「design」の検索結果

Bridge the gap from product specifications to implemented features. Document requirements, acceptance criteria, tech design, and track implementation progress in Notion.

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 comprehensive test plans, manual test cases, regression test suites, and bug reports for QA engineers. Includes Figma MCP integration for design validation.

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。

Design and configure Microsoft Entra app registrations for secure authentication and authorization. Use this skill when users need OAuth, OpenID Connect, API permissions, app roles, or multi-tenant identity. Covers auth flows, credentials, API permissions, consent models, and zero-trust 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。

Material-UI v7 component library patterns including sx prop styling, theme integration, responsive design, and MUI-specific hooks. Use when working with MUI components, styling with sx prop, theme customization, or MUI utilities.

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。

Build high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services. Covers design principles, architecture, implementation patterns, and evaluation-driven development.

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。

Language models exhibit predictable degradation patterns as context length increases. Understanding these patterns is essential for diagnosing failures and designing resilient systems.

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。

6 production-ready AI engineering workflows: prompt evaluation (8-dimension scoring), context budget planning, RAG pipeline design, agent security audit (65-point checklist), eval harness building, and product sense coaching.

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。

Design and apply Azure role-based access control with least privilege at the right scope. Use this skill when users ask about permissions, role assignments, access errors, or governance. Covers built-in roles, custom roles, managed identities, PIM, and access reviews per Well-Architected principles.

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。

Design and deploy copilot-style assistants on Azure with secure model access, tool integration, and observability. Use this skill when users ask to build assistants, agents, or copilot workflows in Azure. Covers orchestration, retrieval, safety guardrails, and production deployment.

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 in designing and building autonomous AI agents. Masters tool use, memory systems, planning strategies, and multi-agent orchestration.

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 OpenAI's brand colors, typography, and visual identity standards to artifacts. Ensure design consistency with OpenAI branding for presentations, documents, and UI components.

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。

Design and operate Azure Storage services for durability, performance, and secure access. Use this skill when users ask about Blob, Files, Queues, Tables, or Data Lake storage patterns. Covers redundancy, tiering, access models, lifecycle policies, and compliance.

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。

Design an Azure AI gateway layer for centralized model routing, policy enforcement, and operational control. Use this skill when users ask about centralized AI traffic management, model governance, rate limiting, or gateway 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。

Design spec with 98 rules for building CLI tools that AI agents can safely use. Covers structured JSON output, error handling, input contracts, safety guardrails, exit codes, and agent self-description.

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。

Cycle de coordination ai-01 (coordinateur UNIQUEMENT — jamais sur un worker). Lit memoire + dashboards + inbox + GitHub, merge les PRs pretes, tranche les design-gates, dispatche des grains par lane, reporte. Arguments: [--dispatch] [--focus <topic>]

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。

Shape reusable library APIs for Compose modules. Design public composables, split sample-app code from reusable library code, review naming and extensibility.

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Propose a new change with all artifacts generated in one step. Use when the user wants to quickly describe what they want to build and get a complete proposal with design, specs, and tasks ready for implementation.

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Calculate statistical power and sample sizes for RCTs. Use when user mentions: power analysis, sample size calculation, minimum detectable effect, MDE, statistical power, effect size, clustering effects, design effect, ICC, intra-cluster correlation.

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设计脑暴 / 实现前方案校准：当用户想把已基本成立的想法、功能方向或产品问题，在写 PRD、画 mockup 或进入开发计划前，先比较方案、确认取舍、对齐 UI/视觉约束，并收敛成可执行设计 spec 时使用。 可用中文唤起：“先脑暴一下方案”“先不要写 PRD，帮我设计几种路径”“参考 brainstorming 把这个需求变成设计 spec” “实现前先讨论设计”。问题还没定义清楚时先用 ai-collaboration-calibration；已有方案要压力测试时用 grill-me； 直接写 PRD 时用 prd-architect；grill-me 返回精确设计 gap 时，本 Skill 只输出 Design Delta。

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。

Perform comprehensive technical reviews of Ruby on Rails applications. Runs automated analysis tools (RubyCritic, Brakeman, bundler-audit, Gitleaks, Debride, linters, SimpleCov, Rails stats, Rails ERD), analyzes code for architecture, security, authorization (Pundit/CanCanCan), dead code, and design issues, and produces a structured markdown report with prioritized findings and a 0-10 score. Use when the user requests a tech review, code audit, project assessment, or quality analysis of a Rails application.

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。

Agent 配置設計指南 — 基於 Claude Code 6 個 built-in agent 的逆向分析。Use when: 設計新 agent、優化現有 agent prompt、決定工具/模型配置、撰寫 dispatch prompt。

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。

Council — 5 independent AI perspectives + mutual critique + synthesis (adapted from karpathy/llm-council for Claude Code). For high-stakes decisions where the answer does NOT follow unambiguously from the data — go/no-go before real consequences, critiquing a methodology, choosing between competing approaches, pressure-testing a conclusion you're attached to, designing risk controls. NOT for log/data analysis (the council has no data access) and not for factual lookups. Triggers — "convene the council", "run it past the council", "go/no-go", "pressure-test this".

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。

Autonomous goal loop — give it ONE goal plus how to verify it, the agent runs check→fix→check itself and comes back only with the finished result (code with audit, research with fact-check, content with critique, audit/backtest). Self-verification instead of constant supervision, with hard safety guards. Vary the loop with a verification rubric designed before it runs. Triggers — "work autonomously to the result", "don't come back until it's done", "run the loop yourself", "drive it to done".

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。

DRY (Don't Repeat Yourself) review rules for code reviews: find the same knowledge — a rule, a constant, a parser, a validation, a protocol detail — implemented in several places that must change together, and tell it apart from code that merely looks alike. Use when reviewing a diff, a pull request or recent changes for duplication, on "DRY review", "is this duplicated", "copy-paste check", or when a review checklist asks for DRY. Pair it with the solid skill for design and with owasp-asvs for 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。