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Found 285 Skills
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.
Specifies requirements for an analytics dashboard including metrics, visualizations, filters, and data sources. Use when requesting dashboards from data teams, defining KPI tracking, or documenting reporting needs.
All-in-one Assistant for Data Analysis and Office Productivity. Covers end-to-end workflows including data processing, analytical insights, report writing, PPT creation, and data visualization. Always approach from an expert perspective and think one step ahead for users. Proactively confirm with users when encountering uncertain issues. Supported features: Excel data analysis, campaign data review, ROI calculation, data visualization, report generation, PPT creation, formula generation. Use this skill when users mention terms like "analyze data", "create report", "make PPT", "Excel", "campaign analysis", "ROI", "review", "weekly report", "monthly report", "data processing", "chart", "visualization", "presentation", "spreadsheet", "formula".
Statistical visualization. Scatter, box, violin, heatmaps, pair plots, regression, correlation matrices, KDE, faceted plots, for exploratory analysis and publication figures.
Data visualization with Recharts 3.x including responsive charts, custom tooltips, animations, and accessibility for React applications. Use when building charts or dashboards with Recharts.
Designs effective KPI dashboards with proper metric selection, visual hierarchy, and data visualization best practices. Use when building executive dashboards, creating analytics views, or presenting business metrics.
Build production-grade interactive dashboards with Plotly Dash - enterprise features, callbacks, and scalable deployment
Detect whether U.S. inflation pressure is entering a slowdown or reversal phase through the cycle turning points of the CASS Freight Index. It is used to judge whether 'inflation is cooling down' and verify whether the market's macro narrative of interest rate cuts and inflation decline is supported by real economic data.
Calculate the deviation of asset prices relative to the long-term exponential growth trend line, assess whether the current period falls within a historical extreme range, and optionally perform macro factor analysis to evaluate the market regime.
Provides comprehensive guidance for Lime ECharts including chart creation, configuration, data visualization, and interactive charts. Use when the user asks about Lime ECharts, needs to create charts, visualize data, or work with ECharts features.
A Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics. Great for exploring relationships between variables and visualizing distributions. Use for statistical data visualization, exploratory data analysis (EDA), relationship plots, distribution plots, categorical comparisons, regression visualization, heatmaps, cluster maps, and creating publication-quality statistical graphics from Pandas DataFrames.
Analyze datasets to discover patterns, anomalies, and relationships. Use when exploring data files, generating statistical summaries, checking data quality, or creating visualizations. Supports CSV, Excel, JSON, Parquet, and more.