Data & Analytics

Problem-solving strategies for convex optimization in optimization

Problem-solving strategies for constrained optimization in optimization

Calculate portfolio risk metrics including VaR, CVaR, Sharpe, Sortino, and drawdown analysis. Use when measuring portfolio risk, implementing risk limits, or building risk monitoring systems.

Build robust backtesting systems for trading strategies with proper handling of look-ahead bias, survivorship bias, and transaction costs. Use when developing trading algorithms, validating strategies, or building backtesting infrastructure.

Create and manage production Grafana dashboards for real-time visualization of system and application metrics. Use when building monitoring dashboards, visualizing metrics, or creating operational observability interfaces.

Optimize vector index performance for latency, recall, and memory. Use when tuning HNSW parameters, selecting quantization strategies, or scaling vector search infrastructure.

Optimize Apache Spark jobs with partitioning, caching, shuffle optimization, and memory tuning. Use when improving Spark performance, debugging slow jobs, or scaling data processing pipelines.

Master dbt (data build tool) for analytics engineering with model organization, testing, documentation, and incremental strategies. Use when building data transformations, creating data models, or implementing analytics engineering best practices.

Implement data quality validation with Great Expectations, dbt tests, and data contracts. Use when building data quality pipelines, implementing validation rules, or establishing data contracts.

Transform data into compelling narratives using visualization, context, and persuasive structure. Use when presenting analytics to stakeholders, creating data reports, or building executive presentations.

Comprehensive spreadsheet creation, editing, and analysis with support for formulas, formatting, data analysis, and visualization. When Claude needs to work with spreadsheets (.xlsx, .xlsm, .csv, .tsv, etc) for: (1) Creating new spreadsheets with formulas and formatting, (2) Reading or analyzing data, (3) Modify existing spreadsheets while preserving formulas, (4) Data analysis and visualization in spreadsheets, or (5) Recalculating formulas

Expert data analysis and manipulation for customer support operations using pandas

Complete guide for Apache Spark data processing including RDDs, DataFrames, Spark SQL, streaming, MLlib, and production deployment

Use when building Apache Spark applications, distributed data processing pipelines, or optimizing big data workloads. Invoke for DataFrame API, Spark SQL, RDD operations, performance tuning, streaming analytics.

使用Playwright浏览器爬取X(Twitter)真实数据，分析统计信息，生成精美的HTML报告面板并导出为高清图片。

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.

读取、写入、转换和分析CSV文件，支持过滤、排序、聚合和格式转换。

从网页提取和处理数据，使用CSS选择器、XPath智能解析，支持限速和错误处理。

Expert sales operations and analytics guidance for revenue teams. Use when designing CRM workflows, building sales dashboards, optimizing pipeline analytics, creating lead routing rules, designing territories, calculating commissions, managing data quality, building forecasting models, or integrating sales tech stack. Covers Salesforce, HubSpot, Outreach, Gong, and RevOps best practices.

Expert product analytics strategist for SaaS and digital products. Use when designing product metrics frameworks, funnel analysis, cohort retention, feature adoption tracking, A/B testing, experimentation design, data instrumentation, or product dashboards. Covers AARRR, HEART, behavioral analytics, and impact measurement.

Expert customer health scoring and analytics guidance. Use when designing health scores, building churn prediction models, analyzing usage metrics, identifying at-risk accounts, creating executive dashboards, or performing cohort analysis. Use for leading indicator development, customer data enrichment, risk escalation frameworks, and retention analytics.

SAP HANA Machine Learning Python Client (hana-ml) development skill. Use when: Building ML solutions with SAP HANA's in-database machine learning using Python hana-ml library for PAL/APL algorithms, DataFrame operations, AutoML, model persistence, and visualization. Keywords: hana-ml, SAP HANA, machine learning, PAL, APL, predictive analytics, HANA DataFrame, ConnectionContext, classification, regression, clustering, time series, ARIMA, gradient boosting, AutoML, SHAP, model storage

Develops data processing pipelines, integrations, and machine learning scenarios in SAP Data Intelligence Cloud. Use when building graphs/pipelines with operators, integrating ABAP/S4HANA systems, creating replication flows, developing ML scenarios with JupyterLab, or using Data Transformation Language functions. Covers Gen1/Gen2 operators, subengines (Python, Node.js, C++), structured data operators, and repository objects.

Comprehensive plugin for SAP Datasphere development with 3 specialized agents, 5 slash commands, and validation hooks. Use when building data warehouses on SAP BTP, creating analytic models, configuring data flows and replication flows, setting up connections to SAP and third-party systems, managing spaces and users, implementing data access controls, using the datasphere CLI, creating data products for the marketplace, or monitoring data integration tasks. Covers Data Builder (graphical/SQL ...