244건 · 관련도순
Optimize stock allocations from price data using NVIDIA cuOpt, with controls for severe losses or overall variability. Compare risk and return, inspect allocation tables, backtest against benchmarks, and evaluate scheduled or drift-triggered rebalancing.
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[omh] Company budget overrun, cash risk, or close issue: turn finance and accounting inputs into a decision-ready variance, cash, and close-risk brief. Use when the user says: finance analysis, budget variance, budget vs actual, month-end close, 재무 분석, 예산 대비 실적, 월마감.
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Runs the period-end close and produces reporting — close calendar, reconciliations, accruals, variance analysis, and reporting that gets read. Use this to shorten or stabilize a monthly close, design a close checklist, investigate a variance, structure management reporting, or work out why the numbers keep changing after close.
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Comprehensive manuscript review with three modes: single-pass (default), --adversarial critic-fixer loop, and --peer [journal] simulated peer-review pipeline (editor + 2 dispositioned referees + editorial decision, calibrated to a target journal). R&R continuation via --peer --r2/--r3; hostile-editor stress test via --peer --stress; reviewer-disposition variance reporting via --peer --variance N. Auto-invokes /review-r + /audit-reproducibility on referenced scripts unless --no-cross-artifact.
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Design a budget-vs-actuals tracker that stays alive past February — the category grain that matches real statements, the variance view that answers 'are we okay', the update ritual small enough to survive, and the honest handling of irregular expenses. Use when asked build me a budget spreadsheet, track team spend against budget, why do we always blow the budget invisibly, or design a household/project budget tracker. Produces the tracker structure, the variance logic, the irregulars ledger, and the monthly fifteen-minute ritual.
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Fits structural equation models to GWAS summary statistics using GenomicSEM (Grotzinger 2019), including common-factor models, confirmatory factor models, ESEM, common-factor GWAS with Q_SNP heterogeneity, multivariate Wald tests, and stratified GenomicSEM partitioned heritability. Reconciles results against MTAG multi-trait analysis. Handles sample overlap via the LDSC sampling-covariance matrix, identifies and resolves Heywood cases, and verifies model fit with CFI / RMSEA. Use when modeling latent genetic architecture across correlated traits, running multivariate GWAS on a shared factor, distinguishing factor-mediated from trait-specific SNP effects, or comparing GenomicSEM common-factor results against MTAG when both depend on accurate sampling covariance.
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Running experiments out of the data warehouse instead of via dedicated experiment platforms. SQL-based assignment, exposure logging discipline, metric definitions in dbt models, statistical analysis in SQL or Python, variance reduction with CUPED, sequential testing, and the operational tradeoffs vs platforms like Statsig and Optimizely. Triggers on warehouse-native experimentation, run experiments in BigQuery, run experiments in Snowflake, dbt experiments, SQL t-test, CUPED variance reduction, exposure log, sample ratio mismatch, sequential testing, mSPRT, doubly robust estimation, build vs buy experimentation. Also triggers when the team is choosing between platform and warehouse, building warehouse-native experiment infrastructure, auditing one, or running an experiment with a custom metric the platform cannot handle.
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Matter budgeting and ongoing WIP/variance monitoring. Build phase-based fee estimates at matter setup, run bottom-up budgets by jurisdiction or workstream, calculate contingency, and structure AFA arrangements (fixed fee, capped fee, phased fixed fees). Ongoing monitoring: WIP tracking against budget, proportionality assessment (spend vs progress), variance commentary with root cause analysis, forecast-to-complete, realisation monitoring, write-off analysis. Trigger on: 'build a budget', 'fee estimate', 'what will this cost', 'WIP review', 'budget vs actual', 'how are we tracking against budget', 'we're over budget', 'realisation is poor', 'what's our ETC', 'budget for the German workstream', 'model the financial impact of this scope change', 'draft a fee adjustment', 'write-off analysis', 'how much contingency', 'AFA structure', 'fixed fee estimate', 'budget update', 'forecast to complete'.
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Score and compare covariance estimates with precise's assessor panel. Use when you need to judge an estimate out-of-sample or rank competing estimators — and especially in high dimensions, where the plain held-out likelihood is misleading.
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Inspect architectural variances with SMARTS; record only explicit user choices. Report-only requests make no changes.
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Use before generating, editing, or reviewing any user interface — a dashboard panel, a landing page, an email, a slide, an app screen. Sets three deliberate design dials (variance, motion, density) BEFORE generation so the output has a point of view instead of defaulting to the timid, uniform look that reads as "AI made this". Applies to Genesis's OWN dashboard UI, not only things built for others.
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Generate financial reports — monthly close package, variance analysis, and management reporting. Use when asked to "generate monthly financials", "close the books", or "write variance commentary".
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Performs financial ratio analysis, DCF valuation, budget variance analysis, and rolling forecast construction for strategic decision-making. Use when analyzing financial statements, building valuation models, assessing budget variances, or constructing financial projections and forecasts. Also applicable when users mention financial modeling, cash flow analysis, company valuation, financial projections, or spreadsheet analysis.
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Analyze budget-versus-actual or forecast-versus-actual performance and turn finance data into a management-ready narrative using variance logic, drivers, cash impact, and forward actions. Use when finance, FP&A, or business leaders need commentary, management accounts, or performance explanation. Do not use for bookkeeping entries, tax filing preparation, or legal accounting sign-off.
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Generate financial statements (income statement, balance sheet, cash flow) with period-over-period comparison and variance analysis. Use when preparing a monthly or quarterly P&L, closing the books and need to flag material variances, comparing actuals to budget, building a financial summary for leadership review, or looking up GAAP presentation requirements and period-end adjustments.
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"Management accounting toolkit for internal decision support: ABC costing, variance analysis, transfer pricing, and responsibility accounting. Use for product profitability disputes, budget variance diagnosis, inter-division pricing design, and business-unit manager performance evaluation. Triggers: 『這個產品真的賺錢嗎』『成本分攤』『事業部之間怎麼算錢』『本月成本超支』『評核廠長績效』『內部轉撥』『ABC 作業成本』『差異分析』『責任中心』『管理會計』. For Taiwan EMBA cases, term reports, and management-decision simulations (台大／陽交／成大 財管必修).".
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Fits structural equation models to GWAS summary statistics using GenomicSEM (Grotzinger 2019), including common-factor models, confirmatory factor models, ESEM, common-factor GWAS with Q_SNP heterogeneity, multivariate Wald tests, and stratified GenomicSEM partitioned heritability. Reconciles results against MTAG multi-trait analysis. Handles sample overlap via the LDSC sampling-covariance matrix, identifies and resolves Heywood cases, and verifies model fit with CFI / RMSEA. Use when modeling latent genetic architecture across correlated traits, running multivariate GWAS on a shared factor, distinguishing factor-mediated from trait-specific SNP effects, or comparing GenomicSEM common-factor results against MTAG when both depend on accurate sampling covariance.
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Expert-level probability theory knowledge. Use when working with probability spaces, random variables, distributions, expectation, variance, conditional probability, stochastic processes, Markov chains, martingales, or measure-theoretic probability. Also use when the user mentions 'sample space', 'random variable', 'expected value', 'variance', 'conditional probability', 'independence', 'Markov chain', 'Poisson process', 'moment generating function', 'law of large numbers', 'central limit theorem', or 'stochastic process'.
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管理会計ナビゲータースキル。ユーザーの相談内容を管理会計12領域に自動分類し、 適切な分析手法・実行スキルへルーティングする。予実差異分析、CVP分析、原価管理、 KPI設計、月次決算早期化など、管理会計の全領域をカバーする入口として機能。 COSO/IMA管理会計フレームワークに準拠。日英両言語対応。 Use when: ユーザーが管理会計に関する質問・相談をしたとき、どの分析手法を使うべきか 判断する入口として使用。「予算と実績の差が大きい」「原価を下げたい」「損益分岐点を知りたい」 などの相談を適切なスキルへ誘導する。 Triggers: "管理会計", "予実差異", "原価計算", "損益分岐点", "CVP", "KPI設計", "月次決算", "内製外注", "make or buy", "標準原価", "配賦", "ABC", "management accounting", "budget variance", "cost accounting", "break-even"
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Professional financial analysis skill for investment decisions, budgeting, financial statement analysis, and business plan evaluation. Use this skill when you need to analyze financial ratios (profitability, safety, efficiency), calculate DCF/NPV/IRR valuations, perform budget variance analysis, or conduct sensitivity/scenario analysis. Triggers include "analyze financial statements", "calculate DCF", "investment evaluation", "budget variance", "財務分析", "投資評価", "予算差異分析".
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Turns a schedule extract (milestone list or planning tool export) and the period's status notes into a DRAFT plain-language slip explanation: which milestones and activities slipped and by how many working days, arithmetic shown, why per the notes as verbatim fragments with source and date, what each slip pushes next per the extract, and the decision each slip needs framed as a question with the options the notes propose and the owner the governance documents name, every missing fact marked UNKNOWN. Use when the user asks to "explain the schedule slip", "what slipped this month and why", "write the schedule commentary for the steering pack", "put the delay in plain language for the sponsor" or "what decisions do these slips need". Do not use for the weekly status report, use project-status-tracker instead; for cost variances, use budget-variance-explainer; for one sprint's carry-over, use sprint-review-summary. Drafts for human review; never approves, authorises or signs off.
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Expert en budgétisation et prévisions (FP&A, variance analysis, rolling forecasts, DZ)
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Fits structural equation models to GWAS summary statistics using GenomicSEM (Grotzinger 2019), including common-factor models, confirmatory factor models, ESEM, common-factor GWAS with Q_SNP heterogeneity, multivariate Wald tests, and stratified GenomicSEM partitioned heritability. Reconciles results against MTAG multi-trait analysis. Handles sample overlap via the LDSC sampling-covariance matrix, identifies and resolves Heywood cases, and verifies model fit with CFI / RMSEA. Use when modeling latent genetic architecture across correlated traits, running multivariate GWAS on a shared factor, distinguishing factor-mediated from trait-specific SNP effects, or comparing GenomicSEM common-factor results against MTAG when both depend on accurate sampling covariance.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Fits structural equation models to GWAS summary statistics using GenomicSEM (Grotzinger 2019), including common-factor models, confirmatory factor models, ESEM, common-factor GWAS with Q_SNP heterogeneity, multivariate Wald tests, and stratified GenomicSEM partitioned heritability. Reconciles results against MTAG multi-trait analysis. Handles sample overlap via the LDSC sampling-covariance matrix, identifies and resolves Heywood cases, and verifies model fit with CFI / RMSEA. Use when modeling latent genetic architecture across correlated traits, running multivariate GWAS on a shared factor, distinguishing factor-mediated from trait-specific SNP effects, or comparing GenomicSEM common-factor results against MTAG when both depend on accurate sampling covariance.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.