2,807 件 ・ 関連度順
概要と使いどころ
Rust programming expert including ownership, borrowing, lifetimes, async Tokio patterns, error handling, trait system, performance optimization, testing, and production systems 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。
Guided project onboarding for new codebases. Helps agents understand project structure, build systems, test commands, and development workflows by creating persistent knowledge memories.
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。
PHP expert including Laravel, WordPress, and Drupal 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。
iOS development expert including SwiftUI, UIKit, and Apple frameworks
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。
Frontend development expert including UI/UX patterns, responsive design, and accessibility
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。
Feature flag lifecycle management -- safe feature toggling, gradual rollouts, A/B testing patterns, flag cleanup strategies, and technical debt prevention. Covers LaunchDarkly, Unleash, OpenFeature, and custom implementations.
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。
Game development expert including DragonRuby, Unity, and game mechanics
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。
Specifies best practices and conventions for Expo-based mobile app 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。
Docker Compose container orchestration and management. Manage multi-container applications, services, networks, and volumes. Use for local development, testing, and orchestration of containerized 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。
Expert Electron desktop application development — main/renderer process architecture, IPC communication, native OS APIs (menus, tray, notifications, dialogs), auto-updates, code signing, packaging with electron-builder/forge, security hardening (contextIsolation, sandbox), and performance optimization. Use for building cross-platform desktop apps.
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 Android development expert covering Jetpack Compose, Kotlin coroutines/Flow, Architecture Components, Hilt DI, Navigation, testing, performance, Material Design 3, and modern Android patterns (MVI, Clean Architecture).
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。
Orchestrates local Kubernetes development environment management.
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 developing or extending EZ_math_model itself from a written implementation plan, especially adding algorithms, tool skills, templates, or multi-step repo changes.
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。
Execute implementation plan by dispatching fresh subagent for each task, with code review between tasks
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。
Write the test first, watch it fail, write minimal code to pass
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 a new feature with proposal, retrospective, and branch. Use when starting new development 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。
Configure and manage Supabase projects using MCP (Model Context Protocol). Use this skill when working with Supabase databases, setting up MCP servers, designing database schemas, implementing Row Level Security, managing migrations, or building modern data architectures with PostgreSQL. Essential for Supabase development, database design, and AI-powered database operations.
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 Claude Code in autonomous iteration loops until task completion — PRD-based feature development, test coverage improvement, lint fixing, documentation generation, dataset creation, or framework migrations. Uses in-session loop mechanism with specialized templates. Triggers on autonomous loop, iterate until done, run until passing, keep going until, ralph, PRD execution.
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。
Deploy, diff, bootstrap, inspect, roll back, or destroy AWS CDK applications with the community CDKD CLI while preserving safe production and state-management boundaries. Use when the user mentions cdkd or CDK Direct, asks for faster CDK deployments in development or test environments, or wants to evaluate whether an existing CDK app can use CDKD. Also use for CDKD import, export, state, events, and deployment troubleshooting. Prefer the upstream AWS CDK CLI for production and for existing CloudFormation-managed stacks unless the user explicitly approves a management migration.
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。
Assemble the human genetic evidence for and against a target before a programme commits to it — the evidence class that most improves the odds of surviving clinical development. Use this skill to pull gnomAD constraint metrics (LOEUF, pLI, observed/expected) that show whether loss of function is tolerated in people, retrieve GWAS Catalog associations and fine-mapped credible sets for a gene, and read a natural human knockout as a safety readout. Also trigger on gnomAD, LOEUF, pLI, loss-of-function intolerance, mutational constraint, GWAS Catalog, credible set, human knockout, genetic support, or target safety dossier.
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。
Register trained models in MLflow Model Registry with version control, implement stage transitions (Staging, Production, Archived) with approval workflows, and manage model lineage with comprehensive metadata and deployment tracking. Use when promoting a trained model from experimentation to production, managing multiple model versions across development stages, implementing approval workflows for governance, rolling back to previous versions, or auditing model changes for compliance.
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 web design for layout quality, typography, colour usage, spacing, responsive behaviour, brand consistency, and visual hierarchy. Covers design principles evaluation and improvement recommendations. Use when reviewing a design mockup before development, assessing an implemented site for design quality, providing feedback during a design review session, evaluating brand consistency, or checking responsive behaviour across breakpoints.
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。
Develop a gas chromatography method from scratch: define analytical objectives, select column chemistry, optimize temperature programming, choose carrier gas and detector, and validate initial system performance for target analytes in a given matrix.
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。
Develop a high-performance liquid chromatography method: define separation goals, select column chemistry and mobile phase, optimize gradient and flow conditions, choose the appropriate detector, and evaluate initial method performance for target analytes in solution or complex matrices.
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。