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Download and generate plots from Weights & Biases runs. Use when you need to: - List projects you have access to - List runs in a W&B project - Inspect available metrics for a run - Download existing plot images from a run - Generate line plots from metric history (loss, accuracy, etc.)
Analyzes CSV files, generates comprehensive summary statistics, identifies data patterns, and creates visualizations using Python and pandas. Automatically adapts analysis based on data type (sales, customer, financial, survey, operational).
This skill should be used when the user asks "how to create a line chart", "Chart.js bar chart", "pie chart Chart.js", "doughnut chart", "radar chart", "polar area chart", "bubble chart", "scatter chart", "mixed chart", "combo chart", "area chart", "stacked chart", "horizontal bar chart", "Chart.js chart types", "dataset properties", "chart data structure", or needs help implementing specific Chart.js v4.5.1 chart types.
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ะญะบัะฟะตัั ะฟะพ compliance ะพััััะฐะผ. ะัะฟะพะปัะทัะน ะดะปั SOX, GDPR, HIPAA, SOC 2 ะฐัะดะธัะพะฒ ะธ ะดะพะบัะผะตะฝัะฐัะธะธ ัะพะพัะฒะตัััะฒะธั.
Sorting and searching algorithms including O(n) partitioning, binary search, and hierarchical multi-key sorting. Triggers: sort, argsort, partition, searchsorted, lexsort, nan sort order.
Developing Heart Atlas notebooks converted to HTML (local)
Generate and interpret forest plots for meta-analysis visualization using R and the metafor package. Use when users need to create forest plots, understand visual representation of pooled effects, or interpret study weights and confidence intervals.
Create interactive data visualizations using D3.js for charts, graphs, maps, and custom visual analytics
Expert guidance for Polars dataframe manipulation in Python. Use this skill when working with dataframes, data processing, ETL pipelines, or any task involving tabular data manipulation. Provides best practices, performance optimization patterns, and comprehensive API usage for the Polars library.
Assistant for creating, editing, and debugging reactive Python notebooks with marimo. Use when you need to build marimo notebooks, debug reactive execution, add interactive UI elements, or convert traditional notebooks to marimo format. Provides code patterns, utility functions, and best practices for marimo development.
Test at extremes (1000x bigger/smaller, instant/year-long) to expose fundamental truths hidden at normal scales
World-class data science skill for statistical modeling, experimentation, causal inference, and advanced analytics. Expertise in Python (NumPy, Pandas, Scikit-learn), R, SQL, statistical methods, A/B testing, time series, and business intelligence. Includes experiment design, feature engineering, model evaluation, and stakeholder communication. Use when designing experiments, building predictive models, performing causal analysis, or driving data-driven decisions.
Linear algebra operations in NumPy, including matrix multiplication, SVD, system solving, and least squares fitting. Triggers: linalg, matrix multiplication, SVD, eigenvalues, matrix decomposition, lstsq, multi_dot.
Reference for how I like to dance and call square dances.
Build a complete data clustering and visualization pipeline from any data source. Use when the user wants to analyze patterns in text data (GitHub issues, Slack messages, support tickets, code reviews, forum posts, customer feedback, etc.), cluster similar items, or build an interactive visualization to explore the patterns. Triggers on: "cluster", "analyze patterns", "group similar", "clio-style", "pattern analysis", "visualize clusters", "find themes", "topic modeling", "semantic clustering".
MuData ๅคๆจกๆๆฐๆฎๅๆๅทฅๅ ทๅ - 100%่ฆ็ๆๆกฃ๏ผAPI+ๆ็จ+IOๆๅ+ๆ ธๅฟๅ่ฝ๏ผ
This skill should be used when performing local data exploration, profiling, quality analysis, or transformation tasks using DuckDB. It handles CSV, Parquet, and JSON files, provides automated data quality reports, supports complex JSON transformations, and generates interactive HTML reports for data analysis.
Analyze Excel spreadsheet formulas to build dependency DAGs (Directed Acyclic Graphs) and understand calculation chains. This skill should be used when the user wants to reverse-engineer Excel formula dependencies, trace how values are calculated from inputs to outputs, validate formula logic, or create reusable calculators from spreadsheet logic.
Generate a markdown summary report from candidate_profile.csv with statistics and insights
Debug Pandas issues systematically. Use when encountering DataFrame errors, SettingWithCopyWarning, KeyError on column access, merge and join mismatches with unexpected NaN values, memory errors with large DataFrames, dtype conversion issues, index alignment problems, or any data manipulation errors in Python data analysis workflows.
