「layers」の検索結果

Game feel ("juice") for Apple apps — celebration choreography, haptic vocabulary design, sound-effect layers, and the event×channel feedback audit. Use when big moments fall flat, when designing haptics or SFX, or when auditing whether every meaningful event actually reaches the user.

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 scaffolding a project, refactoring into layers, creating view models/repositories, configuring dependency injection, or implementing unidirectional data flow (MVVM).

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。

JTBD methodology for extracting real jobs behind feature requests — job statements, abstraction layers, first-principles extraction, ODI outcome statements, and opportunity scoring

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 raw data exists — Meta, Google, TikTok, GA4, Shopify, a CRM export, or a spreadsheet — and has to become insight and decisions: descriptive, diagnostic, predictive, and prescriptive layers, cuts by channel, campaign, creative, audience, and time, cohorts, and a decision log. Trigger on 'analyze this data', 'read these numbers for me', 'what does this export say', 'pull insight from GA4', 'cohort analysis', 'here is the spreadsheet'. Also use when the user pastes a table and asks what it means. Not for — diagnosing ad root cause, see `03-performance-eval-global`; writing the report a stakeholder reads, see `07-marketing-report-global`; auditing account setup, see `21-ads-audit-global`.

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。

Compose an image prompt with subject, composition and style layers while labeling unverified generator parameters. Use for this Bot Generator Bot template; creates drafts or simulations within its declared scope.

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。

Structure Spring Boot 3+ projects with feature packaging and clean layering. Use when structuring Spring Boot 3 projects, defining layers, or applying architecture 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。

Configure Next.js cache layers, invalidation, and cache-component APIs. Use when choosing `fetch` caching, `use cache`, tags, or stale-data debugging in Next.js.

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。

Implement Domain-Driven Design with typed DTOs, repository interfaces, and single-responsibility Action classes in Laravel. Use when creating domain folders, binding repository contracts in providers, or passing DTOs between layers.

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。

Enforce core architectural standards for scalable Laravel applications. Use when structuring controllers, service layers, action classes, Form Requests, or Service Container bindings in Laravel 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。

Implement multi-level caching, invalidation patterns, and stampede protection in NestJS. Use when adding Redis caching layers, configuring cache-manager interceptors, implementing stale-while-revalidate, or preventing cache stampedes in NestJS services.

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。

Organize Flutter apps with modular feature-based clean architecture. Use when creating features under lib/features/ with domain, data, and presentation layers. Do not use for test-only, BlocBuilder, navigation, or spinner requests.

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。

Cross-cutting standards for error design, response shapes, error codes, and boundary placement across API, domain, and infrastructure layers. Use when defining error hierarchies, wrapping exceptions, building standardized error responses, or placing error boundaries in layered architectures.

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。

Audit structural debt, logic leakage, and monolithic components across Web, Mobile, and Backend codebases. Use when reviewing architecture, assessing tech debt, detecting logic in wrong layers, or identifying God classes.

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。

Apply Clean Architecture layering, modularization, and Unidirectional Data Flow in Android projects. Use when setting up project structure, placing code in layers, configuring feature/core modules, or implementing UDF patterns; defer Compose state and ViewModel/StateFlow implementation to their specific skills.

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Audits internal (dev/) and external (docs/) documentation completeness for a feature, subject, or set of existing docs, maps changes indicated by the user, across Infrahub's documentation layers, reports gaps, and optionally applies the fixes. TRIGGER when: the user wants to audit or check documentation coverage, find doc gaps after a feature branch, or verify docs are still current for a subject or specific files. DO NOT TRIGGER when: authoring new documentation from scratch → use the add-docs flow; only linting/formatting Markdown → run `uv run invoke docs.lint`.

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Mines a pull request's review threads for lessons that generalize beyond that PR, reconstructs each against the actual code before deciding, checks whether it is already codified, and routes the genuinely-new, durable ones into Infrahub's internal documentation — `dev/knowledge/`, `dev/guides/`, `dev/guidelines/`, the `AGENTS.md` files, and `.agents/rules/` — proposing edits first and applying only with the user's approval. TRIGGER when: the user wants to turn PR review feedback into durable conventions, guidelines, or rules; capture recurring reviewer comments as internal documentation; or check whether review lessons are reflected in the knowledge/guides/guidelines/AGENTS.md/rules layer. DO NOT TRIGGER when: sweeping a feature's changes for documentation coverage across all layers → use `audit-docs`; extracting knowledge from completed spec directories → `speckit-opsmill-extract`; only replying to or resolving review threads → normal git/gh flow.

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。

Deeply analyze an existing codebase feature by tracing execution paths, mapping architecture layers, understanding patterns and abstractions, and documenting dependencies. Use this skill when you need to understand how a feature works before modifying or extending it, when dispatched as a sub-task during feature-dev exploration, or when the user asks "how does X work in this codebase".

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。

Full-stack backend architecture and frontend-backend integration guide. TRIGGER when: building a full-stack app, creating REST API with frontend, scaffolding backend service, building todo app, building CRUD app, building real-time app, building chat app, Express + React, Next.js API, Node.js backend, Python backend, Go backend, designing service layers, implementing error handling, managing config/auth, setting up API clients, implementing auth flows, handling file uploads, adding real-time features (SSE/WebSocket), hardening for production. DO NOT TRIGGER when: pure frontend UI work, pure CSS/styling, database schema only.

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。

Meta-skill for internal codebase exploration at varying depths (quick/deep/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。

Reduces attack surface across OS, container, cloud, network, and database layers using CIS Benchmarks and zero-trust principles. Use when hardening production infrastructure, meeting compliance requirements, or implementing defense-in-depth security.

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。

Simplification audit or authorized codebase simplification whose stated objective is to remove accidental complexity. Use for evidence-backed deletion or consolidation of dead code, duplicate state, redundant APIs or layers, ownerless abstractions, obsolete compatibility or design records, and over-engineering in any language; also use for 代码简化 or 熵回收. Do not use for general code review, onboarding, style-only refactoring, or performance tuning.

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 you need to apply data-oriented programming best practices in Java — including separating code (behavior) from data structures using records, designing immutable data with pure transformation functions, keeping data flat and denormalized with ID-based references, starting with generic data structures converting to specific types when needed, ensuring data integrity through pure validation functions, and creating flexible generic data access layers. This should trigger for requests such as Improve the code with Data-Oriented Programming; Apply Data-Oriented Programming; Refactor the code with Data-Oriented Programming; Model Java data with records and pure functions; Separate Java behavior from immutable data structures; Validate data integrity with pure Java functions. Part of Plinth Toolkit

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。

Neutral, framework-agnostic decision tree for project kickoff: "which agent / RAG / LLM framework should I reach for?" Synthesizes the ecosystem sections of 7 landmark-project SOPs (LangGraph, LlamaIndex, DSPy, CrewAI, vLLM, Aider, Dify) into one layered rubric. Core stance: frameworks are LAYERS, not competitors — a real project usually combines DSPy (compile) + LlamaIndex (retrieve) + LangGraph (orchestrate) + vLLM (serve), and you choose ONE per layer, not one to rule all. Use when starting any LLM/agent/RAG project, or whenever the "which framework?" question is asked. Deliberately neutral — unlike vendor docs and the LangChain-biased `framework-selection` on skill.sh, this skill has no horse in the race.

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 performance specialist covering analysis, optimization, load testing, and framework-specific performance. Use when identifying bottlenecks, optimizing code, conducting load tests, analyzing Core Web Vitals, fixing memory leaks, or improving application performance across all layers (application, database, frontend). Includes React-specific optimization 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。