ใผใญใใฆใณใฟใคใ ๆฆ็ฅใใใผใฟๅคๆใใญใผใซใใใฏๆ้ ใไฝฟ็จใใฆใORM้ใใใณใใฉใใใใฉใผใ ้ใงใใผใฟใใผใน็งป่กใๅฎ่กใใพใใใใผใฟใใผในใฎ็งป่กใในใญใผใใฎๅคๆดใใใผใฟๅคๆใฎๅฎ่กใใพใใฏใผใญใใฆใณใฟใคใ ใใใญใคใกใณใๆฆ็ฅใฎๅฎ่ฃ ๆใซไฝฟ็จใใพใใ
Generate data migration scripts for Frappe. Use when migrating data from legacy systems, transforming data structures, or importing large datasets.
Databricks development guidance including Python SDK, Databricks Connect, CLI, and REST API. Use when working with databricks-sdk, databricks-connect, or Databricks APIs.
This describes how to create Parquet files in C#, including updating and multi threaded creation
Writes connector_config for segment/journey activations using `tdx connection schema <type>` to discover available fields. Use when configuring activations - always run schema command first to see connector-specific fields.
Reconciles data sources using stable identifiers (Pay Number, driving licence, driver card, and driver qualification card numbers), producing exception reports and โno silent failureโ checks. Use when you need weekly matching with explicit reasons for non-joins and mismatches.
Advanced td_interval patterns including offset dates (-1d/2025-10-01, -7d/-1d, 0M/now), td_interval_range for debugging, td_time_string for display formatting, and partition pruning optimization.
Generates data cleaning pipelines for pandas/polars with handling for missing values, duplicates, outliers, type conversions, and data validation. Use when user asks to "clean data", "generate data pipeline", "handle missing values", or "remove duplicates from dataset".
Orchestrates complete single-document workflows with automatic validation and indexing in a writeโvalidateโindex pipeline
Coordinate FABER-DB operations within FABER workflow phases
Guide for working with IndexedDB database operations in the DEVS platform. Use this when asked to add database tables, modify schemas, or work with data persistence.
Orchestrates multi-log workflows with parallel execution for batch operations across many logs
ใใผใฟๅคๆใใคใใฉใคใณใฎ่จญ่จใปๅฎ่ฃ ใปๆค่จผใๆด็ใใในใญใซใ ในใญใผใใใใใณใฐใETL่จญ่จใๅ่ณช็ขบ่ชใพใงใฎๅฎๅใใญใผใๆไพใใใ Anchors: โข Designing Data-Intensive Applications / ้ฉ็จ: ใใผใฟใขใใชใณใฐ / ็ฎ็: ๅคๆใฎๆดๅๆง็ขบไฟ โข Designing Data-Intensive Applications / ้ฉ็จ: ในใญใผใ่จญ่จ / ็ฎ็: ใใใใณใฐใฎๆ็ขบๅ โข Designing Data-Intensive Applications / ้ฉ็จ: ใใคใใฉใคใณ่จญ่จ / ็ฎ็: ไผธ็ธฎๆงใจ็ฃ่ฆๆงใฎ็ขบไฟ Trigger: Use when designing data transformation pipelines, defining schema mappings, implementing ETL processes, or optimizing data flows. data transformation, schema mapping, etl design, pipeline optimization, data modeling
ใใผใฟใใผในใฎๅๆใใผใฟๆๅ ฅใๅฎๅ จใซ่จญ่จใปๅฎ่ฃ ใปๆค่จผใใในใญใซใ ้็บ/ใในใ/ๆฌ็ชใฎใใผใฟๅ้ขใใทใผใๆฆ็ฅใๅ็พๆงใฎ็ขบไฟใๆด็ใใใ Anchors: โข Designing Data-Intensive Applications / ้ฉ็จ: ใใผใฟๆดๅๆง / ็ฎ็: ๅ็ งๆดๅๆงใฎๆ ไฟ โข Database Reliability Engineering / ้ฉ็จ: ้็จ่จญ่จ / ็ฎ็: ๆฌ็ชๆๅ ฅใฎๅฎๅ จๆง โข Data Quality Principles / ้ฉ็จ: ใใผใฟๅ่ณช / ็ฎ็: ๅ็พๆงใจๆค่จผๆงใฎ็ขบไฟ Trigger: Use when planning database seeding, generating test fixtures, separating datasets by environment, or validating seed data quality. database seeding, test data, fixtures, seed strategy, environment separation, data validation
Use when you need to archive historical database records to reduce primary database size. This skill automates moving old data to archive tables or cold storage (S3, Azure Blob, GCS). Trigger with phrases like "archive old database records", "implement data retention policy", "move historical data to cold storage", or "reduce database size with archival".
Advanced simulation patterns with policyengine.py - ensure(), output_dataset.data, and map_to_entity()
Use when you need to track database changes for compliance and security monitoring. This skill implements audit logging using triggers, application-level logging, CDC, or native logs. Trigger with phrases like "implement database audit logging", "add audit trails", "track database changes", or "monitor database activity for compliance".
When creating or removing a new feature flag from the database
Stream creation, transformation, sinks, batching, and resilience. Use when building data pipelines with concurrency and backpressure.
Value-based data structures (Data.struct, tuple, array) and high-performance collections (Chunk, HashSet). Use for safe comparisons and pipelines.
Maintains memory cleanliness with deduplication, validation, and expiration
