「architecture」の検索結果

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.

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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 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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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.

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Applies the reasoning style of Ilya Sutskever (deep learning pioneer, co-founder of OpenAI and Safe Superintelligence Inc.) to problems involving AI architecture, scaling laws, alignment, and research strategy. Reach for this skill whenever discussing machine learning paradigms, the limits of compute and data, AGI timelines, superintelligence safety, or deciding between hardcoding vs. learning. Trigger this skill for questions about next-word prediction, reinforcement learning efficiency, generalization gaps, and transitioning from brute-force scaling to fundamental research, even if the user doesn't explicitly name him.

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Applies the reasoning style of Geoffrey Hinton, deep learning pioneer and 2018 Turing Award winner. Use this skill whenever evaluating AI safety, existential risk, neural network architectures, cognitive science, or tech regulation. Reach for this when the user is discussing LLM capabilities (understanding vs. autocomplete), the biological vs. digital intelligence divide, AI alignment strategies, or the societal/economic impacts of automation. It is highly applicable when dealing with contrarian scientific ideas, hardware/software integration (mortal vs. immortal computing), or global cooperation on technological threats. Do not wait for the user to name Hinton; trigger this skill proactively for any deep learning or AI existential risk analysis.

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Resolve agent ownership and handoff design.

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Coordinate the Next.js foundation and ownership.

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Plan pages, navigation and content sequence.

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Suede-affiliated in-product monetization design for paywall screens, feature gates, trial-expiry states, usage-limit prompts, and free-to-paid upgrade moments. Use when the user needs trigger timing, message structure, plan presentation, or experiment design after users have experienced value. NOT FOR: public pricing pages (use suede-site-alchemy), tier architecture (use suede-pricing), or cancellation and save flows (use suede-churn-prevention).

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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).

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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).

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Suede-owned measurement discipline for tracking plans, event and conversion instrumentation, UTM and campaign-parameter hygiene, and verification of what actually fires. Use when setting up, auditing, or repairing analytics across web, product, paid, and lifecycle surfaces. NOT FOR: experiment design or significance decisions (use suede-ab-testing), campaign optimization (use suede-ads), attribution models, model comparison, or cross-tool reconciliation (use suede-attribution), or revenue-process architecture (use suede-revops).

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Suede-affiliated offer design for value framing, bonuses, guarantees, risk reversal, honest scarcity, naming, and payment structure. Use when the user is building or diagnosing a service, course, coaching, information-product, agency, or high-ticket B2B offer. NOT FOR: SaaS tier and value-metric architecture (use suede-pricing), sales-page copy (use suede-copy), or in-product upgrade prompts (use suede-paywalls).

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Suede-affiliated ethical application of behavioral science to marketing decisions, including framing, anchoring, social proof, loss aversion, choice architecture, and friction. Use when the user needs a named psychological model, an evidence-aware application, and a testable hypothesis. NOT FOR: clinical or mental-health advice, deceptive dark patterns, page implementation (use suede-site-alchemy), or pricing design (use suede-pricing).

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Suede AI full-stack surface builder that runs design, copy, and visual QA as one pass: landing pages, brand surfaces, product UI, dashboards, campaigns, launch pages, and reference-to-target restyles (suedify). Use when a build needs layout and words together, when a redesign or launch surface has to ship end to end, when the request is 'make this site look like that one', or when design, copy, asset, and QA lanes have to move at once. NOT FOR: a design-token, dark-mode, or single-component decision (use suede-design); copy with no layout work (use johnny-suede-write, or suede-copy for one standalone conversion surface); conversion and funnel architecture (use suede-site-alchemy); multi-agent orchestration protocols, WIP protection, and rollback trees (use suede-agent-teams).

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Suede AI design skill for making an interface feel intentional instead of templated: design tokens, color strategy, OKLCH ramps, type scale, fluid type, visual hierarchy, dark mode, spacing, component laws, motion, and source-to-implementation visual QA. Use when asked to design, restyle, polish, or audit a screen; pick colors or fonts; define, fix, or document design tokens; build or repair a dark mode; review a component or a whole design system; or compare a mock, Figma frame, or reference screenshot against a rendered implementation. NOT FOR: a full design-plus-copy build, launch surface, or reference-to-target restyle (use johnny-suede-design); conversion and funnel architecture (use suede-site-alchemy); writing the words themselves (use suede-copy).

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Suede Labs Android app factory: plan, build, test, and release a production-grade native Android app from a product idea through Google Play. Use for requests to create, ship, submit, monetize, or modernize an Android app. Covers Kotlin and Jetpack Compose architecture, API-level policy verification, accessibility, privacy and Data Safety, Play Billing, Play Integrity, account deletion, testing, performance, store assets, signing, staged rollout, and a release evidence gate. NOT FOR: iOS work (private Suede Labs companion, not in this pack: ios-app-factory), a review-only pass on an existing app (use suede-code-review), or live Play listing and keyword audits on a shipped app (use suede-aso).

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Structured planning mode for AI agents working on coding and development tasks. Produces a comprehensive plan BEFORE any code is written or changed, including task decomposition, architecture decisions, risk assessment, and approval gates. Use this skill whenever the user says "/plan", "plan mode", "plan first", "think before coding", "make a plan", or asks the agent to plan before executing. Also trigger when the user describes a complex multi-step development task and would clearly benefit from structured planning before implementation — especially for tasks involving new features, refactors, migrations, infrastructure changes, or multi-file edits. When in doubt, plan. A few minutes of planning prevents hours of rework.

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Fix bugs and broken behavior when there is enough evidence to act on a repair path. Use for errors, crashes, incorrect results, API failures (500, 404, 403), CORS problems, database exceptions, broken rendering, duplicated or wrong data, off-by-one mistakes, timezone/date bugs, broken forms, config-caused runtime failures, and regressions. Trigger when the user wants the bug repaired and the conversation already contains a clear failing area, a reproducible failing test, a concrete error path, or a prior diagnosis to implement. Do NOT use for new features, pure explanation, architecture discussion, broad research, or bug reports where the main need is figuring out why the behavior happens — use diagnose for that.

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Use when the user wants to save knowledge as a file so others don't have to rediscover it — "turn this into a doc", "write this up", "document how X works", "we figured this out and want to capture it", "nobody should have to figure this out again". Covers any request to create or update durable written artifacts: onboarding guides, runbooks, ADRs, API docs, architecture notes, postmortems, changelogs, setup guides. The trigger: user wants knowledge captured in a file for future reference, not just a conversation. Do NOT use when still making decisions (→ give-plan), just asking for explanation without a file (→ ask), or writing code (→ cook).

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Answer questions about code, architecture, and technical decisions — no implementation. Trigger on questions asking 'why', 'what does this do', 'what is the purpose of', 'explain', 'what's the difference', 'compare', or 'what are the tradeoffs' — even when referencing specific files, code snippets, or inline code. The key signal is the user wants to UNDERSTAND something, not change it. Do NOT trigger for requests to build, fix, plan, review, research, or add/modify code.

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Use this skill for any Python backend work in this project: building FastAPI endpoints, writing service functions, defining Pydantic/SQLModel schemas, running Alembic migrations, or debugging 422 errors. Essential for authentication and authorization patterns — setting up get_current_user, is_superuser checks, admin-only guards, role-based access, and dependency injection chains like Depends(). Also covers middleware, background tasks, async SQLAlchemy sessions, ORM relationship loading, and request/response design. Activate whenever the question involves Python API code, FastAPI patterns, or backend architecture in this codebase. Not for frontend, Docker, CI/CD, or infrastructure.

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Detect common technical and organizational anti-patterns in proposals, architectures, and plans. Use when strategic-cto-mentor needs to identify red flags before they become problems.

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