141건 · 관련도순
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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'.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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".
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
FutureSearch: Search the Future — AI forecasting with a public track record. First in Metaculus's Summer 2026 FutureEval tournament, above the superfo
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Build AI demand forecasting systems. TRIGGERS - Use when user needs help with ai-demand-forecaster related tasks.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Records and evaluates event-specific probability forecasts for agent outputs. NOT for validation or automatic acceptance.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Complete inventory management, demand forecasting, supplier evaluation, and supply chain optimization for businesses of any size. From stockroom to strategy.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.