「prediction」の検索結果

Audit game analytics and telemetry implementation -- event tracking completeness, FTUE and monetization funnel coverage, retention metric infrastructure, A/B testing framework, heatmap data collection, churn prediction signals, and LTV modeling support..

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。

Audit rehabilitation setback prediction systems for clinical risk modeling and early intervention. Triggers: you need to evaluate risk factor models (Charlson, Elixhauser), early warning indicators for functional decline.

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 medication adherence and management platforms including dose tracking accuracy (MPR, PDC metrics), drug-drug and drug-food interaction checking completeness, refill prediction algorithms, dosage schedule optimization with conflict detection.

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 energy load forecasting systems including demand prediction models (ARIMA, Prophet, LSTM), weather API integration, peak shaving strategies, demand response program optimization, renewable intermittency handling, net load duck curve management.

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。

Audit litigation analytics and case outcome prediction systems -- ML outcome models (logistic regression, gradient boosting, neural nets with temporal train/test splits), settlement range modeling (Monte Carlo simulation, comparable case matching, BATNA 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。

Evaluate elder care fall risk prediction systems including wearable sensor integration, gait and balance analytics, risk scoring algorithms, environmental hazard detection, medication interaction flagging, mobility trend tracking, and alert/response workflow effectiveness.

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 demand forecasting systems — time-series decomposition (ARIMA, Prophet, ETS), seasonal and event-driven demand modeling, booking curve and pickup analysis, cancellation prediction, forecast accuracy metrics (MAPE, bias), and model retraining pipelines.

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。

Audit a tenant management system for eviction prevention and risk prediction. Triggers: building or reviewing property management platforms, affordable housing systems, or tenant services applications.

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 food supply chain systems for waste reduction opportunities including shelf life prediction models, FIFO and FEFO inventory rotation enforcement, demand forecasting accuracy and bias, donation logistics workflows, cold chain temperature monitoring, and sustainability reporting against EPA Food Recovery Hierarchy and UN SDG 12.3 targets.

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 donor retention and CRM systems — lapse risk scoring and prediction models, stewardship workflow automation, donor lifetime value (LTV) modeling, communication personalization engines, giving pattern analysis, and retention metric benchmarking.

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。

Audit a student information system for dropout risk prediction. Analyzes attendance patterns, grade trajectories, behavioral indicators, early warning model accuracy, intervention tracking, equity in risk scoring, and socioeconomic factor integration..

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。

Audit precision agriculture and crop management software for yield prediction model accuracy, soil analysis integration, irrigation optimization algorithms, pest and disease detection pipelines, satellite and drone imagery processing, weather data integration.

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 apparel demand prediction systems for trend forecasting, size curve optimization, color and style analytics, sell-through rate tracking, and markdown optimization following CPFR collaborative planning and GTIN product identification standards..

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 agricultural climate risk systems for weather impact modeling, crop insurance, drought/flood prediction, soil moisture, and carbon tracking. Triggers: 'assess crop climate risk', 'evaluate weather yield models', 'review crop insurance integration'.

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 or refine a research hypothesis. Use when the user says "write a hypothesis", "frame my hypothesis", "is my hypothesis testable", "null and alternative hypothesis", "research hypothesis", "refine my hypothesis", "hypothesis statement", "H0 and H1", "operationalize my hypothesis", "turn my research question into a hypothesis", or needs to convert a research question or intuition into a falsifiable, testable hypothesis with clear variables and predictions - even if they don't explicitly say "hypothesis".

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 the user asks about using Gemini in an enterprise environment or explicitly mentions Vertex AI, Google Cloud, or Agent Platform. Guides the usage of the Gemini API on Agent Platform with the Google Gen AI SDK. Covers SDK usage (Python, JS/TS, Go, Java, C#), capabilities like multimodal inputs, tools, media generation, caching, batch prediction, and Live API.

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。

Designing data stories and narrative-driven analytics in Salesforce: structuring CRM Analytics dashboards for executive communication, text widgets and conditional highlights for annotation, sequential narrative in Tableau Story sheets, and Einstein Discovery narrative REST endpoints. Trigger keywords: data storytelling dashboard, executive analytics narrative, CRM Analytics text widget annotation, Tableau story sheet, smart data discovery narrative API. NOT for building the Einstein Discovery story or prediction definition - use admin/einstein-discovery-setup. NOT for choosing between Reports, CRM Analytics and Tableau - use admin/einstein-analytics-basics.

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。

CRM strategist AI — RFM segmentation, lifecycle, churn prediction, win-back สำหรับธุรกิจไทย

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。

Guides the usage of the Gemini API on Agent Platform with the Google Gen AI SDK. Use when the user asks about using Gemini in an enterprise environment or explicitly mentions Vertex AI, Google Cloud, or Agent Platform. Covers SDK usage (Python, JS/TS, Go, Java, C#), capabilities like Live API, tools, multimedia generation, caching, and batch prediction.

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。

A-share short-term trading decision skill for 1-5 day horizon. Use when you need real-data market sentiment, sector rotation, strong stock scanning, capital flow confirmation, date-based short-term signal scoring, prediction logging, and next-day market comparison for CN A-share momentum trading.

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。

Deep analysis of competitor patent claims: element decomposition, scope interpretation, equivalents prediction, prosecution history estoppel analysis. Automatically converts PDF/DOCX/DOC patent files to structured Markdown via patent-pdf-to-md skill before analysis. Invoke when user asks to analyze/parse competitor patent claims, decompose claim elements, assess claim scope, or perform 权利要求解析/要素分解/保护范围解读. 竞品专利权利要求深度解析：要素分解、保护范围解读、等同范围预判、审查历史限制分析。自动调用patent-pdf-to-md技能将PDF/DOCX/DOC专利文件转换为结构化Markdown后再进行分析。当用户要求解析竞品专利权利要求、要素分解、保护范围时触发。

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。

Performs alchemical free-energy calculations including relative binding free energy (RBFE / FEP+) and absolute binding free energy (ABFE) via OpenFE, FEP+, GROMACS, AMBER pmemd, and OpenMM with explicit lambda scheduling, soft-core potentials, MBAR/BAR analysis, cycle-closure validation, and protocol-appropriate enhanced sampling. Compares ML alternatives (Boltz-2 affinity, DeepDock). Use when ranking analogs by binding affinity beyond docking accuracy, performing prospective lead optimization, or validating SAR predictions.

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。

Query the Ensembl REST API for gene/transcript/protein lookup, sequence retrieval, comparative genomics (Compara), variant effect prediction (VEP), regulatory features, and cross-species ortholog/paralog calls. Use when pulling Ensembl-native data (Ensembl Gene IDs, version-pinned releases, archive endpoints for reproducibility), gene/transcript/exon structure with stable IDs, or VEP for variant annotation. Encodes the 15 req/sec rate limit, archive (e110.rest.ensembl.org) for reproducibility, Ensembl divisions (vertebrates / plants / fungi / metazoa / bacteria), and the symbol-vs-ID stability problem.

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。

Tests genotype-environment associations and identifies adaptive loci while correcting for the four-confound landscape (structure, demography, background selection, sampling design) using LFMM2 with mandatory K via sNMF cross-entropy elbow (LEA 3), BayPass Core/AUX/C2/IS with Omega covariance matrix, RDA / pRDA for polygenic adaptation (Forester 2018; requires imputed genotypes), OutFLANK with trimmed FST null, pcadapt, gradient forests (Ellis-Smith-Pitcher 2012, NOT mis-cited Ellis-Manel), Capblancq & Forester 2021 RDA Swiss-army-knife, genomic-offset prediction with Lind & Lotterhos 2025 three-regime caveat, Lotterhos-Whitlock sampling optima, Wang & Bradburd 2014 IBD vs IBE, and Circuitscape + ResistanceGA. Use when identifying adaptive loci across gradients, choosing K for LFMM2, deciding among GEA methods, predicting maladaptation with the novel-environment caveat, distinguishing IBD vs IBE, or optimizing sampling design.

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。