visualization
Use when "data visualization", "plotting", "charts", "matplotlib", "plotly", "seaborn", "graphs", "figures", "heatmap", "scatter plot", "bar chart", "interactive plots"
Use when "data visualization", "plotting", "charts", "matplotlib", "plotly", "seaborn", "graphs", "figures", "heatmap", "scatter plot", "bar chart", "interactive plots"
Teach network meta-analysis (NMA) for comparing multiple treatments simultaneously. Use when users need to compare more than two interventions, understand indirect comparisons, or create network plots and league tables.
Basketball statistics formatting using BasketballStats\StatsFormatter for percentages, averages, and totals. Use when displaying stats, calculating averages, or formatting basketball numbers.
Generate R code for meta-analysis using the metafor package, including data preparation, model fitting, visualization, and sensitivity analyses. Use when users need executable R code for their meta-analysis workflow.
Diagnose and debug issues in the vehicle insurance data analysis platform. Use when user encounters errors, data not refreshing, filters not working, charts not displaying, API failures, or performance issues. Provides quick diagnostic checklists and proven troubleshooting steps specific to this Vue 3 + Flask + Pandas stack.
华安保险车险周报自动生成器(麦肯锡风格)。将周度车险数据(支持Excel/CSV/JSON/DuckDB)转化为12-13页董事会级经营分析报告。采用问题导向标题、16:9宽屏、四象限/气泡图可视化、深红#a02724配色。报告结构:经营概览、保费进度、变动成本、损失暴露、费用支出,每个章节分机构和分客户类别双维度分析。支持自定义阈值配置和保费计划。触发场景:用户上传车险周报数据文件(.xlsx/.csv/.json/.db),要求生成董事会汇报PPT。
Assess and interpret between-study heterogeneity in meta-analysis using I², Q statistic, tau², and prediction intervals. Use when users need to evaluate consistency across studies, understand sources of variation, or decide if pooling is appropriate.
Use when working with R ggplot2 package, especially ggplot2 4.0+ features. Covers S7 migration (@ property access), theme defaults with ink/paper/accent, element_geom(), from_theme(), theme shortcuts (theme_sub_*), palette themes, labels with dictionary/attributes, discrete scale improvements (palette, continuous.limits, minor_breaks, sec.axis), position aesthetics (nudge_x/nudge_y, order), facet_wrap dir/space/layout, boxplot/violin/label styling, stat_manual(), stat_connect(), coord reversal.
Analyze CSV data files and interpret figures to generate the Results section. Fifth step of writer workflow. Requires scope.md, data/ folder with CSVs, and figures/ folder with images.
Review individual V2 transaction MDX documentation for coverage and accuracy
Dashboard symbol_signals uses parallel lists (symbols[], signal_values[], gate_statuses[]) not dict keyed by symbol. Trigger when: (1) 'list' object has no attribute 'get', (2) .items() on symbol_signals fails.
R 4.4+ best practices with testthat 3.2, lintr 3.2, and data analysis
Create parabola plots using R with customizable coefficients, range, styling, and annotations. Use for mathematical visualization, quadratic function analysis, and educational demonstrations of parabolic curves.
Comprehensive validation of FTD analysis output files. Checks JSONL format, field completeness, score validity, and structural requirements for TALD scale evaluations.
华安保险车险周报HTML可视化生成器。将车险周报数据(Excel/CSV/JSON/DuckDB)转化为交互式网页仪表盘,支持标签页切换(经营概览、保费进度、变动成本、损失暴露、费用支出)和下钻分析(机构/客户类别双维度)。采用ECharts图表、响应式布局、麦肯锡配色方案。触发场景:用户上传车险周报数据文件,要求生成HTML可视化网页、交互式仪表盘或网页版报告。
Эксперт анализа распределений. Используй для statistical distributions, data analysis и hypothesis testing.
Use when organizing experiment logs, results, and metadata for Python research code.
Scaffolds a high-performance scientific GUI application using a Data-Centric Architecture (PyQt+Visualization).
Comprehensive guide for TAR UMT Data Science (RDS) students completing their Final Year Project. Use when RDS students need help with (1) understanding FYP processes and requirements, (2) structuring FYP reports and deliverables, (3) writing research-based chapters and thesis, (4) selecting appropriate data science projects, (5) understanding research methodology and theoretical frameworks, (6) conducting experiments and statistical analysis, (7) preparing for system testing and presentations, (8) meeting submission deadlines and formats, (9) report formatting and structure requirements, or (10) any other FYP-related guidance specific to Data Science students at TAR UMT.
Set-theoretic operations for finding unique elements, membership testing, and array intersections. Triggers: unique, isin, intersect1d, setdiff1d, union1d.
Jupyter notebook operations: editing cells, reading notebooks, executing, and format conversion. Trigger when working with .ipynb files for: (1) Creating/editing/deleting/reordering cells, (2) Reading notebook content, (3) Executing notebooks with papermill, (4) Converting to HTML/PDF/script formats. Supports Cursor EditNotebook tool, Jupytext workflows, and nbformat.
Use when animating charts, graphs, dashboards, data transitions, or any information visualization work.