发现 tools data engineering 相关 Skills，比较来源仓库和用途，查看适合你工作流的技能说明。
Work with Domino Datasets - high-performance, versioned filesystem storage. Covers dataset creation, snapshots for versioning, sharing across projects, mounting paths (/domino/datasets/), and performance optimi...
Connect Domino workloads to external data sources including AWS S3 (via Mountpoint CSI driver), credential propagation with AWS IRSA and Azure Entra ID, and External Data Volumes. Use when configuring S3 access...
Work with distributed computing frameworks in Domino including Apache Spark, Ray, and Dask clusters. Covers cluster configuration, on-demand clusters, choosing between frameworks, PySpark usage, and scaling wor...
Use the domino-data Python SDK (dominodatalab-data) for programmatic data access in Domino. Covers DataSourceClient for SQL queries and object storage, DatasetClient for dataset files, TrainingSets for ML data...
Create Domino Launchers - parameterized web forms for self-service job execution. Enable business users to run analyses, generate reports, and trigger batch predictions without coding. Covers parameter types, e...
Orchestrate multi-step ML workflows using Domino Flows (built on Flyte). Define DAGs with typed inputs/outputs, heterogeneous environments, automatic lineage, and reproducibility. Use when building data pipelin...
Processes CSV files and pandas DataFrames. Use when working with CSV files, tabular data, spreadsheets, or when the user asks to query, analyze, or manipulate structured data.
Store ClickHouse Cloud credentials in Doppler + 1Password for gapless-crypto-clickhouse. Use when setting up new services, rotating credentials, or backing up authentication material. Covers API keys, connectio...
Test and validate ClickHouse Cloud connection using clickhouse-connect for gapless-crypto-clickhouse. Use when validating connectivity, troubleshooting connection issues, or verifying environment configuration....
Install, configure, and validate local ClickHouse for gapless-crypto-clickhouse development and backtesting. Use when setting up local development environment, enabling offline mode, improving query performance...
Fetch ClickHouse Cloud service details from API (organization ID, service endpoints, configuration). Use when setting up new ClickHouse Cloud services, discovering endpoints, or validating service metadata for...
Onboard company employees (Claude Code CLI users) to use gapless-crypto-clickhouse package with ClickHouse Cloud credentials. Use when company employee needs to set up ClickHouse Cloud access, mentions first-ti...
Transform and export data using DuckDB SQL. Read CSV/Parquet/JSON/Excel/databases, apply SQL transformations (joins, aggregations, PIVOT/UNPIVOT, sampling), and optionally write results to files. Use when the u...
PostgreSQL database optimization combining institutional knowledge with live analysis
WHEN: SQL query review, query optimization, index usage, N+1 detection, performance analysis WHAT: Query plan analysis + Index recommendations + N+1 detection + Join optimization + Performance tuning WHEN NOT:...
WHEN: Database schema review, table design, normalization, constraints, index planning WHAT: Normalization analysis + Constraint validation + Index strategy + Data types + Relationship design WHEN NOT: Query op...
Design GraphQL schemas, resolvers, and federation. Optimizes queries, solves N+1 problems, and implements subscriptions. Use PROACTIVELY for GraphQL API design or performance issues.
Data analysis expert for SQL queries, BigQuery operations, and data insights. Use proactively for data analysis tasks and queries.
Build ETL pipelines, data warehouses, and streaming architectures. Implements Spark jobs, Airflow DAGs, and Kafka streams. Use PROACTIVELY for data pipeline design or analytics infrastructure.
Work with Resource datasets (mutable state tracking) using OPAL temporal joins. Use when you need to enrich Events/Intervals with contextual state information, track resource state changes over time, or navigat...
Aggregate and summarize event datasets (logs) using OPAL statsby. Use when you need to count, sum, or calculate statistics across log events. Covers make_col for derived columns, statsby for aggregation, group_...
Extract structured fields from unstructured log data using OPAL parsing functions. Covers extract_regex() for pattern matching with type casting, split() for delimited data, parse_json() for JSON logs, and JSON...
Filter and search event datasets (logs) using OPAL. Use when you need to find specific log events by text search, regex patterns, or field values. Covers contains(), tilda operator ~, field comparisons, boolean...
Work with Interval datasets (time-bounded data) using OPAL. Use when analyzing data with start and end timestamps like distributed traces, batch jobs, or CI/CD pipeline runs. Covers duration calculations, tempo...
默认按已收录数据中的 GitHub 仓库 Stars 排序。Stars 属于来源仓库，同一仓库的多个技能可能共享相同数值；它反映仓库关注度，不代表单个技能的安装量或质量评分。