Total 53,177 skills, Data Processing has 2676 skills
Showing 12 of 2676 skills
Correlation Analyzer - Auto-activating skill for Data Analytics. Triggers on: correlation analyzer, correlation analyzer Part of the Data Analytics skill category.
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Performance rules for query shape, aggregation strategy, and payload minimization.
Use to analyze loyalty member behavior, segmentation, and experiment results.
通过Eureka专利数据平台获取翻译后的专利说明书（描述）文本。当用户要求专利说明书翻译、其他语言的专利全文、翻译后的专利全文，或想查看中文、英文、日文版的专利说明书、Eureka专利说明书、patent specification translation, patent description translation, Eureka patent, patent translation时触发此技能。当用户提供专利ID或公开号并要求获取其他语言的说明书/描述内容，或提到"专利说明书翻译"、"描述翻译"、"翻译全文"等类似意图时也应触发。
MUST USE when designing ClickHouse architectures, selecting between ingestion or modeling patterns, or translating best practices into workload-specific system designs. Complements clickhouse-best-practices with decision frameworks and explicit provenance labels.
生成 akquant 框架的可执行量化策略代码，涵盖数据接口、事件驱动、风控与优化；当用户需要开发 akquant量化策略 时使用
Runs SQL analytics on SageMaker Catalog asset metadata tables exported as Apache Iceberg in S3 Tables. Covers governance queries, asset growth tracking, ownership audits, time-travel over catalog state, and metadata quality analysis. Applies when querying catalog inventory, finding assets without descriptions, comparing catalog snapshots, or auditing data ownership. Trigger phrases: catalog inventory SQL, how many assets, assets without descriptions, asset growth over time, who owns this data, catalog governance, data quality audit, catalog analytics.
In-process ClickHouse SQL engine for Python — run ClickHouse SQL queries directly on local files, remote databases, and cloud storage without a server. Use when the user wants to write SQL queries against Parquet/CSV/ JSON files, use ClickHouse table functions (mysql(), s3(), postgresql(), iceberg(), deltaLake() etc.), build stateful analytical pipelines with Session, use parametrized queries, window functions, or other advanced ClickHouse SQL features. Also use when the user explicitly mentions chdb.query(), ClickHouse SQL syntax, or wants cross-source SQL joins. Do NOT use for pandas-style DataFrame operations — use chdb-datastore instead.
Execute read-only SQL queries against multiple MySQL databases. Use when: (1) querying MySQL databases, (2) exploring database schemas/tables, (3) running SELECT queries for data analysis, (4) checking database contents. Supports multiple database connections with descriptions for intelligent auto-selection. Blocks all write operations (INSERT, UPDATE, DELETE, DROP, etc.) for safety.
Work with raster and imagery data including ImageryLayer, ImageryTileLayer, multidimensional data, pixel filtering, and raster analysis. Use for satellite imagery, elevation data, and scientific raster datasets.
Drop-in pandas replacement with ClickHouse performance. Use `import chdb.datastore as pd` (or `from datastore import DataStore`) and write standard pandas code — same API, 10-100x faster on large datasets. Supports 16+ data sources (MySQL, PostgreSQL, S3, MongoDB, ClickHouse, Iceberg, Delta Lake, etc.) and 10+ file formats (Parquet, CSV, JSON, Arrow, ORC, etc.) with cross-source joins. Use this skill when the user wants to analyze data with pandas-style syntax, speed up slow pandas code, query remote databases or cloud storage as DataFrames, or join data across different sources — even if they don't explicitly mention chdb or DataStore. Do NOT use for raw SQL queries, ClickHouse server administration, or non-Python languages.