Total 55,005 skills, Data Processing has 2817 skills
Showing 12 of 2817 skills
Use when "scientific computing", "astronomy", "astropy", "bioinformatics", "biopython", "symbolic math", "sympy", "statistics", "statsmodels", "scientific Python"
Patterns and best practices for using Lakebase Autoscaling (next-gen managed PostgreSQL) with autoscaling, branching, scale-to-zero, and instant restore.
MUST USE when installing chv, setting up local ClickHouse development, or running ClickHouse locally. Contains 5 guides covering chv CLI installation, local project initialization, running a local server, executing SQL from files, and migrating to cloud. Always read relevant guide files and cite them in responses.
Guidance for data resharding tasks that involve reorganizing files across directory structures with constraints on file sizes and directory contents. This skill applies when redistributing datasets, splitting large files, or reorganizing data into shards while maintaining constraints like maximum files per directory or maximum file sizes. Use when tasks involve resharding, data partitioning, or directory-constrained file reorganization.
Use when designing databases for data-heavy applications, making schema decisions for performance, choosing between normalization and denormalization, selecting storage/indexing strategies, planning for scale, or evaluating OLTP vs OLAP trade-offs. Also use when encountering N+1 queries, ORM issues, or concurrency problems.
Fast in-process analytical database for SQL queries on DataFrames, CSV, Parquet, JSON files, and more. Use when user wants to perform SQL analytics on data files or Python DataFrames (pandas, Polars), run complex aggregations, joins, or window functions, or query external data sources without loading into memory. Best for analytical workloads, OLAP queries, and data exploration.
Synthesize multiple media analyses into cross-source patterns and insights. Use when you need to cross-reference analyses, find patterns across sources, or perform meta-analysis of media content.
Native Arrow filesystem integration with PyArrow. Optimized for Parquet workflows, zero-copy data transfer, predicate pushdown, and column pruning. Covers S3, GCS, HDFS with PyArrow datasets.
Expert data engineering covering data pipelines, ETL/ELT, data warehousing, streaming, and data quality.
Master data engineering, ETL/ELT, data warehousing, SQL optimization, and analytics. Use when building data pipelines, designing data systems, or working with large datasets.
Comprehensive data quality patterns using Great Expectations, DLT expectations, and custom validators for ensuring data reliability and trust.
Quantifies market breadth health using TraderMonty's public CSV data. Generates a 0-100 composite score across 6 components (100 = healthy). No API key required. Use when user asks about market breadth, participation rate, advance-decline health, whether the rally is broad-based, or general market health assessment.