「bi」の検索結果

Opinionated constraints for building better web interfaces with coding agents. Use this skill whenever the user asks you to build, review, or fix a UI — landing pages, dashboards, components, or any frontend code — and whenever you write Tailwind CSS, motion/react animations, accessible component primitives (Base UI, React Aria, Radix), or review agent-generated interface code for quality, accessibility, and performance. Also trigger for "make the UI better", "review my interface", "clean up this component", or any frontend work in React, Next.js, or plain HTML/CSS.

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。

Domain-Driven Design system for software development. Use when designing new systems with DDD principles, refactoring existing codebases toward DDD, generating code scaffolding (entities, aggregates, repositories, domain events), facilitating Event Storming sessions, creating bounded context maps, or performing code reviews with a DDD lens. Covers both strategic design (bounded contexts, subdomains, context maps, ubiquitous language) and tactical design (entities, value objects, aggregates, domain services, repositories). Supports all major architecture patterns (Hexagonal/Ports & Adapters, CQRS, Event Sourcing, Clean Architecture) with language-agnostic guidance and concrete examples in Python and TypeScript.

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。

Find, build, evaluate, and improve systems using decision models. Use for bounded classification, routing, ranking, Choice/Score/Noul, decision-model composition, evaluation harnesses, prompt/program hill climbing, and Software 3.0 workflows across software, business, organizations, and life. Separate models, code, and human judgment. TypeSafe Jev is the default hosted exemplar. Not for straightforward arithmetic, prose rewriting, or provider setup alone.

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。

Schema.org structured data audit and generation optimized for AI discoverability — detect, validate, and generate JSON-LD markup

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 website GEO+SEO audit with parallel agents first and skills fallback. Orchestrates a comprehensive Generative Engine Optimization audit across AI citability, platform analysis, technical infrastructure, content quality, and schema markup. Produces a composite GEO Score (0-100) with prioritized action 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。

Brand mention and authority scanner for AI visibility. Analyzes brand presence across platforms that AI models rely on for entity recognition and citation decisions. Produces a Brand Authority Score (0-100) with platform-specific recommendations.

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。

AI crawler access analysis. Checks robots.txt, meta tags, and HTTP headers to determine which AI crawlers can access the site. Provides a complete access map and recommendations for maximizing AI visibility while maintaining appropriate control.

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。

Generate a professional, client-facing GEO report combining all audit results into a single deliverable with scores, findings, and prioritized actions

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。

Content quality and E-E-A-T assessment for AI citability — evaluate experience, expertise, authoritativeness, trustworthiness, and content structure

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。

Platform-specific AI search optimization — audit and optimize for Google AI Overviews, ChatGPT, Perplexity, Gemini, and Bing Copilot individually

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 traceability from natural-language intent to machine predicates, final observables, evidence, and owner-readable acceptance. Use when conducting any audit, review, QA pass, parity or compliance check, security assessment, code or architecture review, visual or behavioral comparison, data validation, system verification, completion gate, or claim that an implementation satisfies a human request.

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 writing or refactoring modern C# with maintainability and performance standards. Keywords: coding standards, C# best practices, refactor C#, records, pattern matching, async best practices.

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 selecting serialisation formats and contracts for APIs, messaging, caching, or persistence. Keywords: serialisation, System.Text.Json, source generator, protobuf, messagepack, wire compatibility, AOT.

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 reviewing PRs/diffs/branches/documents for .NET quality, correctness, performance, security, data access, messaging, and observability. Includes adversarial critical-thinking mode for skeptical/cynical review requests. Keywords: code review, review PR, review diff, critical review, cynical review, adversarial review, production readiness, low GC, AOT, security review.

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 asks for a multi-agent .NET code review - five parallel reviewers (correctness, performance, security/observability, data/messaging/integration, generalist) plus an adversarial maintainer that refutes weak findings. Requires the review agent roles registered by scripts/install-codex.sh. Keywords: dotnet review pipeline, multi-agent review, parallel reviewers, maintainer pushback, review this branch, cynical review.

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。

Sync reusable frontend decisions into persistent project documentation after UI work. Use when frontend changes introduce or modify component APIs, variants, design-system tokens, styling conventions, layout behavior, accessibility rules, interaction patterns, responsive behavior, or agent operating instructions; when the user asks to reflect decisions in components.md, design system docs, AGENTS.md, CLAUDE.md, GEMINI.md, or similar project memory files; or before finalizing frontend work where future consistency depends on decisions made during implementation.

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。

Diagnose and fix CJK (Korean, Japanese, Chinese) text-wrapping issues in web UIs. Systematically traces mid-syllable breaks, orphaned glyphs, and awkward line splits through a layered fix strategy covering global CSS cascade, component-library overrides, headline balancing, and translation-level phrase binding. Produces an evidence-based diagnosis and verification report before any code change is applied.

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 an existing agent skill or instruction file for lines that do not change behavior, routing overlap, and portability, or vet a third-party skill before installing it. To create or edit a skill, use the platform's skill-creator instead. Not for running a skill's own task.

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 a two-agent independent review/redteam followed by peer collaboration to produce a consensus superset of findings and enumerated revision requests. Use when Codex 5.5 and Claude Opus 4.7 should first critique a matter independently, then exchange work, synthesize the best combined analysis, and finish with a single agreed revision list.

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。

Harness-first workflow for evaluating and improving terminal UIs through real interaction. Use this whenever the user wants to review a TUI experience, compare before/after behavior, identify UX friction, validate navigation or filtering flows, inspect visual stability, or turn observed interaction problems into concrete follow-up tests and improvement recommendations. Prefer this skill any time a TUI should be exercised through a reproducible PTY/session harness instead of only reading 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。

Use this skill whenever the user wants to create, edit, explain, validate, or troubleshoot a proctmux.yaml/procmux.yaml file, add or change proctmux processes, configure lifecycle behavior such as stop signals/on_kill/autostart, customize unified-mode layout/keybindings/style, or enable Makefile/package.json process discovery. Use it even when the user says this casually, such as "add my dev server to proctmux", "fix this proctmux config", "write a config for these services", or "what YAML option controls focus".

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。

Lista de verificação abrangente para conduzir revisões de código minuciosas, cobrindo funcionalidade, segurança, desempenho e manutenibilidade

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。

Habilidade abrangente de revisão de código para TypeScript, JavaScript, Python, Swift, Kotlin, Go. Inclui análise automatizada de código, verificação de boas práticas, scanning de segurança e geração de checklist de revisão. Use ao revisar pull requests, fornecer feedback de código, identificar problemas ou garantir padrões de qualidade de código.

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。

Esta habilidade deve ser usada quando o usuário pede para "criar um slash command", "adicionar um comando", "escrever um comando personalizado", "definir argumentos de comando", "usar frontmatter de comando", "organizar comandos", "criar comando com referências de arquivo", "comando interativo", "usar AskUserQuestion em comando", ou precisa de orientação sobre estrutura de slash command, campos de frontmatter YAML, argumentos dinâmicos, execução bash em comandos, padrões de interação com usuário, ou melhores práticas de desenvolvimento de comando para Claude 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。