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概要と使いどころ
Structures portfolio company engagement with board preparation, hiring support, business development, and follow-on strategy. Use when supporting portfolio companies, preparing board meetings, or coordinating investor resources.
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Assesses scaling companies with product-market fit validation, unit economics maturity, and growth trajectory sustainability analysis. Use when evaluating growth-stage investments, analyzing scaling businesses, or assessing expansion readiness.
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。
Creates sell-side CIMs with business description, financial overview, growth drivers, and investment highlights. Use when preparing sell-side marketing materials, writing CIMs, or positioning companies for sale.
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。
Assesses operational improvement opportunities with cost rationalization, revenue stabilization, and management changes for distressed businesses. Use when evaluating turnaround plans, identifying operational improvements, or assessing management capability.
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。
Structures management presentation materials for buyer meetings with business overview, strategy, and financial deep-dives. Use when preparing management presentations, coaching management teams, or organizing buyer meetings.
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。
Structures S-1/F-1 prospectus content with business description, risk factors, MD&A, and financial presentation requirements. Use when preparing IPO prospectuses, drafting SEC filings, or structuring offering documents.
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。
Assesses marketplace dynamics with take rate analysis, liquidity metrics, supply/demand balance, and network effect strength. Use when evaluating marketplaces, analyzing take rates, or assessing network effect moats.
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。
Deconstructs unit economics with CAC, LTV, payback period, gross margin, and contribution margin analysis. Use when analyzing unit economics, validating SaaS metrics, or assessing business model 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。
Shell out to Cursor Agent CLI for headless IDE-aware code tasks. Supports multi-model routing (auto mode routes to Claude, Gemini, GPT). Requires Cursor Pro/Business subscription.
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 SEO audit with parallel subagent delegation. Crawls up to 500 pages, detects business type, delegates to 7 specialists, generates health score. Use when user says "audit", "full SEO check", "analyze my site", or "website health check".
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。
Prospective failure analysis using Gary Klein's pre-mortem technique. Assumes complete failure, works backward to identify risks, leading indicators, and circuit breakers. Counters optimism bias by forcing systematic exploration of failure modes before they materialize. Use for project plans, architecture decisions, technology adoption, business strategy, or feature launches. Triggers on "리스크", "위험", "실패하면", "swing-mortem", "뭐가 잘못될 수 있어", "risk", "what could go wrong", "걱정되는 점", "failure modes", "리스크 분석", "위험 분석".
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。
Reflective Memory
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。
Reflective Memory. Use when working with memory, reflection, context management, or intent tracking. Provides the reflective memory practice — how and when to use keep tools for capturing insights, tracking commitments, and maintaining awareness.
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。
Review a data analysis for quality, correctness, and reproducibility. Covers data quality assessment, assumption checking, model validation, data leakage detection, and reproducibility verification. Use when reviewing a colleague's analysis before publication, validating an ML pipeline before production deployment, auditing a report for regulatory or business decision-making, or performing a second-analyst review in a regulated environment.
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 execute A/B tests for ML models in production using traffic splitting, statistical significance testing, and canary/shadow deployment strategies. Measure performance differences and make data-driven decisions about model rollout. Use when validating a new model version before full rollout, comparing candidate models trained with different algorithms, measuring business metric impact of model changes, or when regulatory requirements mandate gradual rollout.
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。
Implement comprehensive model drift monitoring using Evidently AI, statistical tests (PSI, KS), and custom metrics to detect data drift and concept drift in production ML systems. Set up automated alerting and reporting workflows to catch degradation before it impacts business metrics. Use when production models show unexplained performance degradation, when new data distributions differ from training data, when seasonal shifts affect input features, or when regulatory requirements mandate model monitoring.
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。
Implement cloud cost optimization strategies for Kubernetes workloads using tools like Kubecost for visibility, right-sizing recommendations, horizontal and vertical pod autoscaling, spot/preemptible instances, and resource quotas. Covers cost allocation, showback reporting, and continuous optimization practices. Use when cloud costs are growing without proportional business value, when resource requests are misaligned with actual usage, when manual scaling leads to over-provisioning, or when implementing showback and chargeback for internal cost accountability.
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。
Remove dead code, unused imports, fix lint warnings, and normalize formatting across a codebase without changing business logic or architecture. Use when lint warnings have piled up during rapid development, unused imports and variables clutter files, dead code paths were never removed, formatting is inconsistent, or static analysis tools report fixable hygiene issues.
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 US stock analysis including fundamental analysis (financial metrics, business quality, valuation), technical analysis (indicators, chart patterns, support/resistance), stock comparisons, and investment report generation. Use when user requests analysis of US stock tickers (e.g., "analyze AAPL", "compare TSLA vs NVDA", "give me a report on Microsoft"), evaluation of financial metrics, technical chart analysis, or investment recommendations for American stocks.
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-platform review aggregation and thematic analysis for any business
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。
Issue Greek invoices through AADE's timologio, get the MARK, keep one JSON per invoice, push it into the ERP (SAP Business One today; other adapters welcome) and email the PDF. Trigger on: τιμολόγιο, τιμολόγησε, έκδοση τιμολογίου, invoice, myDATA, timologio, MARK, ΤΠΥ, πελάτης ΑΦΜ, προεπισκόπηση τιμολογίου.
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 an answer is wanted from one named seat only: breaking, business, career, codebase, human, impact, innovation, moat, rent-seeking, skeptic, or stability. Not for multi-lens synthesis: use prism. Not for rebuilding from primitives: use from-first-principle.
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。
Elicit and challenge data-analysis requirements before querying. Use when the user asks ambiguous data questions, requests business/report answers, needs metric definitions clarified, or may be drawing decisions from incomplete 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。
Read project data documentation (data dictionaries, dbt manifests, model docs, column descriptions, lineage, metric definitions) before writing analytics SQL. Use for mapping business and product terms to concrete models and columns.
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。