7,223 件 ・ 関連度順
概要と使いどころ
Query Google Search Console for SEO data - search queries, top pages, CTR opportunities, URL inspection, and sitemaps. Use when analyzing search performance, finding optimization opportunities, or checking indexing status.
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 multi-agent teams with defined roles, task lifecycles, handoff protocols, and review workflows. Use when: (1) Setting up a team of 2+ agents with different specializations, (2) Defining task routing and lifecycle (inbox → spec → build → review → done), (3) Creating handoff protocols between agents, (4) Establishing review and quality gates, (5) Managing async communication and artifact sharing between agents.
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。
Maxim 3D CGI product render — engineered for purchase confidence and premium positioning. Uses Material Realism + Endowment Effect + Photorealism Trust Signals to move premium-product viewers toward purchase. Use for luxury product reveals, architectural visualization, tech hardware marketing, high-ticket commerce.
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。
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.
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。
Maxim Presentation Intelligence — pitch decks, investor presentations, executive briefings, product demos, conference talks, narrative-driven visual storytelling, data visualization in slides. Applies Minto Pyramid, Duarte Sparkline, McKinsey Slide Logic, and Dual Coding Theory to every deck. Powered by Maxim behavioral science layer.
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。
Decide what to build — product strategy, roadmap, feature prioritization, product-market fit, pricing model, backlog, sprint planning, product vision, RICE, JTBD, OKRs. Distinct from designing or building it.
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。
Regulatory review across 14 frameworks — compliance review, privacy impact assessment, regulatory gap analysis, data subject rights, consent mechanisms, audit trail, AI ethics review, data flow mapping. Covers GDPR, PIPEDA, HIPAA, PCI-DSS, SOC2, UAE-PDPL, EU AI Act and more.
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。
PostgreSQL-specific development assistant focusing on unique PostgreSQL features, advanced data types, and PostgreSQL-exclusive capabilities. Covers JSONB operations, array types, custom types, range/geometric types, full-text search, window functions, and PostgreSQL extensions ecosystem.
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。
Power BI report visualization design prompt for creating effective, user-friendly, and accessible reports with optimal chart selection and layout design.
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 Power BI data model design review prompt for evaluating model architecture, relationships, and optimization opportunities.
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。
Python JSON parsing best practices covering performance optimization (orjson/msgspec), handling large files (streaming/JSONL), security (injection prevention), and advanced querying (JSONPath/JMESPath).
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。
PostgreSQL-specific code review assistant focusing on PostgreSQL best practices, anti-patterns, and unique quality standards. Covers JSONB operations, array usage, custom types, schema design, function optimization, and PostgreSQL-exclusive security features like Row Level Security (RLS).
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。
Four-phase framework for onboarding enterprise customers from contract to value realization. Use when implementing new enterprise customers, preventing churn during onboarding, or solving the adoption cliff that kills deals post-go-live. Includes the Week 4 ghosting pattern.
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 to get context about Fabric Lakehouse and its features for software systems and AI-powered functions. It offers descriptions of Lakehouse data components, organization with schemas and shortcuts, access control, and code examples. This skill supports users in designing, building, and optimizing Lakehouse solutions using 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。
GitHub CLI (gh) comprehensive reference for repositories, issues, pull requests, Actions, projects, releases, gists, codespaces, organizations, extensions, and all GitHub operations from the command line.
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。
Retrieve and display GitHub Copilot usage metrics for organizations and enterprises using the GitHub CLI and REST API.
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 production-ready Python code using Dataverse SDK with error handling, optimization, and 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。
Generate production code for Dataverse SDK using advanced patterns, error handling, and optimization techniques.
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。
Technology-agnostic prompt generator that creates customizable AI prompts for scanning codebases and identifying high-quality code exemplars. Supports multiple programming languages (.NET, Java, JavaScript, TypeScript, React, Angular, Python) with configurable analysis depth, categorization methods, and documentation formats to establish coding standards and maintain consistency across development teams.
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 git branch cleanup and organization
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 skill for working with Azure DevOps REST API across all services including Boards (work items, queries, backlogs), Repos (Git, pull requests, commits), Pipelines (builds, releases, deployments), Test Plans, Artifacts, organizations, projects, security, extensions, and more. Use when implementing Azure DevOps integrations, automating DevOps workflows, or building applications that interact with Azure DevOps 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。
Modular orchestration of agent patterns from Anthropic's engineering guide. Intelligently selects and implements prompt chaining, routing, parallelization, orchestrator-workers, evaluator-optimizer, and autonomous agents. Includes pattern combinations and language-specific implementations.
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。
Patterns for designing well-structured, maintainable Ansible playbooks with state-based (present/absent) design, play organization, and proper variable scoping.
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 iterative experimentation loop for any programming task. Guides the user through defining goals, measurable metrics, and scope constraints, then runs an autonomous loop of code changes, testing, measuring, and keeping/discarding results. Inspired by Karpathy's autoresearch. USE FOR: autonomous improvement, iterative optimization, experiment loop, auto research, performance tuning, automated experimentation, hill climbing, try things automatically, optimize code, run experiments, autonomous coding loop. DO NOT USE FOR: one-shot tasks, simple bug fixes, code review, or tasks without a measurable metric.
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。