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概要と使いどころ
Use when evaluating stocks through Duan Yongping's benfen (本分) value investing lens: good business, right price, long hold, understand what you own, and stay within your circle of competence.
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 evaluating Li Lu-style value investments: circle of competence, long-term compounding, deep research, conservative downside, China/global quality businesses, and concentrated ownership.
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 evaluating Chris Hohn / TCI-style concentrated quality investing: exceptional businesses, pricing power, high returns on capital, engagement, and long-duration concentrated ownership.
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。
Explore an existing PostgreSQL database before answering questions about its data or writing SQL. Use this skill whenever a user asks for a query or a data-backed answer against an unfamiliar schema (counts, missing or failed records, recent changes), asks where a business concept lives, or asks how tables, joins, views, routines, triggers, RLS, or extensions work. Find the relevant objects with read-only pg_catalog queries, then request approval before inspecting data-derived statistics or rows. Not a schema-design or migration guide.
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。
World-class data science skill for statistical modeling, experimentation, causal inference, and advanced analytics. Expertise in Python (NumPy, Pandas, Scikit-learn), R, SQL, statistical methods, A/B testing, time series, and business intelligence. Includes experiment design, feature engineering, model evaluation, and stakeholder communication. Use when designing experiments, building predictive models, performing causal analysis, or driving data-driven decisions.
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。
Tools and techniques for detrending time series data in macroeconomic analysis. Use when working with economic time series that need to be decomposed into trend and cyclical components. Covers HP filter, log transformations for growth series, and correlation analysis of business cycles.
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。
Translate logistics and operations rules into optimization variables and constraints. Use when an operations problem describes vehicles, routes, depots, pickups, dropoffs, inventory, capacity, assignments, time windows, service targets, penalties, resource limits, or other business rules that need to become an optimization model.
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。
Write feature specifications as requirements and user stories with acceptance criteria, focusing on business value and testable conditions.
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 when wiring validation, error handling, or framework adapters. Concentrate guards at system boundaries (CLI, config, network, external APIs); trust internal types and keep business logic in pure functions.
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。
Systematically fix all failing tests after business logic changes or refactoring
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。
Create detailed 3-5 year financial model with revenue, costs, cash flow, and scenarios
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。
This skill should be used when the user asks about "key startup metrics", "SaaS metrics", "CAC and LTV", "unit economics", "burn multiple", "rule of 40", "marketplace metrics", or requests guidance on tracking and optimizing business performance metrics.
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 market opportunity analysis with TAM/SAM/SOM calculations
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 saga patterns for distributed transactions and cross-aggregate workflows. Use when coordinating multi-step business processes, handling compensating transactions, or managing long-running workflows.
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。
Derive security requirements from threat models and business context. Use when translating threats into actionable requirements, creating security user stories, or building security test cases.
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 effective KPI dashboards with metrics selection, visualization best practices, and real-time monitoring patterns. Use when building business dashboards, selecting metrics, or designing data visualization layouts.
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。
This skill should be used when the user asks to "calculate TAM", "determine SAM", "estimate SOM", "size the market", "calculate market opportunity", "what's the total addressable market", or requests market sizing analysis for a startup or business opportunity.
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 a comprehensive legacy system modernization using the strangler fig pattern, enabling gradual replacement of outdated components while maintaining continuous business operations through ex
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 data scientist for advanced analytics, machine learning, and statistical modeling. Handles complex data analysis, predictive modeling, and business intelligence. Use PROACTIVELY for data analysis tasks, ML modeling, statistical analysis, and data-driven insights.
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 DevKit · Review code, skills, and prompts for security vulnerabilities — OWASP Top 10, prompt injection, business logic flaws, and insecure defaults. Use when reviewing PRs, auditing modules, reviewing AI skills/prompts, or preparing for release.
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。
ktx's knowledge base - wiki pages for durable, reusable business knowledge. Covers capture workflow for user preferences, metric definitions, organizational conventions, and cross-references between wiki pages and semantic-layer sources. Loaded by the post-turn memory-agent only. The research agent reads wiki via `wiki_read`/`wiki_search` but does not write 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。
End-to-end orchestrator: from a business idea, app idea, or existing app to running Azure deployment with cost estimates and pre-deploy approval. Analyzes your app, auto-detects the right Azure services, scaffolds infrastructure code, and deploys — tailored to your app, not a template. Handles moving existing apps to Azure without rewriting or with minimal changes. WHEN: bring your app to Azure, plan my app, cost to run, is my code ready to deploy, deploy my app to the cloud, deploy all my services, what Azure services do I need, plan my Azure deployment, deploy my new app to Azure, one-click deploy, I have an app and want it on Azure, migrate my app to Azure, help me get started, build an app, no code yet, starter project. DO NOT USE FOR: use azd for deployment(use azure-deploy), optimizing existing costs (use cost-optimization), code readiness checks only (use azure-app-onboard-prereq).
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。
Experiment design reference for problem-class framing, Minimum Viable Experiment coaching, hypothesis formation, vetting and red flags, and experiment readiness. Use when translating a stated business outcome into candidate data-science problem classes, or when framing, vetting, scoping, or evaluating an experiment of any kind, including data feasibility, architecture, LLM, performance, use-case, UX, prototyping, and hardware experiments.
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 traceable internal-review proposal, RFI, RFP, tender, bid, and questionnaire responses from supplied questions and approved sources. Use to analyze questions, contribute business or product evidence, or draft qualified responses.
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。