Data & Analytics

This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.

Expert data scientist for advanced analytics, machine learning, and statistical modeling. Handles complex data analysis, predictive modeling, and business intelligence.

Compute HEIM diversity and equity metrics from VCF or ancestry data. Generates heterozygosity, FST, PCA plots, and a composite HEIM Equity Score with markdown reports.

Ancestry decomposition PCA against the Simons Genome Diversity Project

Analyzes campaign performance with multi-touch attribution, funnel conversion, and ROI calculation for marketing optimization

When the user needs to identify abnormal sensor readings by comparing real-time IoT sensor data against predefined operating parameter ranges. This skill queries time-series sensor data from BigQuery, loads parameter ranges from Excel configuration files, performs range validation, and generates anomaly reports in CSV format with timestamp, machine_id, sensor_type, reading, and normal_range fields. Triggers include IoT sensor data analysis, anomaly detection, operating parameter validation, f...

When the user needs to monitor product inventory levels, check stock quantities against safety thresholds, identify low-stock items, and trigger automated notifications and documentation updates. This skill is triggered by keywords like 'inventory levels', 'stock quantity', 'safety threshold', 'low-stock', 'purchase requisition', 'stock alert', or when working with e-commerce platforms like WooCommerce. It provides capabilities to 1) Retrieve product data from e-commerce systems, 2) Analyze s...

Downloads datasets from Hugging Face and converts them to specific formats (particularly for ML frameworks like Verl). Handles searching, analyzing structure, reading format specs, downloading, transforming schemas, and saving to target formats like Parquet.

When the user requests historical stock price data for specific tickers over a date range, this skill retrieves daily trading data (open, high, low, close, volume) from financial APIs. It handles multiple tickers simultaneously, excludes non-trading days automatically, and can specify whether to use adjusted or unadjusted prices. Triggers include requests for 'stock data', 'historical prices', 'daily trading data', or specific ticker symbols like AAPL, TSLA, etc.

Creates and populates Google Sheets with structured financial or market data. It creates spreadsheets with custom names and worksheets, formats data according to specified templates (like CSV examples), and writes organized datasets. It handles spreadsheet creation, sheet renaming, and bulk data insertion.

When the user needs to collect specific repository information from GitHub for analysis, reporting, or archival purposes. This skill fetches repository data from the GitHub API using repository IDs, extracts key metrics (name, owner, stars, forks, creation time, description, language, URL), handles missing/inaccessible repositories gracefully by skipping them, and saves the formatted data to a JSON file with specified structure. Triggers include GitHub repository data collection, milestone re...

When the user needs to create automated alerting systems with severity-based logging for critical events. This skill writes structured log entries to Google Cloud Logging buckets, formats critical warning messages with relevant identifiers (student IDs, names), uses appropriate severity levels (CRITICAL, WARNING), and integrates with notification systems. Triggers include requests for automated alerting, critical event logging, system notifications, or creating audit trails for important events.

When the user needs to perform end-of-day data settlement processing for gaming or analytics platforms using Google BigQuery, this skill handles the complete workflow. It processes daily score data from streaming tables to 1) Generate ranked leaderboards by aggregating player scores and creating date-stamped tables with player rankings, and 2) Update historical player statistics by inserting daily aggregated metrics into master tracking tables. Key triggers include 'end-of-day settlement', 'd...

Guide to create structured, reproducible Jupyter notebooks for MLOps prototyping, emphasizing configuration management and pipeline integrity.

Execute TruLens evaluations and view results

Statistical visualization. Scatter, box, violin, heatmaps, pair plots, regression, correlation matrices, KDE, faceted plots, for exploratory analysis and publication figures.

Low-level plotting library for full customization. Use when you need fine-grained control over every plot element, creating novel plot types, or integrating with specific scientific workflows. Export to PNG/PDF/SVG for publication. For quick statistical plots use seaborn; for interactive plots use plotly; for publication-ready multi-panel figures with journal styling, use scientific-visualization.

Help analyze datasets and results

This skill should be used when the user wants to visualize data. It intelligently selects the most suitable chart type from 25 available options, extracts parameters based on detailed specifications, and generates a chart image using a Python script.

Use when you need to connect to the SciGraph SCP server for PSPP / PSPP-KG (process-structure-property-performance materials design knowledge graph with influence relations extracted from literature) and call its MCP tools (query_cypher, get_kg_statistics, get_entity_details, get_experiment_workflow), including streamableHttp configuration with SCP-HUB-API-KEY and Python 3.10+ usage examples.

Query Estonian Unemployment Insurance Fund statistics and studies for labor-market, unemployment, and employment-service indicators.

Query Social Insurance Board statistical publications for benefits, social protection services, and caseload trends.

Solve R-based actuarial exam questions (e.g. CS1B, CS2B) using R scripts

Core concepts for property insurance coverage analysis including policy types, coverage limits, and common exclusions