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Found 31 Skills
A high-level interactive graphing library for Python. Ideal for web-based visualizations, 3D plots, and complex interactive dashboards. Built on plotly.js, it allows users to zoom, pan, and hover over data points in a browser-based environment. Use for interactive charts, web applications, Jupyter notebooks, 3D data visualization, geographic maps, financial charts, animations, time-series analysis, and building production-ready dashboards with Dash.
Daily compression of time-series data with merge logic for multiple pipeline runs, structured aggregation for dashboards, and storage estimation for capacity planning.
Prometheus/Grafana metrics analysis and PromQL queries. Use when investigating latency, error rates, resource usage, or any time-series metrics.
Use when the user needs to inspect Google Cloud (GCP) logs, metrics, and monitoring signals via gcloud for incident triage, debugging, or operational analysis. Supports Cloud Logging queries, Cloud Monitoring time-series reads, and environment checks for a target project.
Comprehensive guide for interacting with the hydric Liquidity Pools Indexer (Envio/HyperIndex). Use this skill when you need to (1) Query real-time Liquidity Pool data like TVL, Volume, Fees, or Yields/APY, (2) Fetch cross-chain token metadata and prices, (3) Aggregate protocol data (Uniswap, etc.), (4) Retrieve historical time-series data for generic analytics
Query and analyze data in Azure Data Explorer (Kusto/ADX) using KQL for log analytics, telemetry, and time series analysis. USE FOR: KQL queries, Kusto database queries, Azure Data Explorer, ADX clusters, log analytics, time series data, IoT telemetry, anomaly detection DO NOT USE FOR: SQL databases (use azure-postgres), NoSQL queries (use azure-storage), Elasticsearch, AWS analytics tools
Statistical models library for Python. Use when you need specific model classes (OLS, GLM, mixed models, ARIMA) with detailed diagnostics, residuals, and inference. Best for econometrics, time series, rigorous inference with coefficient tables. For guided statistical test selection with APA reporting use statistical-analysis.
Query FRED (Federal Reserve Economic Data) API for 800,000+ economic time series from 100+ sources. Access GDP, unemployment, inflation, interest rates, exchange rates, housing, and regional data. Use for macroeconomic analysis, financial research, policy studies, economic forecasting, and academic research requiring U.S. and international economic indicators.
Stocks and forex price data, time series, quotes, and reference data
Use when working with pandas DataFrames, data cleaning, aggregation, merging, or time series analysis. Invoke for data manipulation, missing value handling, groupby operations, or performance optimization.
Build financial models, backtest trading strategies, and analyze market data. Implements risk metrics, portfolio optimization, and statistical arbitrage. Use PROACTIVELY for quantitative finance, trading algorithms, or risk analysis.
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.