「designing」の検索結果

Use when reviewing, designing, or modifying Java enterprise systems from a maintainer-authored or maintainer-sanitized NIS2 engineering evidence inventory. Supports essential or important entities, critical-sector services, managed service providers, supply-chain dependencies, and cybersecurity incident escalation obligations without ingesting raw code, logs, runbooks, tickets, provider documents, or other operational free text. Part of Plinth Toolkit

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 reviewing, designing, or modifying Java enterprise systems that process personal data and need GDPR-aware engineering controls. This should trigger for requests such as Review a Java service for GDPR privacy controls; Design data-subject rights workflows; Add retention, deletion, pseudonymization, or privacy-safe logging; Assess data transfer, DPIA, breach evidence, or processor/controller boundary concerns before production release. Part of Plinth Toolkit

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 reviewing, designing, or modifying Java enterprise systems that may support financial entities, critical ICT services, third-party ICT provider integrations, or operational resilience obligations under DORA. This should trigger for requests such as Review a Java platform for DORA ICT risk controls; Design operational resilience evidence for a financial service; Add incident, continuity, backup, recovery, or third-party ICT controls; Assess resilience testing and monitoring before production release. Part of Plinth Toolkit

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 reviewing, designing, or modifying Java enterprise systems that may support intermediary services, hosting services, online platforms, marketplaces, content moderation, recommender systems, advertising delivery, complaint workflows, transparency reporting, or systemic-risk evidence under the EU Digital Services Act. This should trigger for requests such as Review a Java online platform for DSA controls; Design notice-and-action or appeal workflows; Add recommender, ad transparency, moderation, audit, researcher access, or privacy-safe observability evidence; Assess online-platform transparency controls before production release. Part of Plinth Toolkit

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 reviewing, designing, or modifying Java enterprise systems that use GenAI, LLMs, AI-assisted coding, RAG, AI agents, generated code, generated dependencies, prompt workflows, external model providers, or AI-enabled business logic and need ISO/IEC 42001 AI management system-aware engineering guidance. Part of Plinth Toolkit

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 reviewing, designing, or modifying Java enterprise products, services, libraries, agents, plugins, connected components, or platform modules that may qualify as products with digital elements and need EU Cyber Resilience Act secure-by-design, vulnerability handling, security update, SBOM, product documentation, or release-readiness controls. Part of Plinth Toolkit

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 reviewing, designing, or modifying Java enterprise systems that expose, exchange, store, process, export, or port data across users, businesses, connected products, cloud providers, APIs, event streams, AI data pipelines, data spaces, or SaaS platforms and need EU Data Act engineering controls. This should trigger for requests such as Review a Java platform for EU Data Act controls; Design data access and portability evidence; Add data-sharing request workflows, export formats, interoperability, metadata, audit logs, cloud-switching support, non-personal data safeguards, or trade-secret handoffs; Assess Data Act engineering readiness before production release. Part of Plinth Toolkit

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 need to review, improve, or refactor Java code for generics quality — including avoiding raw types, applying the PECS (Producer Extends Consumer Super) principle for wildcards, using bounded type parameters, designing effective generic methods, leveraging the diamond operator, understanding type erasure implications, handling generic inheritance correctly, preventing heap pollution with @SafeVarargs, and integrating generics with modern Java features like Records, sealed types, and pattern matching. This should trigger for requests such as Improve the code with Generics; Apply Generics; Refactor the code with Generics; Improve generic type safety in Java APIs; Fix raw types and unchecked casts in Java code. Part of Plinth Toolkit

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 need to review, improve, or refactor Java code for type design quality — including establishing clear type hierarchies, applying consistent naming conventions, eliminating primitive obsession with domain-specific value objects, leveraging generic type parameters, creating type-safe wrappers, designing fluent interfaces, ensuring precision-appropriate numeric types (BigDecimal for financial calculations), and improving type contrast through interfaces and method signature alignment. This should trigger for requests such as Review Java code for type design; Improve type design in Java code; Fix primitive obsession in Java code; Create value objects in Java code. Part of Plinth Toolkit

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 need to apply functional exception handling best practices in Java — including replacing exception overuse with Optional and VAVR Either types, designing error type hierarchies using sealed classes and enums, implementing monadic error composition pipelines, establishing functional control flow patterns, and reserving exceptions only for truly exceptional system-level failures. This should trigger for requests such as Improve the code with Functional Exception Handling; Apply Functional Exception Handling; Refactor the code with Functional Exception Handling; Model Java errors with Result or Either types; Replace exception-heavy flows with functional error handling. Part of Plinth Toolkit

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 need to apply data-oriented programming best practices in Java — including separating code (behavior) from data structures using records, designing immutable data with pure transformation functions, keeping data flat and denormalized with ID-based references, starting with generic data structures converting to specific types when needed, ensuring data integrity through pure validation functions, and creating flexible generic data access layers. This should trigger for requests such as Improve the code with Data-Oriented Programming; Apply Data-Oriented Programming; Refactor the code with Data-Oriented Programming; Model Java data with records and pure functions; Separate Java behavior from immutable data structures; Validate data integrity with pure Java functions. Part of Plinth Toolkit

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, implementing, reviewing, testing, or cleaning up feature toggles, feature flags, kill switches, runtime configuration gates, canary controls, staged rollouts, experiments, or temporary compatibility switches in Java enterprise systems. This should trigger for requests such as Design a feature toggle strategy; Review this feature flag; Add a kill switch safely; Test toggle on and off paths; Clean up an expired feature toggle; Plan controlled rollout and rollback behavior. Part of Plinth Toolkit

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。

SQL query optimization and database performance specialist. Use when optimizing slow queries, fixing N+1 problems, designing indexes, implementing caching, or improving database performance. Works with PostgreSQL, MySQL, and other databases.

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。

Cloud infrastructure design and deployment patterns for AWS, Azure, and GCP. Use when designing cloud architectures, implementing IaC with Terraform, optimizing costs, or setting up multi-region deployments.

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。

CLI command development specialist. Use when creating commands, designing argument parsing, automating tasks, or implementing CLI best practices. Specializes in command design patterns and user experience.

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。

Next.js architecture specialist. Use when designing Next.js applications, migrating to App Router, implementing Server Components, or optimizing Next.js performance. Covers App Router, Server Components, and best practices.

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。

Network architecture, troubleshooting, and infrastructure patterns. Use when designing network topologies, debugging connectivity issues, configuring load balancers, DNS, or implementing network security.

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 should be used when designing or implementing systems with multiple AI agents that coordinate to accomplish tasks. Triggers on "multi-agent", "orchestrator", "sub-agent", "coordination", "delegation", "parallel agents", "sequential pipeline", "fan-out", "map-reduce", "spawn agents", "agent hierarchy".

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。

Split a multi-call LM workflow by cognitive load, not by accuracy: let one strong model make the few reasoning decisions and a cheap model do the many mechanical executions (Aider architect+editor, DSPy optimizer-LM vs task-LM, vLLM speculative draft+target, LangGraph supervisor+worker are the same shape). Use when designing or cost-optimizing a pipeline that calls an LM many times, when deciding which steps need a strong reasoner vs a cheap executor, or when adding an escalation valve for when the cheap tier degrades. Search keywords: reduce LLM cost, cheaper model, lower token cost, model cascade, route to cheap model, strong model plus cheap model, LLM cost optimization.

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。

Decision rubric for when an LM agent should write-and-run code (Program-of-Thought / code interpreter) versus reason in natural language: classify each step as deterministic- computable (emit + execute code, feed the result back) vs judgment (stay in prose). Use when designing or debugging an agent step that does arithmetic/parsing/data transforms, when prose reasoning hallucinates a computation (under-coding), or when a sandbox round- trip is wasted on a judgment task (over-coding). Search keywords: code interpreter, agent does math wrong, calculator hallucination, when to run code vs reason, program of thought, PoT, tool vs reasoning.

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。

Pick an LM output format per (task x consumer x model) rather than by reflex: different formats carry different cognitive load (e.g. code-in-JSON makes the same model write worse code than plain-text+diff, while asking for prose when you need a typed object fails the other way). Use when designing or debugging an LM's output schema, choosing between plain text / diff / JSON / tool-call / grammar-constrained output, or when a model's quality drops after wrapping its output in a structured format.

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。

Email newsletter workflows. Use when creating newsletters, building subscriber lists, designing templates, or tracking engagement.

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 prompt engineering techniques to maximize LLM performance, reliability, and controllability in production. Use when optimizing prompts, improving LLM outputs, or designing production prompt templates.

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。

Database schema patterns for InsForge including social graphs, e-commerce, content publishing, and multi-tenancy with RLS policies. Use when designing data models with relationships, foreign keys, or Row Level Security.

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。