ipcalc-for-cloud
Calculates IP ranges, CIDR blocks, and subnet allocations for cloud networks (Azure, AWS, GCP, Oracle, AliCloud). Supports single VNet/VPC or hub-spoke topologies with automatic AZ distribution. Outputs as info...
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Calculates IP ranges, CIDR blocks, and subnet allocations for cloud networks (Azure, AWS, GCP, Oracle, AliCloud). Supports single VNet/VPC or hub-spoke topologies with automatic AZ distribution. Outputs as info...
Automatically invoked when reviewing game simulation logs. Retrieves saved game data by ID and formats it for analysis.
Automatically invoked when conducting reviews (code, specs, architecture, UX). Ensures systematic analysis and actionable findings with consistent format.
Automatically invoked when writing formal documentation (specs, ADRs, reviews, plans). Ensures consistent structure, appropriate detail level, and proper file location.
Automatically invoked when running tests, analyzing test results, or verifying code changes. Ensures tests are run appropriately and results are interpreted correctly.
Automatically invoked when creating visual diagrams (flowcharts, sequence diagrams, ERDs, state machines, user journeys). Ensures proper Mermaid syntax and diagram clarity.
Provides expert guidance for NautilusTrader algorithmic trading platform. Covers backtesting strategies, live trading deployment, event-driven architecture, and building trading systems.
Commit changes in a git workspace using the Conventional Commits specification. Use when the user asks to commit, stage, or save changes to git, or mentions "conventional commits". Analyzes staged/unstaged chan...
Analyzes algorithmic trading backtest results from Jupyter notebooks and generates summary reports. Use when the user wants to analyze or summarize backtest notebooks.
SQLModel ORM patterns for PostgreSQL database operations. Use when defining database models, creating queries, or managing database connections for the Todo backend.
REST API design principles and implementation patterns. Use when designing API endpoints, creating API clients, or implementing HTTP communication between frontend and backend.
Managed Docker operations using Docker AI (Gordon) or intelligent simulation. Use when creating, optimizing, or debugging container configurations.
PostgreSQL database patterns for Neon serverless database. Use when configuring database connections, writing queries, or optimizing PostgreSQL for the Todo backend.
Create and manage Helm charts for Kubernetes deployments. Use when packaging applications for deployment.
Tailwind CSS utility-first styling patterns. Use when styling components, building layouts, or customizing design for the Todo frontend.
FastAPI web framework patterns and best practices. Use when building REST APIs, adding endpoints, or configuring FastAPI application for the Todo backend.
Next.js 16+ App Router patterns and best practices. Use when building pages, components, or configuring Next.js application for the Todo frontend.
Intelligent Kubernetes operations using the kubectl-ai plugin. Use when generating, applying, or debugging Kubernetes resources via natural language.
Intelligent Kubernetes agent for analysis, optimization, and troubleshooting. Use when you need deep insights or reasoning about cluster state.
Better Auth authentication framework patterns for Next.js. Use when implementing user authentication, JWT tokens, session management, or OAuth integration for the Todo frontend.
JWT (JSON Web Token) patterns for authentication. Use when implementing token-based authentication, verifying tokens, or configuring JWT in Better Auth.
Create and use deployment blueprints for cloud-native applications. Use when defining deployment strategies and generating infrastructure-as-code from specs.
Manage security policies and access controls for Protect surveillance. Monitor access to recordings and system settings to ensure only authorized personnel have appropriate access.
Manage device adoption and onboarding, maintain device inventory, and monitor device configurations across your UniFi Protect infrastructure.
Le classement utilise les étoiles du dépôt GitHub indexé. Plusieurs skills peuvent partager ce nombre : ce n’est ni un nombre d’installations ni une note de qualité.