-machine workloads: instance-type selection (Graviton/Arm64, burstable T credits, GPU, instance store vs EBS), launch templates, Auto Scaling groups (scaling policies, instance refresh, mixed instances, Spot, warm pools, lifecycle hooks), IMDSv2, placement groups, Elastic IPs, AMI lifecycle, and Systems Manager fleet operations (Session Manager, Run Command, Patch Manager). Applies to EC2 instance and fleet questions, InsufficientInstanceCapacity, CPU-credit/surplus charges, IMDSv2 401s, instances stuck in Pending:Wait, ASG not replacing unhealthy instances, status-check failures, SSH refused/timed out, or instances missing as SSM managed nodes. For a single secure instance launch, the launching-ec2-instance-with-best-practices skill is more appropriate; for instance profiles, see setting-up-ec2-instance-profiles; for Image Builder, see amazon-ec2-image-builder. Does NOT cover Lambda, ECS/Fargate, EKS, VPC/ALB/NLB design, or IAM policy authoring.

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Build a scalable outbound B2B sales machine with specialized roles (SDR, AE, CSM). Use when the user mentions "outbound sales", "Cold Calling 2.0", "cold email sequences", "sales pipeline", "SDR process", "sales development", "build an outbound sales team", or "fill my pipeline". Also trigger when setting up a B2B SaaS sales team from scratch or building a lead-qualification framework to improve close rates. Covers the three lead types (seeds/nets/spears), role specialization, the referral-email method, ANUM qualification, and pipeline math. For offer design, see hundred-million-offers. For persuasion science, see influence-psychology.

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Generates complete conventional non-oncology diagnostic machine-learning research designs from a user-provided disease context, optional mechanism theme, and validation direction. Use when a study centers on disease-vs-control transcriptome comparison, optional mechanism-gene restriction, feature shrinkage, diagnostic model construction, ROC / calibration / DCA evaluation, interpretation layers, and orthogonal validation. Covers five study patterns and always outputs Lite / Standard / Advanced / Publication+ with a recommended primary plan, stepwise workflow, figure plan, validation hierarchy, minimal executable version, publication upgrade path, and strictly verified literature retrieval.

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。

Design and implement a complete ML pipeline for: $ARGUMENTS

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Organic story anatomy from John Truby's The Anatomy of Story (有机结构/设计原则/22步骤/道德议题/四角对立/幽灵/场景编排) — the designing principle (story process plus original execution), the seven key steps as the organic spine, all twenty-two steps with the middle-act machinery the beat sheets lack (ghost, fake-ally opponent, plan, opponent's drive, ally's attack, fake defeat, three revelation-and-decision pairs, audience revelation, visit to death, moral decision), four-corner opposition, the moral-argument chain with the hero-opponent power-balance rule, scene weave with structure-step tags and storyline numbers, and the accordion rule (never fewer than seven steps). Kept side by side with the Field/Snyder page maps. Use when the middle act sags into unconnected episodes, when the premise works but the story feels mechanical, when one antagonist cannot carry the theme, when the theme stays stated instead of acted, or when a BS2 board needs an organic second pass.

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。

AWS Step Functions workflow orchestration with state machines. Use when designing workflows, implementing error handling, configuring parallel execution, integrating with AWS services, or debugging executions.

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。

Design and run risk-targeted robustness, sensitivity, error, and baseline checks for an approved mathematical model, emitting compact machine evidence in lean mode and a final report in submission mode.

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。

Expert patterns for combat systems including hitbox/hurtbox architecture, damage calculation (DamageData class), health components, combat state machines, combo systems, ability cooldowns, and damage popups. Use for action games, RPGs, or fighting games. Trigger keywords: Hitbox, Hurtbox, DamageData, HealthComponent, combat_state, combo_system, ability_cooldown, invincibility_frames, damage_popup.

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。

Expert patterns for 2D animation in Godot using AnimatedSprite2D and skeletal cutout rigs. Use when implementing sprite frame animations, procedural animation (squash/stretch), cutout bone hierarchies, or frame-perfect timing systems. Trigger keywords: AnimatedSprite2D, SpriteFrames, animation_finished, animation_looped, frame_changed, frame_progress, set_frame_and_progress, cutout animation, skeletal 2D, Bone2D, procedural animation, animation state machine, advance(0).

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。

Bevy ECS skill for app states and per-entity state data. Keywords: Bevy, States, NextState, OnEnter.

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 branded architecture, architecture delta, IT current-state, flowchart, sequence, state machine, ER/data model, timeline, swimlane, quadrant, radar/spider, polar chart (polar/radial lollipop), loop/flywheel, nested, tree, org chart, layer stack, exploded axonometric, axonometric plan, Venn, pyramid/funnel, treemap and marimekko, heatmap, bar and dumbbell, waterfall, line (slopegraph, ridgeline, streamgraph, bump), Gantt and scatter charts (bubble, beeswarm), high-level, process, medallion, data flow, DP integration, DP security matrix, Sankey, fishbone, Wardley map, kanban, user journey, deployment, dependency graph, UML class, story map, or database schema diagrams as HTML/SVG/PNG, with .drawio, Mermaid, and .excalidraw import, plus lifecycle phase maps, block decomposition trees, and onboarding guidance.

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。

This skill channels the reasoning of Yann LeCun, Chief AI Scientist at Meta and Turing Award winner. Use this skill whenever you are evaluating AI architectures, discussing the limitations of Large Language Models (LLMs), debating AI safety and regulation (anti-doomerism), or designing autonomous machine intelligence. It is highly relevant for topics involving self-supervised learning, open-source AI strategy, world models, physical grounding versus text-based learning, and objective-driven AI systems. Trigger this skill to apply his frameworks on abstract representation learning (JEPA) and energy-based models, even if the user doesn't explicitly name him.

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Use this skill when reasoning about generative AI, adversarial machine learning, neural network security, algorithmic fairness, or deep learning fundamentals. This skill channels the thinking of Ian Goodfellow, inventor of Generative Adversarial Networks (GANs). Trigger this skill when the user asks about model robustness, mitigating bias, evaluating AI guardrails, designing generative models, or defending against adversarial attacks. Apply his frameworks of minimax games, adversarial feature learning, and worst-case robustness analysis to shift the user's perspective from average-case optimization to adversarial resilience.

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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.

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。

Design, implement, and validate reproducible machine-learning pipelines spanning data preparation, training, evaluation, registry, and deployment gates. Use when the user requests an ML pipeline, needs to turn model scripts into an orchestrated workflow, or provides pipeline components that must be connected safely.

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。

Think and work like an expert Fluid Mechanics Engineer. Use when a task calls for Fluid Mechanics Engineer judgment. Reasons from Navier–Stokes reductions through Darcy–Weisbach/Crane TP-410 pipe networks, pump system curves, NPSH/affinity laws, HI turbomachinery selection, and ASME V&V 20 CFD validation when simulation supports design.

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。

UE (user experience engineering) skill for defining end-to-end user experience behavior and interaction logic. Use for tasks like flow design, interaction models, state machines, error recovery, input validation, usability heuristics, accessibility, and turning UX intent into testable acceptance criteria for implementation.

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。

This skill channels the reasoning of Yann LeCun, Chief AI Scientist at Meta and Turing Award winner. Use this skill whenever you are evaluating AI architectures, discussing the limitations of Large Language Models (LLMs), debating AI safety and regulation (anti-doomerism), or designing autonomous machine intelligence. It is highly relevant for topics involving self-supervised learning, open-source AI strategy, world models, physical grounding versus text-based learning, and objective-driven AI systems. Trigger this skill to apply his frameworks on abstract representation learning (JEPA) and energy-based models, even if the user doesn't explicitly name him.

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。

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.

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。