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概要と使いどころ
Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.
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 package performs differential pattern analysis for Ribo-seq data. It identifies genes with significantly different patterns in the ribosome footprint between two conditions. RiboDiPA contains five major components including bam file processing, P-site mapping, data binning, differential pattern analysis and footprint visualization.
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 dive into SwiftData design patterns and best practices. Covers schema design, query patterns, repository pattern, and performance optimization. Use when designing data models or improving SwiftData usage.
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。
Expert knowledge for Azure US Government development including decision making, architecture & design patterns, security, configuration, integrations & coding patterns, and deployment. Use when choosing Azure Gov vs global, meeting FedRAMP/DoD needs, using SACA patterns, Gov CI/CD, or Gov marketplace, and other Azure US Government related development tasks. Not for Azure Local (use azure-local), Azure Networking (use azure-networking), Azure Security (use azure-security), Azure Policy (use azure-policy).
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。
Spring Boot best practices and patterns. Use when creating controllers, services or repositories, or when the user asks about Spring Boot layering, wiring, configuration or exception handling. For JPA and Hibernate behaviour, use jpa-patterns instead.
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。
Data architecture patterns (warehouse, lake, lakehouse, mesh), ETL/ELT pipelines, streaming architectures, scaling strategies, and schema design patterns
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 architecture patterns, methodologies, quality frameworks, and evaluation methods for solution architects. Load when designing system architecture or selecting patterns.
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。
7 agentic design patterns with decision tree for choosing the right pattern for each agent type
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。
Build modern React component architecture with composition patterns. Use when designing reusable React components, applying composition patterns, or structuring component hierarchies.
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。
Claude Code session analysis, tool call patterns, efficiency metrics, and anti-pattern detection
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。
Static Application Security Testing patterns, OWASP Top 10 checklist, language-specific vulnerability patterns, Semgrep rule writing guide, and CI/CD integration. Use when scanning code for security vulnerabilities or writing custom SAST rules.
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。
Schema design, resolver patterns, DataLoader, N+1 prevention, and subscription patterns for GraphQL APIs.
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。
Lambda best practices, S3 event patterns, SQS/SNS fanout, and DynamoDB access patterns for serverless AWS architectures.
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。
API design and implementation across REST, GraphQL, gRPC, and tRPC patterns. Use when building backend services, public APIs, or service-to-service communication. Covers REST frameworks (FastAPI, Axum, Gin, Hono), GraphQL libraries (Strawberry, async-graphql, gqlgen, Pothos), gRPC (Tonic, Connect-Go), tRPC for TypeScript, pagination strategies (cursor-based, offset-based), rate limiting, caching, versioning, and OpenAPI documentation generation. Includes frontend integration patterns for forms, tables, dashboards, and ai-chat skills.
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。
When designing distributed systems for scalability, reliability, and consistency. Covers CAP/PACELC theorems, consistency models (strong, eventual, causal), replication patterns (leader-follower, multi-leader, leaderless), partitioning strategies (hash, range, geographic), transaction patterns (saga, event sourcing, CQRS), resilience patterns (circuit breaker, bulkhead), service discovery, and caching strategies for building fault-tolerant distributed architectures.
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。
Detects duplicate boilerplate, copy-paste tests, and structural maintainability issues across .NET test suites. Use when the user asks to reduce repetition, consolidate similar test methods, convert copy-paste tests to data-driven parameterized tests, suggest a better test structure, or identify refactoring opportunities. Identifies repeated construction, assertion patterns, copy-paste methods convertible to DataRow/Theory/TestCase, redundant setup/teardown, and shared infrastructure. Produces an analysis report with concrete before/after suggestions. Works with MSTest, xUnit, NUnit, and TUnit. DO NOT USE FOR: writing new tests (use writing-mstest-tests), reviewing test quality or anti-patterns (use test-anti-patterns), or deep mock auditing (use exp-mock-usage-analysis).
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 RxJS patterns for reactive programming in Angular. Use this skill when working with Observables, operators, subscriptions, async data flows, and error handling. Covers common patterns like combineLatest, switchMap, debounceTime, catchError, retry logic, and integration with Angular Signals using toSignal() and toObservable(). Ensures proper subscription cleanup with takeUntilDestroyed().
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。
Quick reference for common SQL patterns, CTEs, window functions, and indexing strategies. Triggers on: sql patterns, cte example, window functions, sql join, index strategy, pagination sql.
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。
Search code by AST structure using ast-grep. Find semantic patterns like function calls, imports, class definitions instead of text patterns. Triggers on: find all calls to X, search for pattern, refactor usages, find where function is used, structural search, ast-grep, sg.
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。
SQLAlchemy and database patterns for Python. Triggers on: sqlalchemy, database, orm, migration, alembic, async database, connection pool, repository pattern, unit of work.
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 ES6+ features including async/await, destructuring, spread operators, arrow functions, promises, modules, iterators, generators, and functional programming patterns for writing clean, efficient JavaScript code. Use when refactoring legacy code, implementing modern patterns, or optimizing JavaScript 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。
Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.
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 backend development guide for Langfuse's Next.js 14/tRPC/Express/TypeScript monorepo. Use when creating tRPC routers, public API endpoints, BullMQ queue processors, services, or working with tRPC procedures, Next.js API routes, Prisma database access, ClickHouse analytics queries, Redis queues, OpenTelemetry instrumentation, Zod v4 validation, env.mjs configuration, tenant isolation patterns, or async patterns. Covers layered architecture (tRPC procedures → services, queue processors → services), dual database system (PostgreSQL + ClickHouse), projectId filtering for multi-tenant isolation, traceException error handling, observability patterns, and testing strategies (Jest for web, vitest for worker).
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 persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.
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。