Data & Analytics

start = '2001-01-01' end = '2001-10-31' version = 2 rf_list_slice = da_glofas_reforecast_interp[version].sel(time=slice(start,end)) ra_slice = da_glofas_reanalysis[version].sel(time=slice(start, end)) rf_list_slice.mean(axis=1).plot.line( x='time', add_legend=True) ra_slice.plot.line(label='Historical', c='k') plt.show() ``` We'll compute forecast skill using the ```xskillscore``` library and focus on the CRPS (continuous ranked probability score) value, which is similar to the mean absolute ...

This notebook explores the Ranked Probability Skill Score (RPSS) for IRI's seasonal forecast. In the end results were not really used. Interactive tool several skill measurements: https://iri.columbia.edu/our-expertise/climate/forecasts/verification/ Data description RPSS: http://iridl.ldeo.columbia.edu/maproom/Global/Forecasts/skill_precip_seasonal.html Data files RPSS: http://iridl.ldeo.columbia.edu/home/.jingyuan/.NMME_seasonal_hindcast_verification/.monthly_RPSS_seasonal_hindcast_precip_E...

Track and analyze US government shutdown liquidity impacts by monitoring TGA (Treasury General Account), bank reserves, EFFR, and SOFR data from FRED API. Use when user wants to (1) analyze current or past government shutdown effects on financial markets, (2) track liquidity conditions during fiscal policy disruptions, (3) assess "stealth tightening" effects, (4) compare shutdown episodes across different monetary policy regimes (QE vs QT), or (5) generate liquidity stress reports with histor...

Instructions for fetching current weather temperature data for Karachi, Pakistan from wttr.in API

追踪 GitHub 话题下的热门项目趋势，AI 智能分析，每日趋势报告。

Analyzes stock data and generates technical signals using Yahoo Finance. Use when the user asks for analysis of a specific stock ticker using this specific tool, or asks for "yahoo stock analysis" or mentions "get_info" or "signal" scripts. Provides RSI, MACD, MA, and simple trend signals.

Fetches AI news from smol.ai RSS and generates structured markdown with intelligent summarization and categorization. Optionally creates beautiful HTML webpages with Apple-style themes and shareable card images. Use when user asks about AI news, daily tech updates, or wants news organized by date or category.

Design ETL/ELT pipelines with proper orchestration, error handling, and monitoring. Use when building data pipelines, designing data workflows, or implementing data transformations.

Implement data quality checks, validation rules, and monitoring. Use when ensuring data quality, validating data pipelines, or implementing data governance.

Generate comprehensive SDLC reports including iteration status, metrics dashboards, and executive summaries.

Facilitate data-driven technical decisions using embedded decision matrices and trade-off analysis.

Generate executive-ready performance summaries with insights and recommendations.

Orchestrate marketing data collection, transformation, and reporting workflows.

Synthesize audience insights from multiple data sources into unified personas and segments.

Reverse-engineer voice profiles from sample content by analyzing writing patterns

Copilot agent that assists with machine learning model development, training, evaluation, deployment, and MLOps Trigger terms: machine learning, ML, AI, model training, MLOps, model deployment, feature engineering, model evaluation, neural network, deep learning Use when: User requests involve ai ml engineer tasks.

Parse and analyze CSV files with data validation

Systematic 7-step methodology for comprehensive patent prior art searches and patentability assessments using BigQuery and CPC classification

Search 100M+ patents via the MCP server's BigQuery tools. No standalone scripts; everything goes through the MCP tools registered by the patent-creator server.

Comprehensive data analysis skill for CSV files using Python and pandas

Comprehensive Excel spreadsheet creation, editing, and analysis with support for formulas, formatting, charts, data analysis, and visualization. Use when working with .xlsx, .xlsm, .csv files for: (1) Creating spreadsheets with formulas and formatting, (2) Reading/analyzing data, (3) Modifying existing spreadsheets while preserving formulas, (4) Creating charts and visualizations, (5) Data transformation and analysis, (6) Multi-worksheet operations

Analyze datasets to discover patterns, anomalies, and relationships. Use when exploring data files, generating statistical summaries, checking data quality, or creating visualizations. Supports CSV, Excel, JSON, Parquet, and more.

Chunked N-D arrays for cloud storage. Compressed arrays, parallel I/O, S3/GCS integration, NumPy/Dask/Xarray compatible, for large-scale scientific computing pipelines.

Graph Neural Networks (PyG). Node/graph classification, link prediction, GCN, GAT, GraphSAGE, heterogeneous graphs, molecular property prediction, for geometric deep learning.