Total 55,012 skills, Data Processing has 2818 skills
Showing 12 of 2818 skills
Solve the newsvendor problem for single-period ordering decisions under uncertain demand. Use this skill when the user needs to determine optimal order quantity for perishable goods, seasonal products, or one-time purchase decisions — even if they say 'how much to order for this season', 'perishable inventory', or 'single-period ordering'.
Summarizes descriptive concepts for max pain options theory, covered-call style crypto ETFs, crypto arbitrage families and risks, and bull/bear flag chart patterns—always as non-prescriptive education. Use when the user asks about max pain, premium income ETFs, arbitrage, funding rates, flash loans, or bull/bear flags in crypto trading context.
Bayesian statistical modeling with PyMC v5+. Use when building probabilistic models, specifying priors, running MCMC inference, diagnosing convergence, or comparing models. Covers PyMC, ArviZ, pymc-bart, pymc-extras, nutpie, and JAX/NumPyro backends. Triggers on tasks involving: Bayesian inference, posterior sampling, hierarchical/multilevel models, GLMs, time series, Gaussian processes, BART, mixture models, prior/posterior predictive checks, MCMC diagnostics, LOO-CV, WAIC, model comparison, or causal inference with do/observe.
Generate high-density information graphics, data visualizations, and blueprint-style infographics. Use when: "信息图生成", "数据可视化", "infographic", "蓝图风格", "长图制作", "data visualization", "信息图表", "可视化报告", "vision蓝图", "技术架构图", "思维导图", "知识图谱", "timeline图", "流程图可视化". Creates visually striking, information-dense graphics suitable for technical documentation, architecture diagrams, and knowledge sharing. Part of UniqueClub content toolkit. Learn more: https://uniqueclub.ai
港股市场全量数据服务。编排港股所有细粒度 skill：实时行情、K线、基础数据、港股通、技术指标。 当用户提及"港股""HKEX""恒生""腾讯"且未明确指定数据维度时触发。
Analyze A-share stocks using fixed scripts, supporting the maintenance of local watchlists and position pools, fetching individual stock and concept sector data, querying risks such as financial report disclosures and share reduction announcements, and outputting structured recommendations including Buy, Watch, Hold, Reduce Position, and Sell. Suitable for scenarios where you need to update local stock pools, fetch real-time data, compare the top three strongest stocks in a concept sector, or generate transaction analysis with risk prompts.
Implement end-to-end Medallion Architecture (Bronze/Silver/Gold) lakehouse patterns in Microsoft Fabric using PySpark, Delta Lake, and Fabric Pipelines. Use when the user wants to: (1) design a Bronze/Silver/Gold data lakehouse, (2) set up multi-layer workspace with lakehouses for each tier, (3) build ingestion-to-analytics pipelines with data quality enforcement, (4) optimize Spark configurations per medallion layer, (5) orchestrate Bronze-to-Silver-to-Gold flows via notebooks. Triggers: "medallion architecture", "bronze silver gold", "lakehouse layers", "e2e data pipeline", "end-to-end lakehouse", "data lakehouse pattern", "multi-layer lakehouse", "build medallion", "setup medallion".
This skill guides the use of Jupyter notebooks for data analysis, exploration, and visualization, particularly with BigQuery. It outlines best practices for notebook execution and validation (supporting both cell-by-cell execution and full notebook generation depending on tool availability), library installation, and structuring notebooks for clarity. It also covers specific rules for data cleaning, plotting, and integrating with BigQuery SQL and machine learning workflows. Relevant when any of the following conditions are true: 1. The user request involves a data analysis, data exploration, data visualization, or data insights task that requires multiple steps, queries, or visualizations to answer. 2. The user explicitly requests a notebook (.ipynb). 3. You are creating, editing, or executing cells in a Jupyter notebook. 4. You need to query BigQuery from within a notebook. DO NOT use the Python BigQuery client library; instead, you MUST use the `%%bqsql` magics explained in this skill.
Skill for BigQuery AI and Machine Learning queries using standard SQL and `AI.*` functions (preferred over dedicated tools).
Build PyGraphistry visualizations with bindings, encodings, layout controls, static export, and privacy-aware sharing. Use for color/size/icon/badge styling, layout tuning, map/static output, and plot link sharing workflows.
Deep dive on a PostHog user by email address. Analyze what they do, where they spend time, and what products they use.
Use this skill when you need to create or modify a LookML Model file (.model.lkml). This includes defining connections, includes, and configuring model-level settings.