2,951 件 ・ 関連度順
概要と使いどころ
Apply a visual direction — an archetype (high-end agency, editorial minimal, brutalist, soft-SaaS, dark-tech) or one of 138 named design systems (apple, linear-app, stripe, vercel, notion, material, shadcn, spotify, tesla…) — by resolving it into the token system. Use when the user wants a specific look/vibe/brand feel, or asks to make a design feel premium/expensive/non-generic.
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。
Map this token system to or from any external design system (Material Design 3, Apple HIG, Fluent, Carbon, Ant, shadcn/ui, Radix, Chakra, Mantine, Bootstrap…) — adopt their look, build on their stack, or migrate between systems. Use when the user mentions interop, migration, or a specific design-system/component-library bridge.
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 PostGIS spatial table design reference covering geometry types, coordinate systems, spatial indexing, and performance patterns for location-based 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。
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。
World-class Java and Spring Boot development skill for enterprise applications, microservices, and cloud-native systems. Expertise in Spring Framework, Spring Boot 3.x, Spring Cloud, JPA/Hibernate, and reactive programming with WebFlux. Includes project scaffolding, dependency management, security implementation, and performance 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。
Use when erlang's concurrency model including lightweight processes, message passing, process links and monitors, error handling patterns, selective receive, and building massively concurrent systems on the BEAM VM.
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 erlang distributed systems including node connectivity, distributed processes, global name registration, distributed supervision, network partitions, and building fault-tolerant multi-node applications on the BEAM VM.
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。
World-class data engineering skill for building scalable data pipelines, ETL/ELT systems, real-time streaming, and data infrastructure. Expertise in Python, SQL, Spark, Airflow, dbt, Kafka, Flink, Kinesis, and modern data stack. Includes data modeling, pipeline orchestration, data quality, streaming quality monitoring, and DataOps. Use when designing data architectures, building batch or streaming data pipelines, optimizing data workflows, or implementing data governance.
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。
Calculate PI/PID controller gains using Internal Model Control (IMC) tuning rules for first-order 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。
Generator economic dispatch and cost optimization for power systems. Use when minimizing generation costs, computing optimal generator setpoints, calculating operating margins, or working with generator cost functions.
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。
Hex grid spatial utilities for offset coordinate systems. Use when working with hexagonal grids, calculating distances, finding neighbors, or spatial queries on hex maps.
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。
DC power flow analysis for power systems. Use when computing power flows using DC approximation, building susceptance matrices, calculating line flows and loading percentages, or performing sensitivity analysis on transmission networks.
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。
Nonlinear optimization with CasADi and IPOPT solver. Use when building and solving NLP problems: defining symbolic variables, adding nonlinear constraints, setting solver options, handling multiple initializations, and extracting solutions. Covers power systems optimization patterns including per-unit scaling and complex number formulations.
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。
Implement email and push notification delivery systems — transactional email, notification templates, delivery tracking, and bounce handling.
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。
Implement reliable background job processing systems — queue management, retry policies, dead-letter handling, and distributed workers.
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。
Drive feature development using Outside-In TDD with Hexagonal Architecture. Design emerges through inline code, in-memory fakes, interface extraction, and deferred I/O. Use when building features, writing tests, or structuring backend services. Triggers on: TDD, outside-in, hexagonal, ports and adapters, emergent design, acceptance test, component test, walking skeleton, in-memory fakes, component, contract test, adapter, fast tests, sub-second feedback. Language-agnostic (Go, Rust, Python, TypeScript, Java, C#).
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。
CrewAI multi-agent orchestration setup for collaborative AI 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。
Microsoft AutoGen multi-agent configuration for conversational AI 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。
LangChain tool creation and integration utilities for agent 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。
Reference for querying the Atlas knowledge graph through its MCP tools — the SECONDARY enrichment/comparison layer that adds best-practice context to systems you have ALREADY scanned from your real sources (`az`, repos, dirs). Use when you need to look up nodes, edges, kinds, clusters, stats, or wiki pages in Atlas to compare against your real inventory. (atlas graph, query atlas, atlas mcp, search the graph, graph neighbors, atlas record, atlas kinds, enrichment layer)
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。
Creating algorithmic art using p5.js with seeded randomness and interactive parameter exploration. Use this when users request creating art using code, generative art, algorithmic art, flow fields, or particle systems. Create original algorithmic art rather than copying existing artists' work to avoid copyright violations.
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。
WooYun business logic vulnerability methodology — 22,132 real cases across 6 domains (authentication bypass, authorization bypass, payment tampering, information disclosure, logic flaws, misconfiguration) and 33 vulnerability classes. Use for ANY security testing, auditing, or code review of web apps, APIs, or business systems — even without explicit "security" keywords. Triggers: penetration testing, security audit, vulnerability, bug bounty, payment security, IDOR, password reset, weak credentials, unauthorized access, race condition, parameter tampering, code review, 渗透测试, 安全审计, 漏洞挖掘, 支付安全, 越权, 逻辑漏洞, 业务安全, SRC, 代码审计. Also triggers on implicit intent: "test this endpoint", "find bugs", "can I bypass this", "帮我测测这个接口", "这个参数能不能改", "帮我找bug".
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。
Understand the components, mechanics, and constraints of context in agent systems. Use when writing, editing, or optimizing commands, skills, or sub-agents prompts.
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 Code implementation and refactoring, architecturing or designing systems, process and workflow improvements, error handling and validation. Provide tehniquest to avoid over-engineering and apply iterative improvements.
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。