「nct」の検索結果

Expert knowledge for Azure App Service development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when configuring App Service plans/slots, VNet integration, managed identity/Key Vault, CI/CD deployments, or diagnostics, and other Azure App Service related development tasks. Not for Azure Functions (use azure-functions), Azure Static Web Apps (use azure-static-web-apps), Azure Spring Apps (use azure-spring-apps), Azure Container Apps (use azure-container-apps).

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Expert knowledge for Azure Automation development including troubleshooting, best practices, decision making, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when building runbooks, DSC configs, Hybrid Runbook Workers, managed identities, or Automation deployments, and other Azure Automation related development tasks. Not for Azure Functions (use azure-functions), Azure Logic Apps (use azure-logic-apps), Azure DevOps (use azure-devops), Azure DevTest Labs (use azure-devtest-labs).

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 API Management development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when configuring APIM gateways, policies, VNets, self-hosted gateways, or AI/LLM-backed APIs, and other Azure API Management related development tasks. Not for Azure App Service (use azure-app-service), Azure Functions (use azure-functions), Azure Logic Apps (use azure-logic-apps), Azure Service Bus (use azure-service-bus).

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 Batch development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when configuring Batch pools, autoscale, private networking, CMK encryption, or CI/CD for HPC/render jobs, and other Azure Batch related development tasks. Not for Azure Container Instances (use azure-container-instances), Azure Kubernetes Service (AKS) (use azure-kubernetes-service), Azure Functions (use azure-functions), Azure Virtual Machines (use azure-virtual-machines).

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 Connector Namespace development including configuration. Use when setting up hosted MCP servers, defining Connector Namespace endpoints, auth settings, or runtime options, and other Azure Connector Namespace related development tasks. Not for Azure Service Connector (use azure-service-connector), Azure API Management (use azure-api-management), Azure Logic Apps (use azure-logic-apps), Azure Functions (use azure-functions).

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在Python 2.7环境下解析Windows目录链接（Junction）或符号链接的真实物理路径。提供基于PyWin32 API和PyQt4/5 QProcess调用cmd命令的两种解决方案，解决os.path.realpath失效及输出解析问题。

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Build modern React Native components using function components and composition. Use when building or refactoring React Native function components and composable UI.

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Core Kotlin patterns for scope functions, backing properties, and read-only collection interfaces. Use for let/apply/run/also/with choices or encapsulating mutable state; defer general language constructs, extension functions, coroutines/runCatching, Android/Room recipes, and testing/tooling setup.

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Standardize SRS and FRS specifications for technical behavior, interfaces, data contracts, quality constraints, and verification mapping. Use when writing SRS, functional specification, system behavior requirements, API/data contracts, or non-functional thresholds.

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Enforce SOLID principles, guard-clause style, function size limits, and intention-revealing naming across all languages. Use when refactoring for readability, applying clean-code patterns, reviewing naming conventions, or reducing function complexity.

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SKILL.md simplification with functional integrity verification. Analyze redundancy, optimize content, check no functionality lost. Triggers on "simplify skill", "optimize skill", "skill-simplify".

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Instinct-based learning system that observes sessions via hooks, creates atomic instincts with confidence scoring, and evolves them into skills/commands/agents.

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Write, review, or modernize F# code in .NET repositories with functional-first design, algebraic data types, pattern matching, pipelines, async workflows, project ordering, and C# interop. USE FOR: F# .fs/.fsproj code; discriminated unions, records, options, results, pattern matching, computation expressions, or functional domain modeling; strongly typed AI-generated .NET code. DO NOT USE FOR: C# language modernization; FSI-only exploratory scripts with no project changes. INVOKES: inspect .fsproj file order and SDK settings, edit F# source and project files, and run dotnet build/test or dotnet fsi validation commands when changes are made.

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Build AI-enabled .NET applications with Semantic Kernel using services, plugins, prompts, and function-calling patterns that remain testable and maintainable. USE FOR: adding AI-driven prompts, plugins, or orchestration to a .NET app; reviewing kernel construction, service registration, or plugin usage; building function-calling. DO NOT USE FOR: unrelated stacks; generic tasks that do not need this specific guidance. INVOKES: inspect the repository context, edit targeted files, and run relevant build, test, lint, or validation commands when changes are made.

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Use when you need to add or evaluate Maven dependencies that improve code quality or domain modeling — including nullness annotations (JSpecify), static analysis (Error Prone + NullAway), functional programming (VAVR), architecture testing (ArchUnit), or money and currency support (JavaMoney) — and want a consultative, question-driven approach that adds only what you actually need. This should trigger for requests such as Add Maven dependencies; Add JSpecify nullness dependencies; Add Error Prone NullAway dependencies; Add VAVR functional dependencies; Add ArchUnit architecture testing dependencies; Add JavaMoney dependencies. Part of Plinth Toolkit

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This skill should be used when working with Firebase projects, including initializing projects, adding Cloud Functions or Firestore collections, debugging emulator issues, or reviewing Firebase code. Triggers on "firebase", "firestore", "cloud functions", "emulator", "firebase auth", "deploy to firebase", "firestore rules".

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Integration skill for Lovable.dev projects. Activates when working with: - Lovable.dev projects with GitHub sync - Supabase Edge Functions that need deployment - Database migrations for Lovable Cloud - Projects with supabase/ directory structure - Any mention of "Lovable", "deploy edge function", "apply migration" Provides exact Lovable prompts for backend operations that can't be done via GitHub alone.

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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.

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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.

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C-optimized technical analysis with 150+ functions and 61 candlestick pattern recognition functions via TA-Lib

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Ten minute security pass over a Convex backend: public functions that should be internal, missing auth checks, unvalidated args, IDs from the client trusted without ownership checks, secrets in code. Use before merging a pull request, after adding new public functions, or when the user says 'quick security check'.

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Routes Convex work to the right builder skill and covers the handful of rules that apply everywhere: validators on every function, withIndex over filter, idempotent mutations, internal functions for scheduling. Use when a task mentions Convex and no more specific skill matches, or as the first stop before picking one.

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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.

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Comprehensive toolkit for protein language models including ESM3 (generative multimodal protein design across sequence, structure, and function) and ESM C (efficient protein embeddings and representations). Use this skill when working with protein sequences, structures, or function prediction; designing novel proteins; generating protein embeddings; performing inverse folding; or conducting protein engineering tasks. Supports both local model usage and cloud-based Forge API for scalable inference.

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。