Data & Analytics

🤖 Data Science & AI/ML skill suite derived from VoltAgent/awesome-agent-skills. Data pipelines, model training, evaluation, MLOps and analytical reporting. Provides 10 specialised commands for data-science, machine-learning, analytics workflows.

Create/edit/analyze/verify spreadsheet workbooks including XLSX/CSV/TSV.

Compatibility alias for the xlsx skill. Prefer loading xlsx.

Choose and produce an appropriate chart/dashboard/visual explanation from data, with legible encodings and source/assumption notes.

Create/edit/analyze/verify spreadsheet workbooks including XLSX/CSV/TSV.

Compatibility alias for the xlsx skill. Prefer loading xlsx.

Choose and produce an appropriate chart/dashboard/visual explanation from data, with legible encodings and source/assumption notes.

Create/edit/analyze/verify spreadsheet workbooks including XLSX/CSV/TSV.

Choose and produce an appropriate chart/dashboard/visual explanation from data, with legible encodings and source/assumption notes.

Correlation-regime detection and crisis attribution — edge-density regime states with hysteresis, causal (no look-ahead) smoothing, regime-aware exposure context, first-mover crisis attribution with honest NAME / MACRO / AMBIGUOUS / ABSTAIN verdicts, and a correlation-rewiring leaderboard that catches slow bleed-outs

CLI harness for NSLogger — parse, filter, export, and monitor NSLogger log files (.rawnsloggerdata / .nsloggerdata)

Design a service-learning project connecting genuine community need with embedded curriculum learning. Use when planning community projects, civic engagement, or social action units.

This skill should be used when the user asks to "analyze experimental results", "run strict statistical analysis", "compare model performance", "generate scientific figures", "check significance", "do ablation analysis", or mentions interpreting experiment data with rigorous statistics and visualization. It focuses on strict analysis bundles, not Results-section prose.

This skill should be used when the user asks for a publication-quality scientific figure or table, wants help choosing the right chart for results, needs a paper-ready pubfig or pubtab workflow, wants a figure + companion table for a results section, wants an Excel sheet turned into publication-ready LaTeX, or wants an existing scientific figure/table reviewed and upgraded.

Cleans a weekly CRM export and produces a regional sales summary. Activates when the user asks to clean a CRM export, deduplicate sales rows, calculate regional totals, or generate a weekly sales report from a CSV.

Generates a publication-ready heatmap for differentially expressed genes (DEGs) or marker genes using viridis colormap, column-only hierarchical clustering, and Arial font — applicable to any normalized gene expression matrix with genes as rows and samples/subclusters as columns.

Automates catalyst activity analysis from CSV: computes per-sample mean conversion or degradation rate after 3-sigma outlier removal within each group, anchors 'Control' (case-insensitively detected) at the first bar position with distinct gray styling, sorts remaining samples by descending mean activity, generates a publication-ready bar chart with Chinese/English label support, and annotates samples with mean <5% as 'INACTIVATION ZONE'. Supports catalytic conversion and photocatalytic degra...

专门处理SEEG及实验数据的Python/Pandas助手，支持宽表转长表转换、时间格式转换、行间减法运算及指定格式的表格生成。

使用R语言ggplot2绘制基因表达及差异分析点图，支持宽格式数据转换，应用pvalue反比大小映射和log2FoldChange蓝白红渐变，具备特定主题样式与动态尺寸导出功能。

Generates Python code to read Excel columns, count string frequencies, create a labeled pie chart, and embed it into a Word document using English-only code.

Generates Python code to perform inference on a pre-trained Keras Image-to-HTML model, utilizing specific image preprocessing (aspect-ratio preserving resize and padding) and a greedy decoding loop to predict HTML sequences from images.

Generates a Python script to fine-tune a DistilBert model on a JSONL dataset containing 'question' and 'answer' columns. The script uses manual label mapping (avoiding sklearn), includes progress logging, error handling, and model evaluation.

Extracts seasonality features (specifically STL features) from a panel time series dataset to determine the optimal season length for forecasting models. Handles conversion from Polars to Pandas and ensures correct data formatting.

Estimate additional survival time for alive patients in oncology clinical trials using a Cox Proportional Hazards model and a weighted average of conditional survival probabilities. Includes data simulation and step-by-step statistical explanation.