9,864 件 ・ 関連度順
概要と使いどころ
Align protein structures using Foldseek 3Di, TM-align, US-align, DALI, or Foldmason for structural MSA. Predict, score, and superpose backbone coordinates when sequence identity is below the twilight zone or remote-homology detection is required. Use when sequence MSA fails (<25% identity), when the dark proteome is the target, when AlphaFoldDB / ESM Atlas search is needed, or when structural superposition is the goal.
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。
Analyzes alternative splicing from PacBio Iso-Seq (HiFi, Kinnex/MAS-Iso-seq) and Oxford Nanopore (direct cDNA, direct RNA, R10.4.1+) long-read RNA-seq with full-isoform resolution. Tools include FLAIR (correct/collapse/quantify/diffSplice for PacBio + ONT), IsoQuant (de-novo or annotation-guided isoform discovery 2024 SOTA), Bambu (annotation-aware Bayesian discovery + quantification with Novel Discovery Rate), SQANTI3/SQANTI-LR (isoform classification: FSM/ISM/NIC/NNC + artifact flags), rMATS-long (event calling on long-read isoforms), and minimap2 (-ax splice:hq for HiFi; -ax splice -k14 for ONT cDNA; add -uf only for direct RNA or stranded cDNA preps). Solves microexon detection, recursive splicing, complex multi-exon isoforms, and DTU without transcript-quantification uncertainty. Use when short-read AS limitations (anchor length, complex isoforms, microexons, recursive splicing, transcript ambiguity) demand full-isoform resolution.
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。
The GitHub Actions cost rules ($100/week cap). Use when editing .github/workflows, adding a schedule or cron, dispatching a workflow, publishing a package version, hitting a billing block, or seeing a CI SPEND OFF TRACK line, ci-breaker issue or spend alert.
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。
Provides fault tolerance patterns for Spring Boot 3.x using Resilience4j. Use when implementing circuit breakers, handling service failures, adding retry logic with exponential backoff, configuring rate limiters, or protecting services from cascading failures. Generates circuit breaker, retry, rate limiter, bulkhead, time limiter, and fallback implementations. Validates resilience configurations through Actuator endpoints.
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。
Performs simple mutation testing to verify test coverage and assertions.
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。
Provides Spring Data Neo4j integration patterns for Spring Boot applications. Use when you need to work with a graph database, Neo4j nodes and relationships, Cypher queries, or Spring Data Neo4j. Creates node entities with @Node annotation, defines relationships with @Relationship, writes Cypher queries using @Query, configures imperative and reactive Neo4j repositories, implements graph traversal patterns, and sets up testing with embedded 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。
Provides patterns to generate comprehensive REST API documentation using SpringDoc OpenAPI 3.0 and Swagger UI in Spring Boot 3.x applications. Use when setting up API documentation, configuring Swagger UI, adding OpenAPI annotations, implementing security documentation, or enhancing REST endpoints with examples and schemas.
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。
Create or resume an opt-in Delivery Ledger for substantial authorized 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。
Run the advanced iterative workflow — Wayfinding AST discovery, Relentless Grilling, contract and spec design, TDD, council review, and docs.
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。
Provides integration patterns for LangChain4j with Spring Boot. Configures AI model beans, sets up chat memory with Spring context, integrates RAG pipelines with Spring Data, and handles auto-configuration, dependency injection, and Spring ecosystem integration. Use when embedding LangChain4j into Spring Boot applications, building Java LLM applications with @Bean configuration, or setting up Spring AI 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。
Refactor Spring Boot and Java code to improve maintainability, readability, and adherence to enterprise best practices. This skill transforms messy Spring Boot applications into clean, well-structured solutions following SOLID principles and Spring Boot 3.x conventions. It addresses fat controllers, improper transaction boundaries, field injection anti-patterns, and scattered configuration. Leverages Java 21+ features including record patterns, pattern matching for switch, virtual threads, and sequenced collections.
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。
Provides patterns for implementing Spring Boot caching: configures Redis/Caffeine/EhCache providers with TTL and eviction policies, applies @Cacheable/@CacheEvict/@CachePut annotations, validates cache hit/miss behavior, and exposes metrics via Actuator. Use when adding caching to Spring Boot services, configuring cache expiration, evicting stale data, or diagnosing cache misses.
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 building PHP applications with modern PHP 8.3+ features, Laravel, or Symfony frameworks. Invokes strict typing, PHPStan level 9, async patterns with Swoole, and PSR standards. Creates controllers, configures middleware, generates migrations, writes PHPUnit/Pest tests, defines typed DTOs and value objects, sets up dependency injection, and scaffolds REST/GraphQL APIs. Use when working with Eloquent, Doctrine, Composer, Psalm, ReactPHP, or any PHP API development.
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。
Debug Spring Boot issues systematically. Use when encountering bean errors like NoSuchBeanDefinitionException, circular dependency issues, application startup failures, JPA/Hibernate problems including LazyInitializationException and N+1 queries, security misconfigurations causing 403 Forbidden errors, property binding failures, CSRF token issues, or any Spring Boot application requiring diagnosis with Actuator endpoints and JVM debugging.
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 adding hypermedia links to REST responses, building self-describing APIs, or implementing Spring HATEOAS. Use when you see EntityModel, CollectionModel, or RepresentationModel in the project.
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 adding monitoring, metrics, logging, or tracing to Spring AI or LLM integration code. Covers token tracking, latency measurement, cost estimation, and prompt/response logging. Use when user mentions AI monitoring, token costs, or LLM observability.
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。
Add strategic color to features that are too monochromatic or lack visual interest, making interfaces more engaging and expressive. Use when the user mentions the design looking gray, dull, lacking warmth, needing more color, or wanting a more vibrant or expressive palette.
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。
Integrate and maintain Robelest Convex Auth in apps by always checking upstream before implementation. Use when adding auth setup, updating auth wiring, migrating between upstream patterns, or troubleshooting @robelest/convex-auth behavior across projects.
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。
Creates or adds Convex to an app. Use for new Convex projects, npm create convex@latest, frontend setup, env vars, or the first npx convex dev run.
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。
Plan and execute Convex schema migrations safely, including adding fields, creating tables, and data transformations. Use when schema changes affect existing data.
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。
Review a feature and enhance it with purposeful animations, micro-interactions, and motion effects that improve usability and delight. Use when the user mentions adding animation, transitions, micro-interactions, motion design, hover effects, or making the UI feel more alive.
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。
Improve interface resilience through better error handling, i18n support, text overflow handling, and edge case management. Makes interfaces robust and production-ready. Use when the user asks to harden, make production-ready, handle edge cases, add error states, or fix overflow and i18n issues.
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。
Drill-down on the shared MatrxDataTable, local or server, with per-Dimension levels. Use when adding group-by, breakdown or 'by X' analytics to a table, replacing hand-built totals or rollup tables, or when people want counts or sums by status, owner, model or day.
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。
Import a memory export from another AI assistant into Claude's memory — conversationally, additively, and with the content treated as data.
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。