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Found 213 Skills
Analyze datasets to extract insights, identify patterns, and generate reports. Use when exploring data, creating visualizations, or performing statistical analysis. Handles CSV, JSON, SQL queries, and Python pandas operations.
Perform comprehensive exploratory data analysis on scientific data files across 200+ file formats. This skill should be used when analyzing any scientific data file to understand its structure, content, quality, and characteristics. Automatically detects file type and generates detailed markdown reports with format-specific analysis, quality metrics, and downstream analysis recommendations. Covers chemistry, bioinformatics, microscopy, spectroscopy, proteomics, metabolomics, and general scientific data formats.
Analyze spreadsheet data, generate insights, create visualizations, and build reports from Excel/CSV data.
Use this skill any time the user wants to analyze data, create charts, or build data visualizations. This includes: sales analysis, financial modeling, cohort analysis, funnel analysis, A/B test results, KPI tracking, data reports, revenue breakdowns, user retention analysis, conversion rate analysis, CSV summarization, and dashboard creation. Also trigger when: user says 分析这组数据, 做个图表, 数据可视化, 销售分析, 漏斗分析, 留存分析, 做个数据报表. If data needs to be analyzed or visualized, use this skill.
Executive-grade data analysis with pandas/polars and McKinsey-quality visualizations. Use when analyzing data, building dashboards, creating investor presentations, or calculating SaaS metrics.
Generate statistical analysis code with 4-round review. Select appropriate statistical tests, interpret results, and produce analysis reports with p-values, effect sizes, and confidence intervals. Use when analyzing experimental data for a paper.
Data analysis and statistical computation. Use when user needs "数据分析/统计/计算指标/数据洞察". Supports general analysis, financial data (stocks, returns), business data (sales, users), and scientific research. Uses pandas/numpy/scikit-learn for processing. Automatically activates data-base for data acquisition.
Expert guidance for data analysis, visualization, and Jupyter Notebook development with pandas, matplotlib, seaborn, and numpy.
Use this skill when the user uploads Excel (.xlsx/.xls) or CSV files and wants to perform data analysis, generate statistics, create summaries, pivot tables, SQL queries, or any form of structured data exploration. Supports multi-sheet Excel workbooks, aggregation, filtering, joins, and exporting results to CSV/JSON/Markdown.
Data analysis, visualization, and storytelling skill for financial and RevOps contexts. Use when: analyzing revenue data, building forecasts, cohort analysis, churn modeling, pipeline analytics, creating data-driven reports, building dashboards, cleaning messy data, sanity-checking analytical claims, exporting to Excel with formulas, or extracting data from PDFs. Features decision logging, bias-aware interpretation, and progressive disclosure (slide deck -> detailed report -> full notebook with all decisions documented).
Implement analytics, data analysis, and visualization best practices using Python, Jupyter, and modern data tools.
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.