「stock」の検索結果

Codified expertise for demand forecasting, safety stock optimization, replenishment planning, and promotional lift estimation at multi-location retailers. Informed by demand planners with 15+ years experience managing hundreds of SKUs. Includes forecasting method selection, ABC/XYZ analysis, seasonal transition management, and vendor negotiation frameworks. Use when forecasting demand, setting safety stock, planning replenishment, managing promotions, or optimizing inventory levels.

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股票投资调研执行引擎，执行8阶段投资尽调流程。接收stock-question-refiner生成的结构化调研指令，部署多智能体并行研究，生成带引用的投资尽调报告。覆盖：公司事实底座、行业周期、业务拆解、财务质量、股权治理、市场分歧、估值护城河、综合报告。当用户需要进行股票投资研究、基本面分析、投资尽调时使用此技能。

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Professional US stock analysis with financial data, news, social sentiment, and multi-model AI. Comprehensive reports at $0.02-0.10 per analysis.

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企業買収が一株当たり利益に与える影響をExcelで試算するスキル。買収価格、資金調達、相乗効果の前提を整理し、条件別の比較表と説明用の要約を作成します。

• 買収による一株当たり利益の試算
• 現金・株式の支払い条件の比較
• 買収価格と相乗効果の条件別比較

Use this skill whenever the user wants to create a finished video from a topic, title, idea, prompt, or script with MoneyPrinterTurbo. This includes short-form, voice-over, educational, marketing, social-media, and stock-footage videos. Also use it when the user mentions MoneyPrinterTurbo, provides this Skill URL, asks an AI agent to install or configure MoneyPrinterTurbo, needs missing API keys identified, wants a failed generation repaired, or wants a generated MP4 located and delivered. Use this skill when the expected outcome is a final video file, not setup instructions.

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。

X/Twitter public sentiment research for recent market, company, product, or community discourse. Use when the brief asks what people are saying on X, Twitter sentiment, CT sentiment, public opinion, expert posts, or social reaction around a stock, sector, company, product, or market event.

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Rewrite AI-sounding text so it reads like the writer without changing what it says. Use when editing or reviewing prose for AI tells: not-X-but-Y contrasts, one-line closers, staged openers, forced triads, dashes everywhere, inflated claims, sales language, stock AI words, bold labels, or filler. Based on Wikipedia's "Signs of AI writing."

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Crypto market leaderboards — ranked aggregate feeds across the whole market: social-hype/sentiment rank, trending / top-search / Binance Alpha / tokenized-stocks rank, smart-money net-inflow rank (which tokens received the most inflow), top meme tokens by breakout score on Pulse launchpad, top trader PnL leaderboard (ALL / KOL). Use whenever the user wants a ranked list of tokens or addresses by some metric — phrases like "trending tokens", "top N by hype", "leaderboard", "rank by X", "biggest inflows", "top traders this week".

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Build pre-earnings analysis with estimate models, scenario frameworks, and key metrics to watch. Use before a company reports quarterly earnings to prepare positioning notes, set up bull/bear scenarios, and identify what will move the stock. Triggers on "earnings preview", "what to watch for [company] earnings", "pre-earnings", "earnings setup", or "preview Q[X] for [company]".

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Generate comprehensive equity research snapshots combining analyst consensus estimates, company fundamentals, historical prices, and macroeconomic context. Use when researching stocks, comparing estimates to actuals, analyzing company financials, assessing equity valuations, or building investment cases.

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Draft concise morning meeting notes summarizing overnight developments, trade ideas, and key events for coverage stocks. Designed for the 7am morning meeting format — tight, opinionated, actionable. Triggers on "morning note", "morning meeting", "what happened overnight", "trade idea", "morning call prep", or "daily note".

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yfinance global market data interface — retrieve OHLCV and research data for US, HK, and Canadian stocks, ETFs, and indices via Yahoo Finance. Free, no API key required.

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Buy-side discipline system. For each holding, maintain a written investment thesis — core thesis in 5 sentences, falsifiable assumptions, red lines, valuation anchors — and re-check it each quarter against new earnings/events. Scores thesis health 1-10 from assumption breakage and red-line triggers, and recommends hold / add / trim / exit. Use it right after buying a stock (to write the thesis) and every earnings season (to check it). Bridges the research→buy→monitor gap that most investors skip.

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Multi-factor cross-sectional stock ranking. Combines factor standardization, equal-weight or IC-weighted scoring, and TopN portfolio construction. Suitable for multi-instrument portfolio strategies.

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A short self-bias checklist to run at the START of any investment research task (stock screen / sector study / company deep-dive). Four biases that systematically warp AI research — leader-bias (only big caps), English-bias (miss JP/KR/TW players), narrative-bias (chase concept labels), confirmation-bias (only bullish evidence) — plus recency-bias. Load this first, then research with the corrections in mind. Not a workflow, just a 60-second attitude reset that materially improves coverage and honesty.

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Performance attribution analysis — Brinson sector/stock-selection attribution, factor alpha/beta decomposition, market-timing evaluation, and benchmark comparison framework.

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Professional finance research toolkit — backtesting (11 engines + benchmark comparison panel), factor analysis, Alpha Zoo (478 pre-built alphas across qlib158/alpha101/gtja191/academic/fundamental), options pricing, 90 finance skills, 30 multi-agent swarm teams, Trade Journal analyzer, and Shadow Account (extract → backtest → render) across 29 market-data sources (tushare, yfinance, okx, binance, akshare, baostock, tencent, mootdx, ccxt, futu, mt5, tickerall, local, eastmoney, sina, stooq, yahoo, pykrx, india_broker, qveris, longbridge, nobitex, wallex, plus optional-key finnhub/alphavantage/tiingo/fmp/gildata).

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Stock Connect (Shanghai/Shenzhen-Hong Kong) fund flow analysis — Northbound (foreign into A-shares), Southbound (mainland into HK), sector allocation tracking, and cross-border arbitrage signals.

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Dividend stock analysis for income, dividend-growth, and shareholder-return strategies, including yield quality, payout sustainability, ex-dividend mechanics, and yield-trap checks.

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Deep management assessment — the 'buying a stock is buying a person' layer. For a company or a named executive, evaluate integrity (promise-vs-delivery tracking, crisis behavior, stakeholder treatment), ability (strategic foresight, execution, capital-allocation record) and governance (equity structure, compensation, related-party deals). Outputs a weighted score across integrity / ability / capital-allocation / governance plus Duan Yongping's 3-question verdict ('would you hand this person your money for 10 years'). Use when standard research leaves management quality uncertain, or when management IS the investment thesis.

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。

Zero-shot time series forecasting with Google's TimesFM foundation model. Use this skill when forecasting ANY univariate time series — sales, sensor readings, stock prices, energy demand, patient vitals, weather, or scientific measurements — without training a custom model. Supports both basic forecasting and advanced covariate forecasting (XReg) with dynamic and static exogenous variables. Automatically checks system RAM/GPU before loading the model, validates dataset fit before processing, supports CSV/DataFrame/array inputs, and returns point forecasts with calibrated prediction intervals. Includes a preflight system checker script that MUST be run before first use to verify the machine can load the model and handle your specific dataset.

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筹码分布分析工具（CYQ - Chip Distribution）。 基于量价数据计算获利盘比例、主力成本区、筹码集中度等专业指标。 使用三角分布模型，支持A股所有股票。 Use when: 用户提到"筹码分布"、"筹码分析"、"CYQ"、"获利盘"、"主力成本"、"筹码集中度"、"90%成本"。 NOT for: 推荐买卖时机、预测股价走势、给出投资建议。

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-Trader - AI Trading Signal Platform. Publish trading signals, follow traders. Use when user mentions trading signals, copy trading, stock trading, or follow traders.

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Trigger on mention of GKE cluster autoscaler, node autoscaling, node pool auto-creation / node auto-provisioning. Provides guidance on enabling and optimizing cluster autoscaler, best practices, and troubleshooting issues such as nodes not scaling up or down, zonal stockouts, or capacity buffers. Do not use for ComputeClass-specific YAML generation or priority configuration (defer to gke-compute-classes skill).

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。