「designing」の検索結果

Applies the reasoning, principles, and frameworks of Jürgen Schmidhuber (LSTM co-inventor and deep learning pioneer). Reach for this skill whenever tackling problems involving sequence learning, artificial curiosity, intrinsic motivation, reinforcement learning architectures, or predicting long-term technological and cosmic evolution. Use this when discussing AGI timelines, the history and attribution of AI breakthroughs, data compression as learning, or when designing autonomous agents that must set their own goals. Trigger this skill for topics like recurrent neural networks, algorithmic information theory, open-source AI democratization, and evaluating true existential risks versus media hype.

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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 style of Kaiming He, computer vision pioneer and creator of ResNet. Use this skill whenever you are designing deep learning architectures, debugging neural network optimization, formulating generative AI problems, or bridging AI with other scientific domains. Trigger this skill for discussions on network depth, weight initialization, residual learning, flow matching, or when reframing discriminative tasks as conditional generation. It emphasizes simplicity in complex visual problems, end-to-end optimization, and viewing AI as a universal language for science.

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 rigorous deontological ethics, transcendental idealism, and political philosophy of Immanuel Kant (18th-century German philosopher, Critique of Pure Reason). Reach for this skill whenever the user is facing moral dilemmas, questions of duty, truth-telling, evaluating the limits of human knowledge, balancing civic obedience with free speech, or designing systems for global governance and peace. Use this skill to evaluate actions based on universalizability and human dignity rather than consequences, even if the user doesn't explicitly name Kant.

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。

Suede-owned SMS and MMS marketing discipline. Use when evaluating SMS as a channel, designing consent-aware sequences, drafting messages, setting cadence, comparing number types or platforms, or defining measurement (TCPA consent, A2P 10DLC, quiet hours, cart-recovery and win-back texts. NOT FOR: email campaigns (use suede-emails), phone capture UX (use suede-site-alchemy), or legal certification) this skill does not give legal advice and never sends, schedules, imports, or registers.

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 programmatic SEO discipline. Use when designing data-backed keyword, location, directory, integration, or comparison pages at scale, including templates, index-worthiness gates, internal links, and rollout checks. NOT FOR: auditing an existing site (use suede-seo-audit), planning editorial content (use suede-content-strategy), or publishing bulk pages without approval.

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 signup conversion discipline. Use when auditing or redesigning account creation, registration, or trial-start flows, including field friction, progressive profiling, identity options, mobile behavior, abandonment, and measurement. NOT FOR: post-signup activation (use suede-onboarding), lead-capture forms (use suede-site-alchemy), or deploying auth and compliance changes without 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。

Suede-owned referral and affiliate program discipline. Use when designing refer-a-friend mechanics, ambassador or partner incentives, fraud controls, attribution, payout logic, or viral-loop measurement. NOT FOR: executing payouts or changing billing, launch-wide packaging (use suede-launch-packaging), lifecycle messaging (use suede-emails), or reporting unverified referral lift.

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 AI CI and branch-protection wiring for any repo and any stack: path-aware jobs, a single aggregator required check that cannot deadlock, lockfile hygiene, runtime pinning from the repo, and the exact branch-protection settings. Use when asked to set up CI, protect main, make CI block a bad merge, fix a required check that hangs pending forever, or repair duplicate or misfiring pipelines. Detects the repo's real apps, package managers, and runtime versions first; emits workflow files and settings, never pushes or flips protection itself. NOT FOR: reviewing or grading the change the gate is failing on (use suede-code); designing the AI eval cases to wire in (use suede-ai-eval); branch and worktree hygiene (a private Suede Labs companion, not in this pack).

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 retention discipline for voluntary and involuntary churn: cancel flows, pause paths, evidence-based save offers, failed-payment recovery, proactive signals, and win-back design. Use when diagnosing subscriber loss or designing a bounded retention intervention. NOT FOR: lifecycle-email production (use suede-emails), pricing architecture (use suede-pricing), paywall design (use suede-paywalls), or event instrumentation (use suede-analytics).

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。

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。

Deep expertise in ML/CV model selection, training pipelines, and inference architecture. Use when designing machine learning systems, computer vision pipelines, or AI-powered features.

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。

Formulate clear, testable hypotheses for product experiments. Use when designing experiments to validate solutions.

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, review, and refactor Neo4j graph data models. Use when choosing node labels vs relationship types vs properties, migrating relational/document schemas to graph, detecting anti-patterns (generic labels, supernodes, missing constraints), designing intermediate nodes for n-ary relationships, enforcing schema with constraints and indexes, or assessing an existing model against graph modeling best practices. Does NOT handle Cypher query authoring — use neo4j-cypher-skill. Does NOT handle Spring Data Neo4j entity mapping — use neo4j-spring-data-skill. Does NOT handle GraphQL type definitions — use neo4j-graphql-skill. Does NOT handle data import — use neo4j-import-skill.

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 designing or analysing an A/B test, computing power or sample size, testing whether a difference is real, or writing any statistical claim — effect sizes with intervals, SRM checks, multiple comparisons and causal caveats

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 designing or building any mobile screen or component a user sees (Flutter, SwiftUI or Compose) - platform conventions, hierarchy, type that scales, colour roles and dark mode, safe areas and insets, forms and keyboard, states, feedback, and the generic-mobile anti-patterns.

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Use when designing or building any screen, section or component that a user sees — visual hierarchy, typography, colour, spacing, states, forms and how to avoid the generic AI look.

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 RPC-style APIs with layered architecture (Controller → Manager → Repository). Use when creating new API endpoints, designing API contracts, or reviewing API patterns.

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UI/UX design intelligence with searchable style, palette, typography, and chart databases. Use when designing UI components, choosing colors/fonts, reviewing code for UX issues, building landing pages, or implementing responsive layouts.

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。

Spec CLI surface area before implementation - command tree, flags, exit codes, I/O contract. Use when designing or planning a CLI interface.

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 B2B demand generation and lead acquisition skill. Use when building lead generation systems, creating demand acquisition campaigns, optimizing CAC, designing lead magnets, conducting lead research, or planning multi-channel demand strategies. Covers ICP definition, funnel strategy, channel optimization, lead scoring, attribution, and pipeline generation for B2B SaaS.

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Growth strategy for product-led and loop-driven growth systems. Use when building growth strategy, designing growth loops, planning acquisition channels, evaluating network effects, deciding when to scale, or coordinating product, engineering, data, and marketing around growth. For SEO audits use seo-and-aeo-strategy; for page/form CRO use conversion-rate-optimization; for A/B test design use ab-test-setup.

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。