806 件 ・ 関連度順
概要と使いどころ
Consult a virtual board of 4 strategic advisors (Donald Miller, Seth Godin, Alex Hormozi, Daniel Priestley) on any major business decision. Two rounds of argument + rebuttal, then a decision brief, interactive dashboard, and clear recommendation.
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, multi-dimensional company investment research framework for AI agents and human analysts. Provides a 10-part checklist (moat, tech, market, customers, growth, financials, geography, governance, valuation, recommendation) to turn scattered info into a consistent, high-quality investment memo. | 面向 AI Agent 与人工分析师的公司投研框架，用 10 大维度系统梳理商业模式、护城河、成长与估值，快速产出结构化投研报告。
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。
Analyze chatmode or prompt files and recommend optimal AI models based on task complexity, required capabilities, and cost-efficiency
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 mentions IP geolocation feeds, RFC 8805, geofeeds, or wants help creating, tuning, validating, or publishing a self-published IP geolocation feed in CSV format. Intended user audience is a network operator, ISP, mobile carrier, cloud provider, hosting company, IXP, or satellite provider asking about IP geolocation accuracy, or geofeed authoring best practices. Helps create, refine, and improve CSV-format IP geolocation feeds with opinionated recommendations beyond RFC 8805 compliance. Do NOT use for private or internal IP address management — applies only to publicly routable IP addresses.
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 complete solutions for specific Dataverse SDK use cases with architecture 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。
Prepare for tomorrow's meetings and tasks. Pulls calendar from Outlook via WorkIQ, cross-references open tasks and workspace context, classifies meetings, detects conflicts and day-fit issues, finds learning and deep-work slots, and generates a structured HTML prep file with productivity 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。
Comprehensive AI prompt engineering safety review and improvement prompt. Analyzes prompts for safety, bias, security vulnerabilities, and effectiveness while providing detailed improvement recommendations with extensive frameworks, testing methodologies, and educational content.
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 vector databases, embedding strategies, and semantic search implementation. Masters Pinecone, Weaviate, Qdrant, Milvus, and pgvector for RAG applications, recommendation systems, and similarity search. Use PROACTIVELY for vector search implementation, embedding optimization, or semantic retrieval 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。
Analyzes provided content for quality, E-E-A-T signals, and SEO best practices. Scores content and provides improvement recommendations based on established guidelines. Use PROACTIVELY for content 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。
Master modern business analysis with AI-powered analytics, real-time dashboards, and data-driven insights. Build comprehensive KPI frameworks, predictive models, and strategic recommendations. Use PROACTIVELY for business intelligence or strategic analysis.
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。
Test naming conventions and best practices specialized skill. Used when naming test methods, improving test readability, and establishing naming standards. Covers three-part naming method, Chinese naming recommendations, test class naming, etc. Keywords: test naming, test naming, naming conventions, naming conventions, three-part naming, three-part naming, method_scenario_expected, method_scenario_expected, how to name tests, test readability, naming best practices, test report, test documentation
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 AI prompt engineering safety review and improvement prompt. Analyzes prompts for safety, bias, security vulnerabilities, and effectiveness while providing detailed improvement 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。
Facilitate structured post-incident reviews to identify root causes, document what worked and failed, and produce actionable recommendations to improve future incident response.
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。
Analyze and improve the internal link structure of an AEM Edge Delivery Services site. Builds a link graph from the query index and page content, identifies orphan pages, hub pages, and content silos, and generates specific linking recommendations with suggested anchor text and placement. Use when improving site navigation, fixing orphan pages, strengthening topical authority, or auditing link equity distribution.
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 source-grounded consulting-style analysis for strategy, market, product, operations, or decision memos. Use for MECE problem framing, hypotheses, options, tradeoffs, executive recommendations, or "consulting analysis" requests; do not use for legal/financial advice, implementation, data computation, chart rendering, or unsourced business claims.
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。
PR test analysis routing. Use for pull request or diff test impact, changed-test review, CI failure interpretation, and focused test matrix recommendations; do not use for general code review or editing tests.
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。
Analyzes and optimizes SQL queries using EXPLAIN plans, index recommendations, query rewrites, and performance benchmarking. Use for "query optimization", "slow queries", "database performance", or "EXPLAIN analysis".
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。
Standardizes UX states for data fetching flows (loading, error, empty, success) and complex multi-view UI patterns. Provides state machine approach recommendations, loading skeletons, error boundaries, and empty state components. Use when implementing "loading states", "error handling", "empty states", or "state machines".
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。
Scores and prioritizes technical debt by impact, risk, and effort. Provides ranked backlog with ROI analysis and quarterly paydown recommendations. Use for "tech debt", "technical debt management", "code quality", or "refactoring priorities".
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。
Identifies domain modules, ownership boundaries, dependencies, and interfaces using Domain-Driven Design principles. Provides domain maps, bounded contexts, refactor recommendations. Use for "DDD", "domain modeling", "bounded contexts", or "service boundaries".
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。
Audits project dependencies for outdated packages, duplicates, heavy/problematic libraries, security vulnerabilities, and risky version combinations. Generates prioritized reports with security, performance, and maintainability insights, upgrade paths, and safe pinning recommendations. Use when users request dependency audits, package updates, security checks, or dependency optimization.
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。
Installs and configures Prettier, ESLint, EditorConfig, and other code quality tools to enforce consistent code style across the team. Generates config files, npm scripts, editor settings recommendations, and CI integration suggestions. Use when users request "setup prettier", "add eslint", "configure code formatting", or "enforce code style".
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 LLM costs and improves response times through caching, model selection, batching, and prompt optimization. Provides cost breakdowns, latency hotspots, and configuration recommendations. Use for "cost reduction", "performance optimization", "latency improvement", or "efficiency".
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。
Estimates infrastructure needs based on traffic forecasts, workload analysis, and performance requirements with sizing recommendations and cost trade-offs. Use for "capacity planning", "infrastructure sizing", "resource estimation", or "scalability planning".
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。