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Revenue Intelligence setup: activating the RI app and its CRM Analytics assets, Einstein deal insights, the opportunity-change waterfall, and forecast-accuracy analytics for sales leaders. NOT for configuring the Pipeline Inspection view, its metrics and review cadence - use admin/pipeline-review-design. NOT for forecast types, categories, rollups and quotas - use admin/collaborative-forecasts.
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Automate AR workflows: aging analysis, collection prioritization, payment follow-ups, cash application, and bad debt forecasting.
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Analyze design trends and recommend appropriate styles, color palettes, and typography systems for your project based on current design movements. NOT for accessibility auditing or full design system creation.
中文简介正在准备中。当前显示原文说明。
Strategic analyst that maps competitive landscapes, identifies white space opportunities, and provides positioning recommendations. Use when users need competitive analysis, market positioning strategy, differentiation tactics, or "how do I stand out?" guidance across any domain (portfolios, products, services). NOT for market size estimation or financial forecasting.
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This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.
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Use when constructing the finance-side forecast — top-down vs bottom-up shape, confidence bands, retro-loop. Triggers on 'build the forecast model', 'reconcile top-down with bottom-up'.
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Revenue leadership for B2B SaaS companies. Revenue forecasting, sales model design, pricing strategy, net revenue retention, and sales team scaling. Use when designing the revenue engine, setting quotas, modeling NRR, evaluating pricing, building board forecasts, or when user mentions CRO, chief revenue officer, revenue strategy, sales model, ARR growth, NRR, expansion revenue, churn, pricing strategy, or sales capacity.
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Distributed machine learning, data mining, and iterative HPC with Exasol. Covers end-to-end ML pipelines (DISTRIBUTE BY + SET scripts + BucketFS), per-entity federated training with partial_fit and ctx.reset(), batch inference, map-reduce ensemble training, distributed ensemble and SON algorithm for frequent itemset mining (Apriori, FP-Growth, association rules, market-basket analysis), Lua execute script orchestration for iterative algorithms (k-means, SGD, gradient descent), scikit-learn model training, parallel hyperparameter search, per-entity forecasting, anomaly detection, model lifecycle in BucketFS (pickle/joblib/ONNX versioning), GPU acceleration via CUDA SLCs (PyTorch/TensorFlow/RAPIDS), and ML-specific performance tuning (skew, OOM, multi-pass chunking).
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サプライチェーン最適化コンサルティングスキル。需要予測、在庫最適化、調達戦略、 物流ネットワーク設計、S&OP(Sales and Operations Planning)を支援。 Use when optimizing supply chain operations, improving inventory management, designing logistics networks, or conducting supply chain risk assessments. Triggers: "supply chain", "在庫最適化", "inventory optimization", "demand forecasting", "S&OP", "procurement strategy", "logistics network".
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Comprehensive data science workflow skill for analyzing tabular and time series data. Use when conducting exploratory data analysis, building predictive models, performing feature engineering, or generating data-driven insights. Supports end-to-end analysis from data understanding through model deployment, following industry best practices for regression, classification, time series forecasting, and statistical analysis.
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FutureSearch: Search the Future — AI forecasting with a public track record. First in Metaculus's Summer 2026 FutureEval tournament, above the superfo
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Knowledge base from the NIST/SEMATECH e-Handbook of Statistical Methods (NIST HB 151) — the practical statistics reference for engineering, metrology, and quality. Use for: exploratory data analysis and the four univariate assumptions (4-plot); measurement process characterization including bias/precision, calibration designs, gauge R&R, and ISO/GUM uncertainty budgets; production process characterization (stability vs. capability); process modeling and regression (LS/WLS/NLS/LOESS); design of experiments (screening, fractional/factorial, response-surface, Taguchi); statistical process control (Shewhart/CUSUM/EWMA charts, capability indices, acceptance sampling); product/process comparisons (hypothesis tests and confidence intervals for 1/2/3+ groups, ANOVA, multiple comparisons); and reliability (lifetime & repair-rate models, accelerated testing, reliability growth). Scope limits: this is applied frequentist statistics for measurement and quality — it does NOT reproduce the per-distribution formula galleries, worked case studies, datasets, plot images, or Dataplot/R code of the original web Handbook (those are described, not copied); it is thin on modern machine learning, Bayesian methods beyond conjugate reliability priors, time-series/forecasting, and Bayesian experimental design.
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Use when working with Apptio — apptio and Cloudability IT financial management and cloud cost optimization. Covers IT cost allocation, cloud cost analysis, benchmarking, budget forecasting, showback/chargeback reports, and technology business management. Use when managing IT financial planning, benchmarking cloud spend, or creating chargeback models.
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Use when working with Aws Billing — analyze, break down, and report AWS costs and bills. Covers cost breakdown by service, account, or usage type; monthly/daily billing trends; cost anomaly detection; RI/SP utilization; cost forecasting; credit/discount analysis; and multi-account cost comparison. Uses anti-hallucination rules, mandatory currency/credit detection workflow, and reusable Cost Explorer functions.
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This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.
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Build AI demand forecasting systems. TRIGGERS - Use when user needs help with ai-demand-forecaster related tasks.
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Comprehensive dimension analysis and reporting for CJA. Use this skill whenever the user wants to analyze one or more dimensions — including cardinality, distribution/skew, trends, anomalies, data quality errors, comparisons, and forecasting. Also trigger when someone asks "what are the top values for...", "dimension health", "explore this dimension", "dimension dashboard", "dimension statistics", "data quality check on a dimension", "dimension cardinality", "dimension trends", "dimension skew", "dimension anomalies", "compare dimensions", or any similar request to understand what's inside a CJA dimension. Produces an interactive HTML dashboard or a markdown report. Works with the CJA MCP server.
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Analyze design trends and recommend appropriate styles, color palettes, and typography systems for your project based on current design movements. NOT for accessibility auditing or full design system creation.
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Records and evaluates event-specific probability forecasts for agent outputs. NOT for validation or automatic acceptance.
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Strategic analyst that maps competitive landscapes, identifies white space opportunities, and provides positioning recommendations. Use when users need competitive analysis, market positioning strategy, differentiation tactics, or "how do I stand out?" guidance across any domain (portfolios, products, services). NOT for market size estimation or financial forecasting.
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Expert sales execution covering pipeline management, discovery, demos, negotiation, and deal closing. Use when qualifying opportunities, running MEDDIC discovery, building account plans, handling objections, structuring proposals, or forecasting pipeline.
中文简介正在准备中。当前显示原文说明。
Comprehensive dimension analysis and reporting for CJA. Use this skill whenever the user wants to analyze one or more dimensions — including cardinality, distribution/skew, trends, anomalies, data quality errors, comparisons, and forecasting. Also trigger when someone asks "what are the top values for...", "dimension health", "explore this dimension", "dimension dashboard", "dimension statistics", "data quality check on a dimension", "dimension cardinality", "dimension trends", "dimension skew", "dimension anomalies", "compare dimensions", or any similar request to understand what's inside a CJA dimension. Produces an interactive HTML dashboard or a markdown report. Works with the CJA MCP server.
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Complete inventory management, demand forecasting, supplier evaluation, and supply chain optimization for businesses of any size. From stockroom to strategy.
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