「implementation」の検索結果

Build secure, scalable authentication and authorization systems using industry-standard patterns and modern best practices.

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Expert patterns for Algolia search implementation, indexing strategies, React InstantSearch, and relevance tuning

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Use when a coding task should be driven end-to-end from issue intake through implementation, review, deployment, and acceptance verification with minimal human re-intervention.

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AI and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features.

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Use this skill for internal architecture work around config-driven, registry-driven, and pattern-driven feature design in the nuxt-ui-tools repository. Covers how to organize variant-heavy features, where to put implementations, and how to keep both internals and public APIs standardized.

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Archive a completed change in the experimental workflow. Use when the user wants to finalize and archive a change after implementation is complete.

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Fast-forward through OpenSpec artifact creation. Use when the user wants to quickly create all artifacts needed for implementation without stepping through each one individually.

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Implement tasks from an OpenSpec change. Use when the user wants to start implementing, continue implementation, or work through tasks.

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Use when starting feature work that needs isolation from current workspace or before executing implementation plans - creates isolated git worktrees with smart directory selection and safety verification

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Use when executing implementation plans with independent tasks in the current session

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Use when implementing any feature or bugfix, before writing implementation code

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Use when receiving code review feedback, before implementing suggestions, especially if feedback seems unclear or technically questionable - requires technical rigor and verification, not performative agreement or blind implementation

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Use when implementation is complete, all tests pass, and you need to decide how to integrate the work - guides completion of development work by presenting structured options for merge, PR, or cleanup

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You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.

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Use when you have a written implementation plan to execute in a separate session with review checkpoints

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Master authentication and authorization patterns including JWT, OAuth2, session management, and RBAC to build secure, scalable access control systems. Use when implementing auth systems, securing APIs, or debugging security issues.

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Evaluate strategic options by NPV vs ease of implementation. Use for project prioritization, resource allocation, and strategic decision making.

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Breaks down trading ideas into component parts for systematic Pine Script implementation. Use when analyzing trading concepts, decomposing strategies, planning indicator features, or extracting ideas from YouTube videos. Triggers on conceptual questions, "how would I build", YouTube URLs, or video analysis requests.

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Use when the user asks to "optimize entity presence", "build knowledge graph", "improve knowledge panel", "entity audit", "establish brand entity", "Google does not know my brand", "no knowledge panel", or "establish my brand as an entity". Works standalone with public search and AI query testing; supercharged when you connect ~~knowledge graph + ~~SEO tool + ~~AI monitor for automated entity analysis. For structured data implementation, see schema-markup-generator. For content-level AI optimization, see geo-content-optimizer.

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Maxim UI Styling Intelligence — shadcn/ui components, Tailwind CSS, CSS architecture, responsive design, dark mode, accessibility-compliant styling (WCAG 2.1 AA), animation and motion design, theming systems. Activate for component building, layout implementation, theme customization, dark mode, CSS architecture decisions, UI polish. Powered by Maxim behavioral science + Fitts' Law + Color Psychology.

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Structure AI work artifacts under .aiwork/{YYYY-MM-DD}_{slug}/ folders using the project's protocol (intent, triage, research, spec, prd, areas, plan, review, notes, docs/). Use when the user asks to plan, capture an intent, make a spec, triage, research a task, write a report, code review, summarize findings, save the work, document decisions, record this, write that down, persist findings, or references any path under .aiwork/. Also use when starting a non-trivial task that needs a plan or spec before implementation, or when finishing one and capturing notes.

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Comprehensive technology-agnostic prompt generator for documenting end-to-end application workflows. Automatically detects project architecture patterns, technology stacks, and data flow patterns to generate detailed implementation blueprints covering entry points, service layers, data access, error handling, and testing approaches across multiple technologies including .NET, Java/Spring, React, and microservices architectures.

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Generates comprehensive, workable unit tests for any programming language using a multi-agent pipeline. Use when asked to generate tests, write unit tests, improve test coverage, add test coverage, create test files, or test a codebase. Supports C#, TypeScript, JavaScript, Python, Go, Rust, Java, and more. Orchestrates research, planning, and implementation phases to produce tests that compile, pass, and follow project conventions.

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Generate a complete MCP server implementation optimized for Copilot Studio integration with proper schema constraints and streamable HTTP support

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。