「system」の検索結果

Analyze images, websites, and Figma files to extract their design and generate a `design.md` with token system, component inventory, and reconstruction notes. Use this skill whenever the user wants to understand, document, replicate, or audit the design of something visual: a screenshot, a URL, a Figma link, a Pinterest reference, a mockup, a competitor's site, a component, a dashboard, a landing page. Also when they ask 'extract the design system from X', 'document the style of Y', 'analyze this visually', 'convert this image into tokens', 'help me replicate this design', 'what palette does this site use', 'how is this built'. Also for single elements: 'copy this navbar', 'recreate this illustration', 'give me a prompt to regenerate this graphic' — element mode outputs a focused element.md, with token-grounded image-model prompts when the element is visual art. If the user brings any visual source and wants to understand it at a design level — this skill should activate.

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。

Extract a design system from a screenshot (PNG/JPG/WEBP) or a live URL and produce a schema-compliant `design.v1.json` file plus a ready-to-use agent prompt for applying the design to any website/app. When run inside the sleek-ui repository, offers to add the extracted design to the public catalog as a PR — updating `public/designs/`, `src/data/designs/`, `src/data/designs.ts`, and generating a preview. Use when users ask to "extract a design from this screenshot", "clone this site's design", "reverse-engineer the design tokens", "reskin my app to look like {site}", "generate a sleek-ui design from this URL", or provide an image/URL and want a reusable design system.

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。

Open Analytics' design language, extracted from the shipped product: an ink-derived neutral system, squircle surface anatomy, one small spring vocabulary, pixel-matched skeletons, and a plain-spoken copy voice, with type-checked component recipes. Use this whenever you build or restyle a dashboard, SaaS app, analytics UI, settings screen, onboarding flow, or marketing/landing page that should feel calm, dense and native, and whenever the user mentions Open Analytics' look, 'clean SaaS design', cards, dropdowns, tab bars, empty states, skeletons, or UI animation quality, even if they never say the words 'design system'.

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。

Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.

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 product philosophy and leadership frameworks of Steve Jobs, co-founder and CEO of Apple Inc. Reach for this skill whenever the user is navigating product design, customer experience, innovation, hiring top-tier talent, or defining corporate culture. Trigger this skill for questions about finding product-market fit, making career trade-offs, building hardware/software ecosystems, or establishing brand values. Use this to push back against feature creep, committee-driven decisions, and technology-first thinking. Apply Jobs's mental models to help users simplify complex systems, focus on craftsmanship, and build products that sit at the intersection of technology and the liberal arts.

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 foundationalist reasoning and methodological doubt of René Descartes (17th-century French rationalist philosopher). Use this skill whenever the user is questioning fundamental assumptions, seeking absolute certainty in a decision, dismantling flawed systems, or trying to rebuild a project from first principles. Trigger this skill for topics involving epistemology, rationalism, radical doubt, systemic reconstruction, or separating objective truth from sensory illusion. Reach for this whenever a user needs to cut through probabilities and consensus to find an indubitable 'Archimedean point' to build upon.

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。

Use this skill when reasoning about deep tech commercialization, biomedical engineering, drug delivery, material design, or overcoming institutional skepticism. Robert Langer, MIT professor and pioneer of drug delivery systems, approaches problems through first-principles engineering, interdisciplinary convergence, and relentless perseverance against consensus. Trigger this skill whenever the user is evaluating high-risk/high-reward research, deciding between incremental vs. paradigm-shifting impact, spinning out academic discoveries into startups, or designing physical/biological systems from scratch. Apply his frameworks to navigate patenting, funding, and translating lab science into real-world impact.

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 of Zhong Lin Wang (nanotechnology pioneer, Georgia Tech) to problems involving energy harvesting, IoT power scaling, sensor networks, and fundamental physics applications. Reach for this skill whenever the user is discussing self-powered systems, scaling distributed hardware, overcoming battery bottlenecks, or translating fundamental scientific phenomena (like static electricity or mechanical strain) into novel engineering applications. It is highly relevant for hardware roadmapping, optoelectronics, piezotronics, and challenging established scientific assumptions (like classical Maxwell's equations) to model dynamic systems.

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。

Reach for this skill whenever you are discussing reinforcement learning, agentic AI systems, AI alignment, continual learning, or the philosophical limits of large language models. This skill channels the thinking of Richard S. Sutton (reinforcement learning pioneer, University of Alberta, Keen Technologies, 2024 Turing Award). Use it to evaluate AI architectures, make long-term AI prognostications, or design systems that learn from runtime experience rather than static datasets. Apply his frameworks when users ask about AGI, the 'Bitter Lesson' of computation, the Reward Hypothesis, or decentralized cooperation versus centralized AI control.

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 of David Silver, lead researcher on AlphaGo and AlphaZero at DeepMind, to problems of AI design, reinforcement learning, and open-ended discovery. Use this skill whenever you are designing AI systems, evaluating learning algorithms, balancing exploration vs. exploitation, choosing research problems, or discussing how to break past human performance ceilings. Reach for this whenever the user asks about self-play, Monte-Carlo Tree Search, tabula rasa learning, AGI, or moving from human-curated data to autonomous experience. It helps shift the focus from hardcoding human knowledge to building systems that learn for themselves.

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 strategic frameworks and mental models of Anne Wojcicki, co-founder and CEO of 23andMe. Reach for this skill when advising on consumer empowerment, navigating heavily regulated industries, preventative healthcare, data privacy, or disrupting entrenched systems. Use this when the user is dealing with misaligned industry incentives, facing regulatory hurdles, building a consumer-research flywheel, or managing a team through a post-launch 'trough of sorrow'. Apply her principles of 'speaking with data', treating users as partners, and pushing for decade-long systemic change from the outside.

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。

Suede-owned operations-assessment discipline that maps how work actually happens before anyone designs a system: floor-level interviews with the people who do the work, an inventory of every silo including spreadsheets and inboxes used as databases, friction quantified in the requester's own numbers, opportunities ranked by annual value against build complexity, and adoption watched for the thirty days after launch. Use when nobody can answer what to automate, when an operation needs auditing before a build, when tribal-knowledge and key-person risk has to surface, or when a shipped system is quietly being routed around. NOT FOR: deciding what to build and in what order once the map exists (use suede-ops-architecture); researching external customers rather than internal operators (use suede-customer-research); lead lifecycle, scoring, and CRM routing rules (use suede-revops); product instrumentation and dashboards (use suede-analytics); getting a new user to first value (use suede-onboarding).

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。

Suede-owned operations-architecture discipline that fixes the shape of a system before anyone builds it: entity schema first, exactly one write path per entity, every unit of work routed to a deterministic automation, an AI agent, or a human decision, every existing tool marked absorb, keep, or kill, and the build sequenced into phases whose completion is proved by a command. Use when scoping an automation or agent build, deciding whether a workflow needs a model at all, consolidating a sprawling tool stack, designing the data model under a workflow, choosing a migration path off an old system, or ordering a build so nothing lands on a moving foundation. NOT FOR: executing a planned multi-file repo change (use suede-graph-flo-xr); coordinating parallel builders with file ownership (use suede-agent-teams); lead lifecycle, scoring, and CRM routing rules (use suede-revops); proving agent output quality against an eval set (use suede-ai-eval); reviewing the finished diff (use suede-code-review).

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 effective excitation signals (step tests) for system identification and parameter estimation in control systems.

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。

Guidance for scaling systems from startup to enterprise scale. Use when planning for growth, diagnosing bottlenecks, or designing systems that need to handle 10x-1000x current load.

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。

Use when you derive, change or extend a design system - where tokens live per stack, the DTCG token tree, role-based names for both themes, base vs layer vs drift, the DESIGN.md section order, the inventory format, the contrast check and the lint every proposal returns

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。

Use when working in a Unity project that uses Entities (DOTS) or when a task needs thousands of simulated objects - when ECS is worth it, components/bakers/ISystem with IJobEntity and Burst, entity command buffers for structural changes, native memory rules, and testing systems in a test World.

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。

Use when writing Bevy (Rust ECS) game code - pin and respect the repo's minor version, plugins/systems/queries/resources, schedules with FixedUpdate and Time, states and run conditions, messages vs observers across versions, required components, testing systems with App::update and a manual time strategy, compile-time and allocation hygiene.

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。

Detect drift, plan updates, and apply changes to the Shokunin AI Ecosystem. Use this when user asks to update, fix, sync, or verify the ecosystem.

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。

Safe testing practices to prevent real-world side-effects. TRIGGER when user asks to "run tests", "test this", "write tests", "debug", "mock", "fixture", "integration test", "unit test", or when testing code that touches network, database, filesystem, or external services. Use BEFORE executing any test that could affect production systems.

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。

Comprehensive brand messaging, positioning, and value proposition development using proven frameworks including Peep Laja Message Layers, Osterwalder Value Proposition Canvas, Geoffrey Moore positioning, April Dunford's Five Components, StoryBrand SB7, Andy Raskin Strategic Narrative, and Messaging House. Use when developing brand identity, brand messaging architecture, positioning statements, value propositions, messaging hierarchies, brand pillars, taglines, one-liners, elevator pitches, brand guidelines, visual identity systems, or creating Positioning & Messaging Packs. Triggers include: messaging framework, brand positioning, value prop, messaging architecture, brand pillars, brand identity, StoryBrand, positioning statement, brand guidelines, design system, messaging house, corporate identity, brand voice, visual standards.

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。

Front-door router for the product-shots ecosystem. Clarifies underspecified visual creation requests through a 4-stage state machine, injects platform visual DNA (Amazon + 7 social platforms) and industry visual DNA (7 industries), enforces a unified Negative Constraints prompt patch, then routes to one of five downstream business skills (product-shots-main-image / product-shots-detail-page / product-shots-multi-angle / product-shots-ad-creative / product-shots-social-post). Use when the user says "I need a product image", "design something for my listing", "I need content for Instagram", "make an ad for me", "make a cover image", "create a post", "做一个商品图", "帮我做个详情页", "帮我做个广告图", "做一个社媒图", "做一张图", "帮我设计", "做个封面" — i.e. any underspecified visual creation request that needs clarification before generation. This is the intent-routing hub of the product-shots ecosystem.

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。

Analyze images, websites, and Figma files to extract their design and generate a `design.md` with token system, component inventory, and reconstruction notes. Use this skill whenever the user wants to understand, document, replicate, or audit the design of something visual: a screenshot, a URL, a Figma link, a Pinterest reference, a mockup, a competitor's site, a component, a dashboard, a landing page. Also when they ask 'extract the design system from X', 'document the style of Y', 'analyze this visually', 'convert this image into tokens', 'help me replicate this design', 'what palette does this site use', 'how is this built'. Also for single elements: 'copy this navbar', 'recreate this illustration', 'give me a prompt to regenerate this graphic' — element mode outputs a focused element.md, with token-grounded image-model prompts when the element is visual art. If the user brings any visual source and wants to understand it at a design level — this skill should activate.

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。

Designs and audits UI/UX systems with usability and accessibility requirements. Use when shaping flows, design systems, interaction patterns, or WCAG-aware product behavior.

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。