Data & Analytics

Automated cross-platform arbitrage detection and monitoring

Performance attribution, trade analytics, and strategy optimization

Patterns for building dark-themed financial charts and data visualizations. Covers chart theming, color scales for gains/losses, and real-time data display. Use when building trading dashboards or financial analytics. Triggers on chart theme, data visualization, financial chart, dark theme, gains losses, trading UI.

Extract structured data from scientific literature across multiple formats (PDF, HTML, images, plain text). Auto-detects scientific domain to recommend specialized tools for chemistry/materials when appropriate. Use this skill when: extracting numerical data from papers, digitizing graphs/plots, parsing tables from PDFs, extracting chemical properties or reactions, or converting unstructured scientific text to structured formats. Key capabilities: format detection and routing, domain-specif...

Expert guidance for regression analysis, statistical modeling, and outlier detection in Python using statsmodels, scikit-learn, scipy, and PyOD - includes model diagnostics, assumption checking, robust methods, and comprehensive outlier detection strategies

Comprehensive plotting and visualization in Python - matplotlib (static publication-quality plots), seaborn (statistical visualization), and plotly (interactive plots); includes plot types, customization, best practices, and library selection guidance

Comprehensive guidance for using pymatgen (Python Materials Genomics) for computational materials science. Covers structure creation and manipulation, file I/O (CIF, POSCAR, XYZ), symmetry analysis, Materials Project API integration, phase diagrams, electronic structure analysis, and DFT input generation. Use when working with crystal structures, materials properties, computational chemistry calculations, or materials databases. Triggers include 'pymatgen', 'crystal structure', 'Materials Pro...

Expert guidance for pycalphad - computational thermodynamics library implementing the CALPHAD method for calculating phase diagrams, phase equilibria, and thermodynamic properties of multicomponent materials systems using thermodynamic databases (TDB files)

Expert guidance for Meta's FAIRChem library - machine learning methods for materials science and quantum chemistry using pretrained UMA models with ASE integration for fast, accurate predictions

Static 3D visualization utilities wrapping Rerun SDK for adding point clouds, trajectories, cameras, planes, and chessboards. Use when visualizing 3D data in Rerun, including SLAM trajectories, robot poses, camera calibration targets, and debug visualizations. All methods are static and do not require viewer instance management. For SE(3)/SO(3) matrix operations, use pywayne-vio-se3 or pywayne-vio-so3 skills.

3D visualization toolkit wrapping Pangolin viewer for real-time display of point clouds, trajectories, cameras, planes, chessboards, and images. Use when visualizing sensor data (IMU, SLAM, tracking), robot states, or any 3D data with camera poses and trajectories. Supports dual-image display, step mode for debugging, and main camera following.

Analyze and visualize Git commit time distribution. Use when users need to analyze Git repository commit patterns, generate commit statistics, visualize commit activity by time, hour, or weekday. Triggered by requests to analyze commits, show commit distribution, visualize Git activity, or generate commit time statistics.

全面的电子表格创建、编辑和分析功能，支持公式、格式化、数据分析和可视化。当 Claude 需要处理电子表格（.xlsx、.xlsm、.csv、.tsv 等）时使用，包括：(1) 创建带有公式和格式的新电子表格，(2) 读取或分析数据，(3) 修改现有电子表格同时保留公式，(4) 电子表格中的数据分析和可视化，或 (5) 重新计算公式

Work with Excel spreadsheets (XLSX/XLS/CSV) - read data, create spreadsheets, convert formats, analyze data, and generate reports. Use when the user asks to work with Excel files or spreadsheet data.

Automatically generate Excel reports from data sources including CSV, databases, or Python data structures. Supports data analysis reports, business reports, data export, and template-based report generation using pandas and openpyxl. Activate when users mention Excel, spreadsheet, report generation, data export, or business reporting.

Comprehensive YouTube channel analysis using YouTube Data API v3. Analyze your own channel's performance metrics, content strategy, upload patterns, engagement rates, video performance, and growth trends. Use when users want to (1) Analyze their YouTube channel performance, (2) Get insights on video engagement and metrics, (3) Understand upload patterns and optimal posting times, (4) Identify top-performing content types, (5) Generate channel health reports, (6) Track subscriber and view grow...

Find and analyze YouTube competitor channels using YouTube Data API v3. Discover competitors through keyword search, category matching, content similarity, and related channel discovery. Compare metrics, content strategies, and market positioning. Use when users want to (1) Find competitors for their YouTube channel, (2) Analyze competitor performance metrics, (3) Compare their channel against competitors, (4) Identify content gaps and opportunities, (5) Benchmark against similar creators, (6...

An experiment results document captures what happened when you tested a hypothesis, including statistical outcomes, segment analysis, learnings, and clear recommendations. Good results documentation turns individual experiments into organizational knowledge that improves future decision-making.

A dashboard requirements document specifies what questions a dashboard should answer, what metrics it displays, and how data should be visualized. Clear requirements help data teams build dashboards that actually inform decisions rather than just displaying numbers.

Detect chromatin loops and point interactions from Hi-C data using cooltools, chromosight, and HiCCUPS-like methods. Identify CTCF-mediated loops and enhancer-promoter contacts. Use when detecting chromatin loops from Hi-C data.

Generate and execute Python code to analyze large log datasets, detect patterns, and extract actionable insights

Current location context with nearby places and pattern insights. Use when checking where you are, understanding local context, finding nearby places, or getting location-aware information. Trigger words: location, where, nearby, place, context, here.

Loads internal CausalPy example datasets. Use when the user needs example data or asks about available demos.

Ultra-fast data discovery and loading skill for industrial finance datasets. Handles CSV, JSON, DuckDB, Parquet, and Feather with memory-efficient 2-step discovery and loading.