Total 50,529 skills, Data Processing has 2561 skills
Showing 12 of 2561 skills
Guides actuarial work for insurance and reinsurance—pricing and rate adequacy, reserving and IBNR, loss development and triangles, mortality/morbidity and lapse assumptions, experience studies and credibility, capital and risk metrics at overview level, product design tradeoffs (life, health, P&C, annuity), and regulatory reporting concepts (NAIC, IFRS 17, Solvency II overview—not legal advice). Use when the user mentions actuary, actuarial, IBNR, loss development, reserve analysis, mortality table, pricing insurance, experience study, IFRS 17, loss ratio, combined ratio, credibility, or asks for assumption documentation and model governance for insurance products—not generic FP&A (financial-analyst), investment banking valuation (comps-analysis, dcf-model), legal policy interpretation (commercial-counsel), clinical trials, software-only implementation (senior-software-engineer), or broad GRC without actuarial models (compliance-engineer).
Expert knowledge for Azure Data Explorer development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when configuring ADX clusters, private endpoints, follower DBs, streaming ingestion, or Power BI integration, and other Azure Data Explorer related development tasks. Not for Azure Synapse Analytics (use azure-synapse-analytics), Azure Stream Analytics (use azure-stream-analytics), Azure HDInsight (use azure-hdinsight), Azure Databricks (use azure-databricks).
Convert any data file to another format: CSV, Parquet, JSON, Excel, GeoJSON, and more. Use when the user says "convert to parquet", "save as xlsx", "export as JSON", "make this a CSV", "turn into parquet", or any variation of format-to-format conversion for data files. Also triggers when the user wants to write Parquet, Excel, or other binary formats that Claude cannot produce natively.
Market regime identification using volatility clustering, trend detection, and statistical methods for adaptive trading
万行以上 Excel 数据集的高性能分析引擎。提供 openpyxl read_only 流式读取（iter_rows 支持 10 万行以上）、Parquet 转换加速、内存优化、分块处理和大文件写入模式。**遇到以下任一情况就主动使用本 skill**：①数据行数 ≥ 10k（由 sn-da-excel-workflow 的行数评估步骤触发）；②用户出现触发词：大文件 / 大数据量 / 性能优化 / 内存不足 / OOM / 百万行 / 十万行 / 流式读取 / Parquet / 分块处理 / large file / big data / streaming read / chunked processing；③直接使用 pd.read_excel() 导致超时或内存溢出；④用户明确要求对大规模数据集进行高性能处理。仅不用于：小于 10k 行的常规 Excel 分析（使用 sn-da-excel-workflow 即可）。
Query Catalog, database, and table metadata resources in Alibaba Cloud Data Lake Formation (DLF). Provides read-only queries via the DLF OpenAPI Python SDK, supporting listing and viewing Catalogs, databases, tables with their detailed information and Schema definitions. Use cases: "list available Catalogs", "list databases", "view table schema", "search tables", "search tables by name", "fuzzy search", "view DLF metadata", "what databases are in the data lake", "what columns does a table have", "find tables whose name contains xxx". This Skill only contains read-only operations — no create, modify, or delete operations.
MPSTATS marketplace analytics API. Use when working with MPSTATS API, Wildberries analytics, Ozon analytics, Yandex Market analytics, marketplace data, product research, sales analytics, competitor analysis, niche research, SKU analysis, seller analytics, brand analytics.
End-to-end data engineering pipeline using MinIO, Airbyte, PostgreSQL, DBT, and Airflow with medallion architecture (Bronze/Silver/Gold layers)
Build end-to-end real-time data pipelines with Kafka, PostgreSQL, Airflow, and Streamlit using Medallion Architecture for streaming analytics.
Comprehensive study guide covering data engineering concepts, tools, and best practices for learning and reference
Roblox Murder Mystery 2 analytics dashboard and inventory tracking toolkit with data visualization and strategy analysis
Update financial models with new data — quarterly earnings, management guidance, macro changes, or revised assumptions. Adjusts estimates, recalculates valuation, and flags material changes. Use after earnings, guidance updates, or when assumptions need refreshing. Triggers on "update model", "plug earnings", "refresh estimates", "update numbers for [company]", "new guidance", or "revise estimates".