3,618 件 ・ 関連度順
概要と使いどころ
Plan or review FriendlyBet architecture, implementation, schema, data flows, scoring, provider integrations, static/PWA behavior, Supabase/RLS impact, migrations, automation, and technical sequencing. Use when a task asks how to build, refactor, generalize, integrate, test, or safely ship FriendlyBet code.
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 iterative IA deepening, finalize shard, update indexes/tracker, and enforce ambiguity gates before handoff
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。
Orchestrate IA shard authoring via design and deepen passes, with pipeline-state-driven shard selection and ambiguity gates
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。
Scoring rubrics for all 5 pipeline layers (Vision, Architecture, IA, BE, FE). Use when auditing specs, running self-checks, or remediating ambiguity gaps.
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。
Axis-by-axis interview methodology for establishing engineering performance budgets during architecture PRD compilation. Use when conducting the Step 10.5 performance budget interview in /create-prd-compile.
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。
Validate shard load/dependencies, create deep-dive skeletons, update ledger assignments, and generate pipeline tracker
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 decomposition directory structure, IA shard skeletons, and all required layer indexes from approved decomposition plan
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。
Define security, attack surface, integration, and observability architecture with compliance escalation
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 the user wants to design, redesign, shape, critique, audit, polish, clarify, distill, harden, optimize, adapt, animate, colorize, extract, or otherwise improve a frontend interface. Covers websites, landing pages, dashboards, product UI, app shells, components, forms, settings, onboarding, and empty states. Handles UX review, visual hierarchy, information architecture, cognitive load, accessibility, performance, responsive behavior, theming, anti-patterns, typography, fonts, spacing, layout, alignment, color, motion, micro-interactions, UX copy, error states, edge cases, i18n, and reusable design systems or tokens. Also use for bland designs that need to become bolder or more delightful, loud designs that should become quieter, live browser iteration on UI elements, or ambitious visual effects that should feel technically extraordinary. Not for backend-only or non-UI 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。
Senior Kotlin specialist code review — framework-agnostic. Use whenever the user asks to review, audit, critique, or get feedback on Kotlin code, a file, a class, a function, a module, a diff, a branch, a commit, or a pull request. Judges Kotlin language idioms, logic correctness, architecture and layering, concurrency (coroutines, Flow, structured concurrency, cancellation, race conditions), null safety, exception safety, testability, performance, and security. Detects force-unwraps, unchecked casts, GlobalScope, leaked mutability, broken structured concurrency, hidden side effects, weak abstractions, untestable seams, allocation hot spots, weak crypto, input-validation gaps, and PII logging. Outputs prioritized findings with file:line anchors and minimal-diff fixes. No assumptions about DI framework, UI framework, or build system.
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 Kotlin Compose design tokens from one of three supported sources — Google Stitch (MCP), Figma (MCP or JSON), or Claude Design (kta-design-spec output, Claude HTML/markdown artifacts). Three-layer architecture (Primitive/Semantic/Component) with code generation. Use when user asks to "generate design tokens", "create theme from Figma/Stitch/Claude design", "build Compose tokens from design spec", or "convert design source to tokens". Refuses any other input source and asks user for correct seed data.
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 Kotlin Compose design tokens from one of three supported sources — Google Stitch (MCP), Figma (MCP or JSON), or Codex Design (kta-design-spec output, Codex HTML/markdown artifacts). Three-layer architecture (Primitive/Semantic/Component) with code generation. Use when user asks to "generate design tokens", "create theme from Figma/Stitch/Codex design", "build Compose tokens from design spec", or "convert design source to tokens". Refuses any other input source and asks user for correct seed data.
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 this skill for any Ionic Vue + Capacitor mobile work: Vue architecture, Composition API, TypeScript, state, env config, shared conventions. Required for every implementation task alongside mobile-testing.
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 Gang of Four design patterns to solve architectural problems in TypeScript. Use when refactoring code architecture, implementing extensible systems, decoupling components, or following SOLID principles. Covers all 22 GoF patterns: Creational (Factory Method, Abstract Factory, Builder, Prototype, Singleton), Structural (Adapter, Bridge, Composite, Decorator, Facade, Flyweight, Proxy), and Behavioral (Chain of Responsibility, Command, Iterator, Mediator, Memento, Observer, State, Strategy, Template Method, Visitor).
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。
React composition patterns that scale. Use when refactoring components with boolean prop proliferation, building flexible component libraries, or designing reusable APIs. Triggers on tasks involving compound components, render props, context providers, or component architecture. Includes React 19 API 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。
Multi-phase dialectical stress-test for HIGH-STAKES decisions only (architecture choices, irreversible product calls, strategic bets). Heavy-cost skill — do NOT use for routine questions, brainstorming, or simple tradeoffs. User must explicitly invoke or describe a decision they call "high-stakes", "irreversible", or "needs stress-testing". Triggers: "stress test this decision", "dialectic on", "challenge this thesis", "should I really".
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。
Structured decision-making framework for technical proposals and trade-off analysis. When the user faces a choice between multiple approaches (architecture decisions, tool selection, refactoring strategies, migration paths), this skill produces a 1-3-1 format: one clear problem statement, three distinct options with pros/cons, and one concrete recommendation with definition of done and implementation plan. Use when the user asks for a "1-3-1", says "give me options", or needs help choosing between competing approaches.
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。
Top 20 GitHub Repositories for AI Agents in 2026 (Ranked by Stars) — 6. Apr. 2026 · Discover the 20 most popular open-source AI agent repositories on
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September 16, 2026 New ask Hacker News story: Open-sourced jev architecture last year with model,pap
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With this cross-journal Collection, the editors at Nature Communications , Communications Psychology GAP: no mappable capability in latest signal (our
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Neuromorphic Computing - Fraunhofer Institute for … — Neuromorphic computing is a field of technology aimed at designing computer architectures that m
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Architecture Matters We often conflate different AI paradigms: LLMs (Transformers): Predict token se
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Sep 24, 2025 Deep Dive into Context Engineering for Agents Sep 18, 2025 Architectures for Multi-Agen
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MegaTrain Full Precision Training of 100B+ Parameter LLMs on a Single GPU A RAM-centric architecture that stores parameters in host memory and treats
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。